Development of an On-line Planning and Delivery Technique. for Radiotherapy of Spinal Metastases

Transcription

1 Development of an On-line Planning and Delivery Technique for Radiotherapy of Spinal Metastases by DANIEL LÉTOURNEAU A thesis submitted in conformity with the requirements of the degree of Doctor of Philosophy Graduate Department of Institute of Medical Science University of Toronto Copyright by Daniel Létourneau, 2008

2 Development of an On-line Planning and Delivery Technique for Radiotherapy of Spinal Metastases Doctor of Philosophy, 2008 DANIEL LÉTOURNEAU Graduate Department of Institute of Medical Science University of Toronto The objective of this work is to develop an on-line planning and delivery technique for palliative radiotherapy of spinal metastases using a linear accelerator capable of cone-beam CT (CBCT) imaging. This technique integrates all preparation and delivery steps into a single session equivalent to an initial treatment session. The key technical challenges pertaining to the development and implementation of this novel treatment technique are related to CBCT image performance, efficient system integration, development of on-line planning tools and design of novel quality assurance (QA) phantoms and processes. Hardware and software image corrections were first implemented to make CBCT images suitable for target definition and planning. These corrections reduced CBCT non-uniformity and improved CBCT-number accuracy. The on-line treatment technique workflow and the integration of all the subsystems involved in the process were assessed on a customized spine phantom constructed for the study. ii

3 The challenges related to the routine QA of the highly integrated on-line treatment technique were addressed with the construction and validation of an integral test phantom. This phantom, which contains point detectors (diodes) allows for real-time QA of the entire image guidance, planning and treatment process in terms of dose delivery accuracy. The integral test phantom was also effective for the QA of high-dose, high-precision spinal radiosurgery. Simulation of the on-line treatment technique on patient data showed that the planning step was the one of the most time consuming tasks due predominantly to manual target definition. A semi-automatic method for detection and identification of vertebrae on CBCT images was developed and validated to streamline vertebra segmentation and improve the on-line treatment efficiency. With a single patient setup at the treatment unit, patient motion during the on-line process represents the main source of geometric uncertainty for dose delivery. Spine intrafraction motion was assessed on CBCT for a group of 49 patients treated with a palliative intent. The use of surface marker tracking as a surrogate for spine motion was also evaluated. Finally, the complete on-line planning and delivery technique was implemented in a research ethics board (REB) approved clinical study at the Princess Margaret Hospital and 7 patients have been successfully treated at the time of this report with this novel treatment approach. iii

4 Préface J ai commencé ma carrière de physicien médical en novembre 1996 à l Hôpital de Chicoutimi après avoir complété une maîtrise en physique médicale à l Hôtel-Dieu de Québec. J étais loin de me douter à cette époque que j allais un jour retourner à l université pour faire un doctorat. Mes années à Chicoutimi furent bien remplies et j y acquis une grande part de mon expérience clinique. C est par une série d heureux hasards que je me suis retrouvé à l hôpital William Beaumont dans la banlieue de Détroit en juillet Si mes années à Chicoutimi représentent pour moi mon apprentissage clinique, Beaumont fut mon ouverture sur la recherche et aussi, d une certaine façon, mon ouverture sur le monde. À Chicoutimi, je suis devenu un physicien solide (au moins, c est ce que j aime croire), à Beaumont, j ai eu l occasion de le montrer un peu à la communauté internationale de radiothérapie. Je suis arrivé à Toronto il y a maintenant plus de trois ans. J ai quitté le Michigan en grande partie parce que j avais rencontré Toni qui est maintenant ma fiancée. Je crois aussi qu à cette époque, j avais besoin de changement dans ma vie professionnelle. Toronto, la métropole canadienne, n avait jamais fait parti de mes projets ou de mes rêves. L opportunité et les conditions qui me furent offertes à l hôpital Princess Margaret étaient assez difficiles à refuser. J avais la chance de faire un doctorat en physique médicale tout en conservant une petite tâche clinique dans le département de radio-oncologie. J ai donc accepté l offre de David Jaffray et je suis devenu étudiant pour la deuxième fois de ma vie le 1 janvier Ce n est pas un mince tâche de retourner à l école. L égo aussi en prend un coup. Je ne regret pas mon choix, mais j ai bien hâte de compléter mon doctorat. Le contenu de cette thèse représente le fruit de mes efforts des trois dernières années. Ouf! C est tout ce que je vais ajouter à ce sujet. Je suis bien fier de ce travail et je peux affirmer que j ai beaucoup appris durant les trois dernières années passées à Toronto. Je veux remercier mon directeur de doctorat, David Jaffray ainsi que mes collègues physiciens du département de radio-oncologie de Princess Margaret. Finalement, je tiens à remercier encore une fois ma compagne Toni Barnes, mes parents et mes sœurs pour leur soutien constant. iv

5 À ma fiancée, Toni Barnes, à mes parents, Monique et André Létourneau, et à mes sœurs, Hélène et Isabel, qui m ont appuyé au cours de cette longue aventure. v

6 Table of Contents Préface... iv Table of Contents... vi List of Figures...x List of Tables... xvii Chapter 1 GENERAL INTRODUCTION Radiotherapy Brief history Modern Radiotherapy Palliative Radiotherapy and Bone Metastases General Conventional Workflow for radiotherapy of bone metastases Radiotherapy treatment planning: Off-line and on-line Objective, hypothesis and rationale Outline of the Thesis References...22 Chapter 2 ON-LINE PLANNING AND DELIVERY TECHNIQUE FOR RADIOTHERAPY OF SPINAL METASTASES USING CONE-BEAM CT: IMAGE QUALITY AND SYSTEM PERFORMANCE Abstract Introduction Methods and Materials System Description Image Processing and Impact on Dose Calculation Process Timing, and Geometric and Dosimetric Performance Results Image Processing and Impact on Dose Calculation Process Timing, and Geometric and Dosimetric Performance Discussion Conclusion...53 vi

7 2.7 Acknowledgement References...55 Chapter 3 INTEGRAL TEST PHANTOM FOR DOSIMETRIC QUALITY ASSURANCE OF IMAGE GUIDED AND INTENSITY MODULATED STEREOTACTIC RADIOTHERAPY Abstract Introduction Methods and Materials System Description System Characterization Integral Testing and Sensitivity Analysis in the Clinical Setting Results System Characterization Integral Testing and Sensitivity Analysis in the Clinical Setting Discussion Conclusion Acknowledgment References...85 Chapter 4 SEMI-AUTOMATIC VERTEBRA VISUALIZATION, DETECTION AND IDENTIFICATION FOR ON-LINE PALLIATIVE RADIOTHERAPY OF BONE METASTASES OF THE SPINE Abstract Introduction Methods and Materials Image datasets Algorithm description Algorithm validation Results Gross Anatomical Region and Reference Landmark Detection Vertebra Detection and Identification Elapsed Time Discussion Conclusion Acknowledgement vii

8 4.8 References Chapter 5 SPINE INTRA-FRACTION MOTION Introduction Methods and Material System Description Spine Intra-Fraction Motion and Surface Marker Validation Framework for Statistical Analysis of Surrogate Results Spine Intra-Fraction Motion and Surface Marker Validation Statistical Analysis of Surface Marker as a Surrogate for Spine Discussion Conclusion References Chapter 6 CLINICAL IMPLEMENTATION Introduction Methods and Material Treatment technique Description Elapsed Time, Plan Evaluation and In-vivo Dosimetry Results Discussion Elapsed time Plan evaluation and improvements Conclusion References Chapter 7 GENERAL DISCUSSION Introduction Cost-benefit discussion Equipment Model for the Routine Use of the On-line Planning and Delivery Technique Patient Perspective Review of specific QA methods viii

9 7.4 Future directions Conclusion References ix

10 List of Figures Figure 1-1. Figure 2-1. Figure 2-2. Figure 2-3. Annual palliative treatment statistics at the Princess Margaret Hospital (PMH) from 2002 to This histogram presents the relative number of palliative treatment courses (all sites included) delivered annually at PMH. The relative number of courses was also compiled for all bone metastasis and for bone metastasis of the spine alone....8 Flow chart for the on-line radiotherapy strategy of spine metastases (a). The steps shown in grey were not performed on phantoms but will be part of the clinical implementation of the on-line treatment strategy. Phantom used to assess the duration and dosimetric accuracy of the on-line treatment strategy. Three radiochromic films were inserted in the spine-like insert made of white Teflon (hard bone) and black Delrin (soft bones) (b). The spine-simulating insert was disassembled in (c) Correction curves as a function of thickness of water equivalent material for scans of the body phantom acquired with the scatter rejection grid in place. These curves were optimized for different longitudinal fields-of-view (FOV z ) of 0.8, 10 and 20 cm in the superior to inferior direction. The field-of-view in the transverse direction (FOV t ) was held constant at 40 cm. The ideal linear relationship (no scatter and no beam hardening) between measured attenuation μx and the thickness of water is also shown...39 CBCT images of a RMI CT calibration phantom acquired (a) uncorrected, (b) with the 10:1 scatter-rejection grid but without non-linear correction, and (c) with grid and non-linear correction. The uncorrected image obtained without grid (a) represents a good example of cupping artifact with the center of the image darker than the edges. The image in (d) was acquired on a state of the art helical CT. The CBCT scans were all acquired with a transverse and SI fields-of-view of 40 and 20 cm, respectively. All images are presented with the same normalized window and level. The ROIs used for the optimization of the non-linear correction are shown in 3c. Horizontal profiles going through the center of the phantom (see dash line on d) are presented for the four images before normalization (e)...42 x

11 Figure 2-4. Figure 2-5. Figure 2-6. Figure 3-1. Figure 3-2. Figure 3-3. Figure 3-4. Average CBCT numbers as a function of the helical CT numbers for the density inserts of the head and the body phantom. The head phantom images were corrected using the curve optimized for the body. The un-corrected results for the body phantom are also shown. The 10:1 grid was used for all scans Joint histogram of CBCT vs. helical CT numbers for a scan of the shoulders and the neck of an anthropomorphic phantom (a) uncorrected, and (b) corrected using the 10:1 grid and the non-linear correction. The grey scale represents the logarithm in base 10 of the number of pixels Comparison of CBCT and helical CT images of sacral (pat #1) and thoracolumbar (pat #2) spine patients. For patient 1, the CBCT image (a) was acquired without the grid and no correction was applied. The grid and the non-linear correction were used for the image shown in (b). The corresponding helical CT image is shown in (c). The CBCT images of patient #2 with and without non-linear correction (d and e, respectively) were both acquired with the grid. The corresponding helical CT image is shown in (f). All CBCT and helical CT images are presented with a normalized window and level. The voxel size for the CBCT and helical CT scans was 1 mm 3 and 0.81 x 0.81 x 2 mm, respectively...48 a): Prototype of IG-IMRT QA phantom. The construction details of the circular diode array are shown in b). c): An individual diode embedded in a 10 cm diameter cylindrical phantom made of acrylic is clearly visible on an axial CBCT image...64 a): Directional response of the central diode of the QA phantom as a function of gantry angle. The error bars on this graph are the same size as the symbols. The directional response of a single diode embedded in a 10 cm diameter cylindrical phantom made of acrylic is also shown. b): A CT slice of the phantom is shown to illustrate the air gaps and the high-density material at the flat cable connection Relative dose differences between measured and planned doses for the 11 diodes in the phantom (20 x 20 cm 2 field). The error bars are equal to ± 0.3% and are estimated from the short-term reproducibility analysis a): Responses of 6 diodes located in the penumbra of a 6 MV beam translated across the phantom (dash lines and symbols). The corresponding planning system calculations are shown in full line. b): Dose measurements performed after xi

12 phantom shifts (0.5, 1.2 and 2.3 mm) are plotted with the measurement obtained before shift. c): Differences between dose-based and camera system displacements were plotted as a function of the dose gradient at the diode locations...76 Figure 3-5. Figure 3-6. Figure 4-1: Figure 4-2: Figure 4-3: a): Phantom sensitivity to systematic MLC calibration errors for a 7-beam IMRT plan designed for a lumbar metastasis. b): The histogram gives the number of measurement points, which agreed with the dose calculation within given tolerances of dose difference and distance to agreement. Positive errors correspond to MLC expansions and negative errors to contractions of the right MLC bank...79 Phantom sensitivity to setup errors in the RL direction for the IMRT plan of Figure 3-5a. This histogram gives the number of measurement points, which agreed with the dose calculation within given tolerances of dose difference and distance to agreement...80 Flow chart describing the semi-automatic vertebra detection and identification algorithm. The tasks shown on grey background represent manual steps. The vertebra finder (VF) is launched via scripting through the treatment planning system (TPS) interface. Steps 3 to 9 (within dashed line) are performed outside TPS. Once the VF results are approved by the user, the coordinates of the vertebra centers are transferred back to the TPS. The plan can then be completed (step 10)...96 Steps 3 and 4 of flowchart (Fig. 4-1). Formation of sagittal and coronal curved digitally reconstructed radiographs (cdrr) from a cone-beam CT dataset (CBCT). The centerline of a cylinder model, which encompasses the vertebral bodies is shown in (a) with a bold dash line. The CBCT data was projected orthogonally to the centerline to produce the sagittal and coronal 2D images (b and c). The projected data was limited to ± 15 mm around the centerline in both anterior-toposterior (coronal cdrr) and left-to-right direction (sagittal cdrr). The position of a second coronal cdrr located 40 mm posterior to the centerline is shown also in (a) as a thin dash line. The 3D dataset (a) was acquired with an imaging dose of about 2 cgy. The 2 spheres and the linear structures visible on the top of the patient abdomen (a) is a reflector tool used for optical tracking of the patient external contour (not part of this study)...99 Step 5 of flowchart (Fig. 4-1). Automatic identification of the thoracic region. A greyscale histogram (a) of the coronal curved digitally reconstructed radiographs xii

13 (cdrr) is produced and the cdrr (b) is normalized to the highest greyscale bin containing at least 5% of the histogram highest peak value (maximum frequency). Pixels within 10-65% of highest greyscale value are identified as lung (the grey shade area on the histogram corresponds to the contoured region on the cdrr). The width of the lung cross-section on the coronal cdrr is estimated by summing the pixels identified as lung along rows (c). The thoracic region is characterized by a minimum lung width of 60 mm Figure 4-4: Figure 4-5: Steps 6 and 7 of flowchart (Fig. 4-1). Reference landmark (T12-ribs) detection on posterior coronal curved digitally reconstructed radiographs (cdrrs) (40 mm posterior to the cylinder centerline). The derivative in the superior to inferior direction of the coronal cdrr was convolved with the derivative of a rib template (a) to obtain a map of normalized cross correlation coefficients (b). The maximum value of this map corresponds to the T12-rib position and the corresponding intervertebral space between T12 and L1 is shown with a dashed line on the cdrr (c) Steps 8 and 9 of flowchart (Fig. 4-1). Vertebra center detection on curved digitally reconstructed radiographs (cdrrs). a) The image gradient in superior to inferior (SI) direction was first computed on the coronal cdrr and a summation window translated in the SI direction produced a cumulated gradient curve. The curve shown in (b) represents the cumulated gradient curve minus the local median value calculated for a window width of 50 mm. The vertebra center candidates (c) correspond to the minima located between the zero crossing points of the cumulated gradient curve. They are identified by name based on their positions with respect to the T12-L1 inter-vertebral space (dashed line) Figure 4-6: Distribution of vertebrae included in the cone-beam CT (CBCT) datasets of 37 patients enrolled in this study. The numbers of false positive and negative vertebra centers detected by the vertebra finder (VF) on sagittal and coronal curved digitally reconstructed radiographs (cdrrs) are shown in (a) and (b), respectively (user 1, inter-observer study). The false positive vertebra centers are plotted between the tick marks of the histogram (a) while the false negatives are shown at the corresponding vertebrae (b) Figure 4-7: Examples of vertebra hole (a) and deformation (b) shown on coronal curved digitally reconstructed radiographs (cdrrs). The erosion of the vertebra (a) did xiii

14 not introduce any detection errors. The deformation of multiple vertebrae (b) produced 3 false negatives, which represent one of the worst results for the VF Figure 4-8: Compilation of the average time required to perform the semi-automatic detection and identification of all imaged vertebrae for 37 patients enrolled in this study (user 1, inter-observer study). The error bars represent 1 standard deviation. The numbers at the extremity of each bar of the histogram correspond to the task number on the flow chart presented in Fig For the Detection/Identification tasks, the grey section of the histogram bar corresponds to the detection and identification process (8 s on average) while the white section represents the time required to display the images and save them for record (17 s on average). The bar sections in white represent time that could be eliminated with a closer integration of the vertebra finder (VF) with treatment planning system (TPS). The total process duration is compiled with and without this extra time Figure 5-1: Schematic of the optical tracking system (OTS) Figure 5-2: Two different types of reflective surface markers used in this study Figure 5-3: Figure 5-4: Figure 5-5: Figure 5-6: Magnitude of 3D displacement of an individual marker measured with the optical tracking system as a function of time for a phase C patient. The bottom time scale presents the main steps of the on-line planning and delivery process Histograms of spine displacements measured on cone-beam CT in right to left (RL), anterior to posterior (AP) and superior to inferior (SI). Gaussian fits based on all spine displacements (dashed line) and non-zero displacements only (full line) are also shown Magnitude of spine displacement in 3D measured on cone-beam CT (CBCT) as a function of the elapsed time between the two CBCT scans. The data was separated between spinal metastases and soft tissue mass patients Marker displacements measured with the optical tracking system (OTS) as a function of the spine displacements measured on cone-beam CT (CBCT) in right to left (RL), anterior to posterior (AP) and superior to inferior (SI). The data was plotted separately for the rigid tool and the individual markers. The results of linear regressions are also shown. The circled data point on b) might be an outlier, has xiv

15 the rigid tool was suspected to have slipped between the acquisition of the two CBCT scans Figure 5-7: Figure 5-8: Figure 5-9: Figure 5-10: Figure 5-11: Figure 5-12: Figure 6-1: Histograms of residual error after hypothetical patient setup correction based on optical tracking system (OTS) measurements in right to left (RL), anterior to posterior (AP) and superior to inferior (SI) Marker displacements measured with the optical tracking system (OTS) as a function of marker displacements measured on cone-beam CT (CBCT) in right to left (RL), anterior to posterior (AP) and superior to inferior (SI). The results of a linear regression as well as the line of identity are also shown Probability of making a bad correction as a function of action level. The full lines were calculated with Equations 5-2 and 5-3 while the symbols were obtained with a Monte Carlo simulation. The total number of corrections performed as a function of the action level is also shown. σ s and σ err represents the standard deviation (SD) on the true spine shifts (s) measured on CBCT and the measurement errors related to the optical tracking system (OTS), respectively The probability of not scanning a patient (P missed_true_shift ) when a setup correction was required (s AL). σ s and σ err represents the standard deviation (SD) on the true spine shifts (s) measured on CBCT and the measurement errors related to the optical tracking system (OTS), respectively The probability of scanning a patient (P extra_scans ) when a setup correction was not required (s AL). σ s and σ err represents the standard deviation (SD) on the true spine shifts (s) measured on CBCT and the measurement errors related to the optical tracking system (OTS), respectively ROC analysis for σ s = 2.0 mm and σ err = 0.7 to 10 mm. σ s and σ err represents the standard deviation (SD) on the true spine shifts (s) measured on CBCT and the measurement errors related to the optical tracking system (OTS), respectively Flowchart of the clinical version of the on-line planning and delivery technique. The steps shown in grey are not yet implemented clinically. The numbers on the time scale at the bottom of the image correspond to the tasks for which the elapsed time was measured (see Table 6-2). Abbreviations: CBCT: Cone-beam CT, Mosaiq: Record and Verify system, RadCalc: Secondary monitor unit calculation xv

16 software, IviewGT: Megavoltage imager, XVI: CBCT imaging system, WebPub: Webpublishing (patient electronic chart) Figure 6-2: Elapsed time measured for the individual steps of the on-line planning and delivery technique as a function of patient number xvi

17 List of Tables Table 2-1. Table 3-1. Table 4-1: Table 4-2: Average duration of the on-line treatment strategy performed on phantom...49 Inclusion criterion and tolerances for comparison between measured (D meas ) and planned (D plan ) doses at a diode location. The dose comparison was done on an individual beam basis. %ΔD tolerance and DTA tolerance represent the relative dosedifference tolerance and the distance to agreement tolerance, respectively Compilation and ranking of 4 different landmarks detected on coronal curved digitally reconstructed radiographs (cdrrs) for the user 1 participating in the inter-observer study. For all landmarks detected properly, the adjustment made by the user to match a corresponding inter-vertebral space was recorded and the average and standard deviation of these adjustments is reported Success rate of vertebra detection. The data is divided by spine level and the success rate is reported for both coronal and sagittal cdrrs (37 patients). For user #1 (U #1), the single numbers represent the mean success rate for three different analyses of the 37 datasets (intra-observer analysis). The numbers in parenthesis are the minimum and maximum success rates obtained by user #1 for the intraobserver analysis. Abbreviation: U = user Table 5-1: Integration limits for Equation 5-2 and Table 5-2: Table 5-3: Table 5-4: Mean spine intra-fraction motion measured on CBCT for patients treated for soft tissue masses or spinal metastasis. The spine displacements are compiled separately for right to left (RL), anterior to posterior (AP) and superior to inferior (SI) direction. The average time spent by the patients on the treatment couch is also provided Parameters of Equation 5.6. The first Gaussian fit was performed using all measured spine shifts while the second Gaussian fit was based only on the nonzero measured spine shifts Mean residual errors after hypothetical couch shift corrections based on the OTS measurements (spine shift on CBCT minus surface marker shift). The mean xvii

18 residual errors were compiled separately for the rigid tool and the individual markers Table 6-1: Example of report generated by the Assisted QA software. Each line represents an item of the on-line plan compared to a given reference plan. The item value in the on-line plan is given at the end of the line followed by P or DIFFERENT if the online and reference plan agree or disagree, respectively Table 6-2: Average duration of the on-line planning and delivery technique performed for 7 patients. The tasks presented in the table above can be related to the flowchart of Figure 6-1 using the numbers on the time scale (Figure 6-1). The time required for the patient to leave the treatment room (step 7) was only measured for the last 4 patients enrolled in the study. Abbreviations: Therapist (MRT(T)), Project physicist (P_Phys), QA Physicist (QA_Phys) and Radiation-oncologist (RO) Table 6-3: Table 6-4: Table 6-5: Table 6-6: PTV Dose coverage for plans generated with the on-line planning and delivery technique. The maximum plan dose is also reported Comparison of monitor units (MUs) calculated by Pinnacle (P 3 ) and Radcalc (Rc), a verification MU software. For the posterior (PA) beam, Pinnacle measured the physical depth of the prescription point from the posterior aspect of the treatment couch. The depth used by Radcalc for the PA beam was measured by the planner (project physicist) and corresponded to the distance from the prescription point to the skin surface of the patient s back. The MUs for the PA beam were recomputed in Pinnacle after subtracting the treatment couch from the CBCT images. For the anterior (AP) beam, Radcalc used the depth measured by Pinnacle (not shown) In-vivo dosimetry measurements compared to dose calculated with Pinnacle at the same point. No measurement was recorded for patient #5, as the MOSFETs were setup outside of the treatment field Comparison of Pinnacle dose calculation performed on a CBCT and a helical CT scan for the same patient. The helical CT scan was acquired a few days after the on-line planning and delivery session. The dose comparison is performed at the treatment prescription (Px) point. Depths of the prescription point for both beams are given xviii

19 Table 6-7: Table 7-1: Table 7-2: Table 7-3: Potential technical improvements for the on-line planning and delivery technique along with the corresponding predicted reduction in time Estimation of equipment cost per minute of use from Poon et al 1. The costs were calculated in 1999 Canadian dollars and include construction, equipment acquisition and maintenance costs. This data was obtained from a study performed at Princess Margaret Hospital Estimation of the cost for the simulation, planning and treatment of spinal metastases using the conventional and on-line treatment technique. The costs were calculated in 1999 Canadian dollars using the equipment costs given in Table Number of Full-time employee (FTE) required for the different tasks of the on-line planning and delivery technique. The numbers in parenthesis refer to the task numbers on Figure 6-1. Abbreviations: Therapist (MRT(T)), Project physicist (P_Phys), QA Physicist (QA_Phys) and Radiation-oncologist (RO) xix

20 Chapter 1 GENERAL INTRODUCTION 1

21 1.1 Radiotherapy Brief history Directly or indirectly ionizing radiation such as high-energy electrons and photons have been used to treat various type of cancers for more than a century. The first reported biological effect of radiation was observed by Henri Becquerel in France in 1896 shortly after the discovery of artificially (Röntgen, 1895) and naturally (Becquerel, 1896) occurring radiation. Becquerel described the skin erythema that appears on his chest a few weeks after forgetting a small container of radioactive material in the pocket of his suit jacket 1. The field of radiation treatment or radiotherapy grew rapidly in Europe and in United States at the beginning of the twentieth century after the discovery of radium by Marie and Pierre Curie in 1898 and the improvement of Röntgen s cathode tube. Up to the 1930 s, it was usual to separate X-ray therapy from radium therapy. X-ray tubes were used for superficial and deep-seated tumors whereas radium therapy was limited to the treatment of superficial cancers and tumors accessible in body cavities 2. X-ray units or orthovoltage units with potential of 250 kilovolts (kv) were already available in The dose that could be delivered to deep-seated tumors with orthovoltage was limited by the skin dose. Cross-fire techniques using multiple ports or beams to target the tumor were develop to overcome the limited penetration of the orthovoltage X-ray beams. Units capable of producing beam quality of 1 mega electron-volt (MeV) such as the Van De Graaf generator became available as early as the 1930 s 2. Their size and cost was however prohibitive and limited their clinical use. The development of cobalt treatment units 3, 4, such as that at the University of Saskatchewan in 1951, overcame some of the beam quality 2

22 limitations of orthovoltage units. Indeed, the shape of the depth-dose curve (variation of absorbed dose as a function of depth in water) of 60 Co allowed for skin sparing while delivering a significant dose at depth. The isocentric mount of the radio-isotope source on a gantry support and the high specific activity of the 60 Co (activity per unit of mass, Ci/g) compared to 137 Cs and 226 Ra also contributed to make the cobalt unit the clinical work horse of radiotherapy departments for the next 30 years. In the early days of radiotherapy, radiation was widely used for the treatment of various nonmalignant diseases and conditions such as arthritic joints, infertility (irradiation of pituitary gland) and facial acne. The recognition of late toxicity and secondary malignancy reduced the use of radiation treatments for benign conditions. Although radiotherapy is primarily used nowadays for the treatment of malignant diseases, it still plays a role in the management of benign brain tumors and the treatment of trigeminal neuralgia 5. Radiation treatments are also used postoperatively to prevent keloid formation 6 and heterotopic ossification after total hip arthroplasty 7. The term radiotherapy or radiation therapy is still widely used but there is a tendency to replace it by the more accurate denomination radiation oncology, which reflects the specific use of radiation for cancer treatment Modern Radiotherapy Today, radiotherapy is one of the principal therapeutic methods along with surgery and chemotherapy to treat cancer. In developed countries, 50% of all new cancer cases will receive radiation 8, 9. The treatment intent depends of the site and stage of the disease as well as patient 3

23 factors and can be radical, adjuvant or palliative. Radiotherapy treatments are often broadly classified based on their delivery method as external beam radiotherapy or brachytherapy. In both approaches, the objective for radical radiotherapy is to achieve local control of the disease by delivering a high radiation dose to the tumor while sparing the surrounding normal tissues. In external beam radiotherapy, the target volume is irradiated with radiation beams produced by a source located at a distance (25 to 100 cm) from the patient. The electron linear accelerator (or linac) is now the treatment unit of choice in external beam radiotherapy due to its isocentric configuration, manageable size and ability to produce a wide range of photon and electron beam energies (usually 4 to 21 MeV beams). The development of linear accelerators in the 1950 s was made possible by the production of powerful and stable micro-wave sources (magnetron and klystron) developed for radar technology during the second world war 10. The linear accelerators almost completely replaced the cobalt units in radiotherapy departments in North America and in Europe by the start of 1990s. The photon beam energies produced by a linac are traditionally specified in unit of mega-volts (MV or 10 6 volts) while the electron beam energies are specified in terms of mega electron-volt (MeV). This range of beam quality allowed deepseated tumors such as prostate cancer as well as superficial skin cancer to be treated efficiently with linear accelerators. It should be noted that orthovoltage units are still used for the treatment of superficial cancers but there is a tendency to replace orthovoltage therapy with electron beam therapy delivered with a linear accelerator. Brachytherapy, also called curietherapie in french in honour of Marie and Pierre Curie, uses radioactive sources implanted temporally or permanently in the patient to delivery dose to the target volume. This irradiation technique produces very localized dose distributions because the 4

24 drop off of the radiation is dominated by the inverse of the square of the distance from the source. Brachytherapy is primarily used for the treatment of gynaecologic and prostate cancer. Another type of brachytherapy treatment involves injection into the blood stream of radioisotopes, which have an affinity with specific tissue, e.g. bone or thyroid. The SI unit for radiation dose is the gray (Gy), which is defined as 1 joule of absorbed energy per kilogram (kg). The old non-si unit for absorbed dose is the rad (1 rad = 0.01 Gy). The non- SI unit of centigray (0.01 cgy) is also used due to its equivalence to the rad. At the radiation energy levels used in radiotherapy, electron beams (directly ionizing radiation) deposit their kinetic energy in the surrounding medium mainly by ionization of the absorber. High-energy photons of 4 to 18 MV (indirectly ionizing radiation) on the other hand interact with the irradiated medium mostly by Compton effect and set in motion fast-moving electrons. The biological effect of radiation arises primarily from damage to the DNA 1. When primary or scattered electrons pass through a living cell, they can damage the DNA of the cell indirectly or directly. Direct DNA damages such as base deletion, and single and double strand breaks can be produced with the ionization of the atoms forming the DNA itself. The electrons can alternatively ionize other molecules in the cell and produce toxic free radicals such as a hydroxyl radical (OH ), which can diffuse through the cell and interact with the DNA. Highenergy photons and electrons used in radiotherapy are sparsely ionizing radiation (low linear energy transfer, LET) compared to protons, neutrons and heavy ions 11, and primarily damage DNA indirectly through free radical production. 5

25 Dose fractionation schemes used in current approaches to radical radiotherapy were derived from experience in the early days of X-ray and radium therapy 1. Depending on the tumor size, a total dose of 50 to 80 Gy is delivered in 1.8 to 2 Gy per fraction, with 5 fractions given per week. The rationale for the fractionation of the radiotherapy treatments can be explained using the four Rs of radiobiology (repair, redistribution, repopulation and reoxygenation) 12. Sufficient time between fractions allows for repair of sublethal damage and repopulation of normal cell populations. If the half-life recovery from radiation damage in normal tissue ranges from 1 to 2 hours 12 a minimum inter-fraction time of 6 to 12 hours should be respected for almost complete repair. Fractionated radiotherapy has also the advantage to increase the tumorgenic effect of treatment because of reoxygenation of the target and redistribution of the cancer cells into radiosensitive phases of the cell cycle between the fractions. The intrinsic radio-sensitivity of the tumor and irradiated surrounding normal tissues is sometime called the fifth R of radiobiology and may dictate the total prescribed dose. 1.2 Palliative Radiotherapy and Bone Metastases General About 50% of all radiotherapy patients are treated with palliative intent 8, 9, 13, 14. The goal of these treatments is to maintain the patients quality of life by controlling the progression of disease-related symptoms (both pain and loss of function). Dose prescriptions for symptom palliation are typically less than curative/radical treatment and therefore palliative fractionation schedules can be shorter and use higher dose per fraction 15. Typical dose fractionation schedule 6

26 ranges from 8 Gy in one fraction to 30 Gy in 10 fractions. Logistically, this is advantageous for the patient as it limits the number of visits to the cancer treatment center. Side effects related to curative or palliative treatments are usually defined as acute ( 90 days after treatment start) or late (> 90 days). For palliative radiotherapy, acute toxicity is limited due to the low total dose. Late effects are also not usually an issue due to the low prescribed dose and the limited life expectancy of patients with advanced cancer. Dose limits to critical organs such as the spinal cord still have to be respected especially when re-treatment may be a consideration. For example, if a patient who already received 8 Gy in 1 fraction to vertebrae T12 and L1 needs retreatment to the same vertebrae, the new prescription dose should take into account previous treatments to avoid late toxicity. For spinal cord, radiation tolerance dose that has 5% probability of myelopathy within 5 years from treatment is generally considered to be 50 Gy in 2 Gy/fraction. Biologically equivalent dose calculation using the linear-quadratic model 1, 12 can be used to cumulate the dose from multiple courses of radiotherapy with different fraction size and verify that tolerance is not exceeded. Bone metastases are common in patients with advanced cancer and represent the most common source of cancer-related pain 16. Bone metastases are most often seen with prostate, breast and lung primaries 15. Radiotherapy is used in the management of bone metastases for pain relief, bone re-mineralization, prevention of pathological fracture, and spinal cord compression. Bone metastases are classified as complicated (instead of uncomplicated) in the presence of related pathological fracture, spinal cord compression or neuropathic pain. At the Princess Margaret Hospital (PMH), between 2002 and 2004, on average about 42% of radiotherapy treatment courses delivered annually were with palliative intent (Figure 1-1). During the same period, the 7

27 average number of bone metastases cases represented about 19% of all patients treated, from which more than half had metastases of the spine. These numbers are slightly lower but consistent with the statistics on the use of palliative radiotherapy reported in the literature (50% of all radiotherapy patients treated with palliative intent of which half had bone metastases) 13, 14. 8, 9, Figure 1-1. Annual palliative treatment statistics at the Princess Margaret Hospital (PMH) from 2002 to This histogram presents the relative number of palliative treatment courses (all sites included) delivered annually at PMH. The relative number of courses was also compiled for all bone metastasis and for bone metastasis of the spine alone. 8

28 Hemi-body irradiation (HBI), systemic radionuclide brachytherapy and local irradiation have all been used to treat bone metastases 15. Local irradiation is the most common treatment technique for bone metastases and will be discussed in detail in this work. Briefly, with HBI, the upper half (base of skull to iliac crest) or the lower half (iliac crest to ankles) of the body is treated with a large radiation field 17. HBI is used for the rapid palliation of widespread and symptomatic metastatic bone disease. Typically, a single fraction of 6 Gy to the upper body and 8 Gy to the lower body is used. The dose to the upper body is smaller than for the lower body due to lung tolerance. Fractionated HBI is also possible and has the potential for a higher total dose delivered with toxicity similar to single fraction 18. Common side effects associated with single-fraction HBI are nausea, vomiting and diarrhea 19. Systemic radionuclide brachytherapy used for the management of widespread metastatic bone disease most commonly involves 89 Sr and 153 Sm which are β emitters and have a mean range of less than 3 mm. After injection, the radio-isotope is deposited at the site of osteoblastic bone metastases 20, 21. Systemic radionuclide brachytherapy is used mainly for patients with advanced prostate cancer as their bone metastases tend to be osteoblastic rather than osteolytic. Toxicity associated with systemic strontium brachytherapy consists mainly of reversible myelosupression 22. Local irradiation (referred to hereafter simply as radiotherapy) is used in the management of localized painful metastatic bone disease. Overall and complete pain response rates to radiotherapy of bony metastases are 59-62% and 32-34%, respectively 23, 24 with a median time to pain relief of 3 weeks 25. As expected, toxicity with local irradiation is site specific. Common acute side effects resulting from lower thoracic and lumbar spine radiotherapy are nausea and vomiting, whereas patients treated at the level of the cervical spine or pelvis might experience 9

29 dysphagia or diarrhea, respectively. As demonstrated by the Radiation Therapy Oncology Group (RTOG) protocol 97-14, acute toxicity for radiotherapy of painful bone metastases is mild (10-17% of patients) and late toxicity is rare (4%) 26. The median follow up for patients with reported late toxicity (at 90 days) was 7.6 months in the RTOG study Common dose fractionation schedules used for the treatment of bone metastases vary from 8 Gy in one fraction to 30 Gy in 10 daily fractions. For uncomplicated bone metastases, the latest meta-analysis from Chow et al 27 confirms previous findings 23, 24 that there is no significant difference in terms of overall or complete response rates or duration of pain relief between single or fractionated irradiation. In addition, no significant difference was observed in terms of acute toxicity 23, 24, 27 and analgesic consumption 23, 24. However, the rate of re-treatment was higher after single fraction irradiation than fractionated treatments (21% compared to 8%) 27. This difference might be due in part to the physician s reluctance to retreat after fractionated treatment due to the higher dose delivered to critical structures. Finally, after excluding the drop out patients, Chow et al reported for both single and fractionated treatments an overall response rate for pain greater than 70% (total number of patients: 4038), which demonstrates clearly the efficacy of radiotherapy of painful bone metastases. Radiotherapy for palliation is effective, however, there have been few technical advances in this field. Technical advances may allow this effective therapy to be more broadly applied and even more cost effective. 10

30 1.2.2 Conventional Workflow for radiotherapy of bone metastases. Workflow for radiotherapy of painful bone metastases at PMH is similar to radical radiotherapy and can be divided in three main steps: simulation, treatment planning and treatment delivery. It is a serial process that can be performed usually within 1 to 3 days. Traditionally, after physician consultation, the patient is first booked for an appointment at the conventional simulator. This unit is geometrically identical to a linear accelerator except that the high-energy treatment beam is replaced by a fluoroscopic imaging system. The patient is usually setup supine on the couch and the fluoroscopic system was used to determine the region to be treated based on bony anatomy and diagnostic information (plain films, bone scan, CT, etc). Once the treatment position was defined and the treatment isocenter was placed approximately at the center of the target, permanent skin marks were tattooed on the patient skin. These skin marks, which correspond to the projection of lasers intersecting at the isocenter, were used later at the treatment unit to setup the patient in the same position. Beam gantry angles, field size and depth of the isocenter for each beam were determined and recorded for dose calculation. Treatment simulation on a conventional simulator required at least two therapists. The treating radiation oncologist also came to the simulator room while the patient was on the simulator couch to verify the patient positioning, determine the region to be treated, and to prescribe the field size. Nowadays, conventional simulators have been replaced in many radiotherapy departments by CT scanners to allow 3D conformal radiotherapy. During the simulation process, the patient is usually setup supine on the CT couch, and the position of a reference point located approximately at the level of the region to be treated is selected based on surface anatomy. The patient skin is tattooed to mark the position of the reference point and fiducial markers are 11

31 superimposed on the skin marks to make them visible on the CT images. The patient is then scanned and the 3D dataset is transferred to the treatment planning system (TPS). At this point, the patient is simply sent home to return another day or has to wait in the department for the completion of the treatment plan and the first treatment. With the simulation performed at the CT scan, the position of the treatment isocenter, the beam angles and the field size are determined as part of the treatment planning process. In general, palliative treatments use simpler beam arrangements than radical treatments since normal tissue avoidance is not as critical in these lower dose treatments. For bone metastases of the spine for example, a single posterior beam or an anterior and a posterior beams are often used to produce a homogeneous dose distribution across the entire region of the spine including cord. Bony anatomy can be used in some cases for field size definition. However, the rich 3D information provided by helical CT images allow the physician to contour the target and conform the beam apertures to it. Most of the routine steps involved in treatment planning, such as image import and beam placement are performed by the planner (a.k.a dosimetrist). The radiation-oncologist will contour the target and some of the organs at risk and evaluate the dose distribution before approval. The planner will also attempt to optimize the dose distribution to satisfy the physician s prescription. In a typical radiotherapy setting, once treatment planning is completed, a medical physicist performs a quality assurance (QA) check of the plan. The plan QA involves, in particular, an independent dose calculation and the verification of the plan deliverability at the treatment unit. Upon physicist and physician s approval, the plan is transferred to the record and verify system for delivery at the linear accelerator and it is also copied in the patient s electronic chart. At this 12

32 point, the patient is scheduled for the treatment appointment. Compared to more complex radical treatments, no patient specific QA involving dosimetric measurements are performed for palliative treatments before the first day of treatment. The electronic transfer of the plan is verified by the therapists at the treatment unit by comparing manually the plan data (dose, beam angles, field size, etc) saved in the record and verify system with the patient s electronic chart. On the first day of treatment, the patient setup is reproduced by aligning the skin marks to the treatment room lasers. Lateral and anterior megavoltage electronic portal images are then acquired and manually registered based on bony anatomy to corresponding digitally reconstructed radiographs (DRR) to assist in patient positioning. The DRRs are generated by the planning system by projecting the 3D CT dataset on a 2D plane for a given beam angle. Finally, the couch is translated accordingly and the treatment is delivered. The elapsed time for the entire process from simulation to treatment delivery can vary widely depending on the human and material resources. Emergency patients with spinal cord compression for example are seen at the Princess Margaret Hospital by a radiation oncologist of the Palliative Radiation Oncology Program (PROP) and treated the same day. From the patient s perspective, this 1-day emergency treatment process represents three different appointments (physician consultation, simulation and treatment) separated by a few hours of waiting. The conventional method of treatment described above also involves patient setup and imaging at the simulator (conventional or CT) and at the treatment unit. For patients suffering from painful bone metastases, the multiple setups can potentially increase their discomfort. The reproduction of the patient position at the treatment unit based on the initial setup at the 13

33 simulator can also introduce inter-fraction setup errors, which is a potential source of geometric uncertainty for dose delivery. 1.3 Radiotherapy treatment planning: Off-line and on-line Radiotherapy treatment planning is a serial process, which is generally performed off-line in the hours or days available between the simulation and treatment appointments. Radiation oncologist, planner and/or physicist are involved in the planning process. Once the patient anatomic representation (e.g. CT dataset) is imported in the TPS, the radiation oncologist contours the gross target volume (GTV) 28, 29 and the organs at risk (OAR). The planning target volume (PTV) is created by adding margins to the GTV to take into account microscopic extensions of the disease (clinical target volume, CTV) as well as setup errors and internal organ motion. Margins can also be added to the OAR to take into account setup errors and organ motion. The expanded OARs are called planning organ at risk volumes (PRVs) 28, 29. The beam arrangement and the field sizes are finally designed by the planner to satisfy the radiation oncologist s prescription and dose limits. Although, commercial TPSs offer plan libraries and scripting capabilities to streamline the planning process, it is still mostly performed manually through an iterative trial and error approach. On-line planning is defined in this project as the generation of a radiotherapy treatment plan in real-time while the patient is lying on the treatment couch, waiting for radiation delivery. This is a fairly new concept in external beam radiotherapy. On-line planning was made possible with the availability of in-room imaging systems such as in-room helical CT 30, 31, Tomotherapy 32, and 14

34 megavoltage (MV) 33, 34 and kilovoltage (kv) 35 cone beam CT (CBCT). All these platforms can provide the user with a complete 3D representation of the patient (including skin contour and various levels of soft tissue contrast) that could potentially be used for on-line planning. The method of treatment and the tools developed in this work are based on the use of kilovoltage cone-beam CT (hereafter, CBCT). The on-line planning and delivery treatment technique for bone metastases patients share similarities with the daily re-planning process suggested in the adaptive radiation therapy (ART) approach for radical radiotherapy of prostate cancer. The original ART feedback process developed by D. Yan et al used patient specific information (e.g. daily CT and portal images) measured during the course of treatment to generate patient specific margins and reoptimize the patient s treatment plan. This re-optimization of the plan was performed once between the fourth and fifth fraction. On-line re-planning represents an extreme form of ART for which the dose distribution would be adapted to the patient anatomy (PTV and OAR) on a daily basis. Both on-line re-planning for prostate radiotherapy and the on-line treatment method proposed in this work use volumetric images of the patient acquired at the treatment unit prior to dose delivery. Although planning studies have shown the benefit of re-planning in terms of target coverage and OAR sparing for prostate cancer patients, this approach is still not clinically implemented due to technical challenges such as automatic soft tissue delineation and real-time optimization of beam intensity modulation. Contrary to prostate treatment re-planning, the online planning of bone metastases is facilitated by the contrast of the bony targets in CT and the use of simple beam arrangements without beam intensity modulation. 15

35 On-line planning is not completely novel as interstitial brachytherapy of prostate cancer represents a successful clinical application of on-line treatment planning In this treatment technique, trans-rectal ultrasound images of the prostate are acquired just prior to the brachytherapy source implant. The radiation oncologist then contours the prostate on the axial ultrasound images and the distribution of source positions or stopping positions is designed manually or automatically. Fast planning systems using simulated annealing algorithm 45 can determine automatically in only a few minutes the source positions, which will optimize the dose distribution for prostate coverage and OAR sparing. Finally, treatment plan QA is performed and the sources are loaded for prostate implantation. This complex brachytherapy technique can be performed efficiently in real-time due in particular to the development of dedicated planning tools and the high level of integration between imaging, planning and delivery systems. The availability of all professionals at the time of implant is also essential. The training of specialized therapists in brachytherapy to perform most of the routine work has the potential to reduce physician and physicist workload and facilitates the clinical implementation of this treatment technique. The workflow designed for on-line planning of prostate brachytherapy could serve as a model for the clinical implementation of the on-line planning and delivery technique for radiotherapy of spinal metastases. 1.4 Objective, hypothesis and rationale The objective of this work is to develop an on-line planning and delivery method of radiotherapy treatment for patients with bone metastases of the spine. This new method of treatment will integrate all the preparation and delivery steps into one single session equivalent 16

36 to an initial treatment appointment ( 30 min). In summary, after physician consultation, the patient will be scheduled the same day for an appointment at a treatment unit capable of conebeam CT imaging (CBCT) 35, 46, 47. The patient will be setup on the treatment couch with the vertebrae to be treated approximately aligned with the machine isocenter based on surface anatomy. A volumetric CBCT dataset will then be acquired and transferred to a TPS workstation located at the treatment unit console. The treatment plan will be performed in realtime while the patient is lying on the couch and transferred to the linear accelerator for delivery following plan QA and approval. The on-line treatment technique described in this work was designed for patients with spinal bone metastases as they represent at least half of all bony metastases patients treated at the Princess Margaret Hospital (Figure 1-1). The method and the tools developed here could potentially be modified and extended for application to other sites of bone metastatic disease. The rationale for the development of this on-line planning and delivery treatment technique is related to patient quality of life. If performed in a reasonable time, the on-line treatment approach has the potential to reduce the patient s burden. From the patient s perspective, the on-line treatment technique described above should be more appealing than the conventional method of treatment as it will be less time consuming, involving only one setup at the treatment unit and eliminates the waiting period between simulation and treatment. An efficient on-line planning and delivery technique also has the potential to improve departmental efficiency by making better use of human and material resources and consequently improves patient access to palliative radiotherapy treatments. 17

37 A research ethics board (REB) approved protocol was opened in January 2005 at the Princess Margaret Hospital to assess the feasibility of the on-line treatment approach and its impact on patient satisfaction and departmental efficiency. This clinical protocol is divided into three phases. Phase A was designed for CBCT image quality assessment, and the application of the on-line method on phantoms. The second phase of the protocol (Phase B) covered the development of on-line planning tools, the simulation of the on-line treatment process on patient images, and the training of the different professionals involved in this new process. Finally, the last phase of the protocol (Phase C) covered the clinical implementation of the on-line treatment method and a patient satisfaction study. The technical developments achieved toward the clinical implementation of the on-line planning and delivery treatment technique are presented in this work. The results related to the patient satisfaction study will be described at a later time in a separate report. The technical challenges related to the development and the implementation of the on-line planning and delivery method are numerous. The suitability of CBCT image for planning represents the first roadblock. For example, CBCT images have lower low-contrast resolution than conventional fan-beam CT due in particular to the high level of X-ray scatter reaching the imaging detector in the cone-beam geometry The presence of scatter also affects the accuracy of the CT number (or Hounsfield units, HU) representation with CBCT These CBCT image artefacts must be reduced to facilitate manual and automatic target definition and allow for accurate dose calculation. Aside from image quality, the integration of all the subsystems involved in the process represent another important challenge. Communication between imaging, planning, QA and delivery systems should be streamlined and automated in 18

38 order to shorten and simplify the entire process. On-line planning tools for semi-automatic detection and segmentation of vertebrae to be treated as well as automatic generation of simple dose plans will also be required for a robust and efficient planning process. The use of CBCT imaging, semi-automatic planning and high-level integration for simple palliative treatments also has an impact on the type of machine QA that should be performed on this highly integrated system. An integral test of the entire imaging and treatment system should be routinely performed to evaluate the process. Finally, the clinical implementation of the on-line planning and delivery method includes other challenges such as the monitoring and management of potential patient motion while the patient is waiting on the couch (intra-fraction motion) as well as methods for rapid yet effective on-line QA of the treatment plan. The tools and techniques developed to solve these problems and limitations represent the core of this work. The challenges and proposed solutions are presented in detail in the following chapters. Section 1.5 (Outline of this thesis) summarizes the sub-objectives of this thesis and will provide the readers with an overview of each chapter. 1.5 Outline of the Thesis In Chapter 2, the development and the implementation of hardware and software corrections for CBCT images are presented. The performance of these correction methods was assessed on phantom and clinical CBCT images in terms of image uniformity (reduction of cupping artefact) and accuracy of CBCT numbers. The workflow for the on-line planning and delivery treatment technique is presented in Chapter 2 along with the integration of all the sub-systems involved in 19

39 the process. The evaluation of the on-line treatment process in terms of duration and dosimetric accuracy was performed in a customized spine phantom constructed for the study. The development and characterization of an integral test phantom for the on-line planning and delivery technique is presented in Chapter 3. This phantom, which contains point detectors (diodes) allows for real-time QA of the entire on-line treatment process (image guidance, planning and treatment) in terms of dose delivery accuracy. It replaces the spine phantom presented in Chapter 2 and facilitated routine QA of CBCT image guidance and dose delivery system. The application of the integral test phantom for QA radiosurgery-type treatments (highprecision, high-dose treatment) is also presented in this chapter, which demonstrates the versatility of the new phantom. Chapter 4 covers the development and the validation of a semi-automatic method for detection and identification of vertebrae on CBCT images. Semi-automatic detection of anatomical features is a valuable tool that could be employed to accelerate the on-line planning step and shorten the overall on-line treatment process. This algorithm is called the vertebra finder (VF). It is interfaced with a research version of a commercial planning system (TPS, Pinnacle 3, version 7.9u, Philips Medical Systems, Andover, MA) and it can be used in conjunction with the TPS auto-segmentation algorithm to streamline the target definition process and improve the efficiency of the on-line treatment planning step. In Chapter 5, the issue of patient intra-fraction motion was evaluated for a group of 49 patients treated with palliative intent for bone metastases of the spine or soft tissue masses located in the 20

40 pelvis or the thorax. The spinal intra-fraction motion was measured by registering the bony anatomy on pre and post-treatment CBCT scans. Surface markers fixed (with adhesive tape) on the patient s skin were tracked with an infrared camera system during the treatment. The use of surface marker tracking as a surrogate for gross spine motion was investigated by comparing intra-fraction motion measured with CBCT and the optical tracking system. The level of intrafraction motion measured on CBCT was used to estimate a population margin for spine intrafraction motion for this group of patients. A statistical analysis to assess the potential of optical tracking of surface markers as a surrogate for spine position correction concludes this chapter. Finally, the clinical implementation of the on-line planning and delivery technique and the description of the treatment of the first 7 patients treated with this method are presented in Chapter 6. The workflow and the role of each professional involved in the process is described in this chapter. The development of software to assist the physicist in the plan QA task is also presented. The experience of clinical implementation of the on-line treatment approach is followed in Chapter 7 by a general discussion on the cost-benefit of the on-line planning and delivery technique as well as future directions for this novel method of treatment. 21

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43 Hoogenhout, C. Warlam-Rodenhuis, G. van Tienhoven, R. Wanders, J. Pomp, M. van Reijn, I. van Mierlo, E. Rutten, "The effect of a single fraction compared to multiple fractions on painful bone metastases: a global analysis of the Dutch Bone Metastasis Study," Radiother Oncol. 52, (1999). 26. W. F. Hartsell, C. B. Scott, D. W. Bruner, C. W. Scarantino, R. A. Ivker, M. Roach, 3rd, J. H. Suh, W. F. Demas, B. Movsas, I. A. Petersen, A. A. Konski, C. S. Cleeland, N. A. Janjan, M. DeSilvio, "Randomized trial of short- versus long-course radiotherapy for palliation of painful bone metastases," J Natl Cancer Inst. 97, (2005). 27. E. Chow, K. Harris, G. Fan, M. Tsao, W. M. Sze, "Palliative radiotherapy trials for bone metastases: a systematic review," J Clin Oncol. 25, (2007). 28. ICRU. Prescribing, Recording and Reporting Photon Beam Therapy, Report No. 50. Washington, DC: International Commission on Radiation Units and Measurements; ICRU. Prescribing, Recording and Reporting Photon Beam Therapy (Supplement to ICRU Report 50), Report No. 62. Washington, DC: International Commission on Radiation Units and Measurements; M. Uematsu, A. Shioda, K. Tahara, T. Fukui, F. Yamamoto, G. Tsumatori, Y. Ozeki, T. Aoki, M. Watanabe, S. Kusano, "Focal, high dose, and fractionated modified stereotactic radiation therapy for lung carcinoma patients: a preliminary experience," Cancer. 82, (1998). 31. A. S. Shiu, E. L. Chang, J. S. Ye, M. Lii, L. D. Rhines, E. Mendel, J. Weinberg, S. Singh, M. H. Maor, R. Mohan, J. D. Cox, "Near simultaneous computed tomography image-guided stereotactic spinal radiotherapy: an emerging paradigm for achieving true stereotaxy," Int J Radiat Oncol Biol Phys. 57, (2003). 32. J. S. Welsh, R. R. Patel, M. A. Ritter, P. M. Harari, T. R. Mackie, M. P. Mehta, "Helical tomotherapy: an innovative technology and approach to radiation therapy," Technol Cancer Res Treat. 1, (2002). 33. E. C. Ford, J. Chang, K. Mueller, K. Sidhu, D. Todor, G. Mageras, E. Yorke, C. C. Ling, H. Amols, "Cone-beam CT with megavoltage beams and an amorphous silicon electronic portal imaging device: potential for verification of radiotherapy of lung cancer," Med Phys. 29, (2002). 34. J. Pouliot, A. Bani-Hashemi, J. Chen, M. Svatos, F. Ghelmansarai, M. Mitschke, M. Aubin, P. Xia, O. Morin, K. Bucci, M. Roach, 3rd, P. Hernandez, Z. Zheng, D. Hristov, L. Verhey, "Low-dose megavoltage cone-beam CT for radiation therapy," Int J Radiat Oncol Biol Phys. 61, (2005). 24

44 35. D. A. Jaffray, J. H. Siewerdsen, J. W. Wong, A. A. Martinez, "Flat-panel cone-beam computed tomography for image-guided radiation therapy," Int J Radiat Oncol Biol Phys. 53, (2002). 36. D. Yan, E. Ziaja, D. Jaffray, J. Wong, D. Brabbins, F. Vicini, A. Martinez, "The use of adaptive radiation therapy to reduce setup error: a prospective clinical study," Int J Radiat Oncol Biol Phys. 41, (1998). 37. D. Yan, F. Vicini, J. Wong, A. Martinez, "Adaptive radiation therapy," Phys Med Biol. 42, (1997). 38. D. Yan, D. Lockman, D. Brabbins, L. Tyburski, A. Martinez, "An off-line strategy for constructing a patient-specific planning target volume in adaptive treatment process for prostate cancer," Int J Radiat Oncol Biol Phys. 48, (2000). 39. L. E. Court, L. Dong, A. K. Lee, R. Cheung, M. D. Bonnen, J. O'Daniel, H. Wang, R. Mohan, D. Kuban, "An automatic CT-guided adaptive radiation therapy technique by online modification of multileaf collimator leaf positions for prostate cancer," Int J Radiat Oncol Biol Phys. 62, (2005). 40. R. Mohan, X. Zhang, H. Wang, Y. Kang, X. Wang, H. Liu, K. K. Ang, D. Kuban, L. Dong, "Use of deformed intensity distributions for on-line modification of image-guided IMRT to account for interfractional anatomic changes," Int J Radiat Oncol Biol Phys. 61, (2005). 41. M. Ghilezan, D. Yan, J. Liang, D. Jaffray, J. Wong, A. Martinez, "Online image-guided intensity-modulated radiotherapy for prostate cancer: How much improvement can we expect? A theoretical assessment of clinical benefits and potential dose escalation by improving precision and accuracy of radiation delivery," Int J Radiat Oncol Biol Phys. 60, (2004). 42. Q. Wu, G. Ivaldi, J. Liang, D. Lockman, D. Yan, A. Martinez, "Geometric and dosimetric evaluations of an online image-guidance strategy for 3D-CRT of prostate cancer," Int J Radiat Oncol Biol Phys. 64, (2006). 43. G. K. Edmundson, N. R. Rizzo, M. Teahan, D. Brabbins, F. A. Vicini, A. Martinez, "Concurrent treatment planning for outpatient high dose rate prostate template implants," Int J Radiat Oncol Biol Phys. 27, (1993). 44. G. K. Edmundson, D. Yan, A. A. Martinez, "Intraoperative optimization of needle placement and dwell times for conformal prostate brachytherapy," Int J Radiat Oncol Biol Phys. 33, (1995). 45. E. Lessard, J. Pouliot, "Inverse planning anatomy-based dose optimization for HDRbrachytherapy of the prostate using fast simulated annealing algorithm and dedicated objective function," Med Phys. 28, (2001). 25

45 46. D. A. Jaffray, D. G. Drake, M. Moreau, A. A. Martinez, J. W. Wong, "A radiographic and tomographic imaging system integrated into a medical linear accelerator for localization of bone and soft-tissue targets," Int J Radiat Oncol Biol Phys. 45, (1999). 47. D. A. Jaffray, J. H. Siewerdsen, "Cone-beam computed tomography with a flat-panel imager: initial performance characterization," Med Phys. 27, (2000). 48. J. H. Siewerdsen, D. A. Jaffray, "Cone-beam computed tomography with a flat-panel imager: effects of image lag," Med Phys. 26, (1999). 49. J. H. Siewerdsen, D. A. Jaffray, "A ghost story: spatio-temporal response characteristics of an indirect-detection flat-panel imager," Med Phys. 26, (1999). 50. J. H. Siewerdsen, D. A. Jaffray, "Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter," Med Phys. 28, (2001). 51. D. Letourneau, J. W. Wong, M. Oldham, M. Gulam, L. Watt, D. A. Jaffray, J. H. Siewerdsen, A. A. Martinez, "Cone-beam-CT guided radiation therapy: technical implementation," Radiother Oncol. 75, (2005). 26

46 Chapter 2 ON-LINE PLANNING AND DELIVERY TECHNIQUE FOR RADIOTHERAPY OF SPINAL METASTASES USING CONE-BEAM CT: IMAGE QUALITY AND SYSTEM PERFORMANCE Daniel Létourneau 1, M.Sc. Rebecca Wong 1,2, MD Douglas Moseley PhD 1,2, Ph.D. Michael B Sharpe PhD 1,2, Ph.D. Stephen Ansell 1 B.Sc. Mary Gospodarowicz 1,2, MD David A Jaffray 1,2,3, Ph.D. 1 Radiation Medicine Program, Princess Margaret Hospital, Toronto, ON, Canada 2 Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada 3 Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada This chapter was published in the Int J Radiat Oncol Biol Phys 67: ,

47 2.1 Abstract Purpose. To assess the feasibility of an on-line strategy for palliative radiotherapy (RT) of spinal bone metastasis, which integrates imaging, planning and treatment delivery in a single step at the treatment unit. The technical challenges of this approach include cone-beam CT (CBCT) image quality for target definition, on-line planning, and efficient process integration. Methods and Materials. An integrated imaging, planning and delivery system was constructed and tested with phantoms. The magnitude of CBCT image artifacts, following the use of an anti-scatter grid and a non-linear scatter correction, was quantified using phantom data and images of patients receiving conventional palliative RT of the spine. The efficacy of on-line planning was then assessed using corrected CBCT images. Testing of the complete process was performed on phantoms with assessment of timing and dosimetric accuracy. Results. The use of image corrections reduced the cupping artifact from 30% to 4.5% on CBCT images of a body phantom and improved the accuracy of CBCT numbers (water: ±20 HU, and lung and bone: to within ±130 HU). Bony anatomy was clearly visible and was deemed sufficient for target definition. The mean total time (n=5) for application of the on-line approach was 23.1 min. Image-guided dose placement was assessed using radiochromic film measurements with good agreement (within 5% of dose difference and 2 mm of distance to agreement). Conclusions. The technical feasibility of CBCT guided on-line planning and delivery for palliative single treatment has been demonstrated. The process was performed in one session 28

48 equivalent to an initial treatment slot (<30 min) with dosimetric accuracy satisfying accepted radiotherapy standards. 29

49 2.2 Introduction Skeletal metastases are the most common indication for palliative radiotherapy (RT) and represent up to 25% of the patient load in a radiation oncology department 1-4. The majority of patients with bone metastases have pain, which can be effectively palliated with local RT. Ideally, treatment of painful bone metastases should be expedited to offer prompt symptom relief. For palliative RT, the conventional preparation and delivery process is onerous as it involves hours of waiting and repeated setup at the simulator and the treatment unit. This may even limit the referral of very ill patients with poor performance status for RT 4. With the availability of treatment units capable of volumetric CT imaging 5-8, image-guided radiotherapy could potentially be used in palliation to expedite treatments and reduce burden to the patient. An on-line treatment strategy for the specific purpose of treating patients with painful bone metastases of the spine is proposed. This strategy integrates all of the imaging, planning, and delivery steps into a single session equivalent to an initial treatment appointment (~30 minutes). In this approach, after physician consultation, the patient would be booked on a linear accelerator capable of cone-beam CT acquisition (CBCT) where volumetric imaging, on-line treatment planning, and delivery would be performed in an integrated treatment process. The key technical challenges pertaining to this approach are related to the effective integration of the multiple subsystems involved and the correction of the CBCT images to make them suitable for target definition and planning. These challenges are addressed in this work through (i) development and implementation of a combined hardware and software image correction scheme to reduce cupping artifact (image non-uniformity) typical of CBCT images and improve 30

50 CBCT-number accuracy, and (ii) the evaluation of the on-line treatment process in terms of duration and dosimetric accuracy achieved in a customized spine phantom constructed for the evaluation. 2.3 Methods and Materials System Description The linear accelerator equipped with integrated X-ray volumetric imaging (Elekta, Crawley, UK) used for the implementation of the on-line treatment strategy of spine metastases has been previously described in detail 7, In brief, the imaging system consists of an X-ray source and an opposing amorphous silicon flat-panel detector mounted on a linear accelerator, with the imaging beam orthogonal to the treatment beam. The kilovoltage projections required to reconstruct the CBCT data set are acquired during a rotation of 360, with a maximal angular speed of one rotation/min. During the application of the on-line treatment strategy, the 3D dataset reconstructed with an in-house software using a Feldkamp-Davis-Kress filtered-back projection algorithm 13 is transferred to the treatment planning system (TPS) workstation (version 7.6c, Pinnacle 3, Philips Medical Systems, Andover, MA) located at the treatment unit console. The projection acquisition, reconstruction and transfer to the planning system were streamlined. An application running on the planning workstation monitors the progress of the CBCT dataset acquisition and uploads the images into an active TPS session as soon as the reconstruction is completed 14, 15. The planning is then performed without disturbing the patient s position and approved before transfer to the treatment management system (RT- Desktop from Elekta via Dicom RT) for subsequent dose delivery. 31

51 2.3.2 Image Processing and Impact on Dose Calculation Currently, the CBCT images acquired as part of the on-line treatment strategy are often characterized by cupping artifacts and an inaccurate representation of CT-numbers, caused mainly by the elevated presence of x-ray scatter reaching the detector in the cone-beam geometry. In order to reduce the image non-uniformity and improve CBCT-number accuracy, approaches of either rejecting the scattered radiation via post-patient grids 11, 16 and/or correcting for the presence of scatter using a non-linear correction to the attenuation via software can be used. A combined approach is taken in this case. The focused grid (Soyee Products linear grids, Korea) used in this study has a 10:1 ratio and can be mounted directly on the housing of the flat panel. The non-linear software correction is based on published methods 17, 18 with parameters determined by reducing globally the image non-uniformity caused by scatter, beam hardening and also detector non-linearity. The non-linear correction was implemented as part of the reconstruction process in our in-house software. It is applied on a pixel-by-pixel basis in the projection image following correction for dark signal offset and spatial variations in detector response. In the first step, the negative natural logarithm was used to convert the projection data into measured attenuation, μx (product of the average linear attenuation coefficient μ and the x-ray path length x). An empirical nonlinear relationship between attenuation and path length was then used to convert the uncorrected signal into an equivalent thickness of water. On a pixel-by-pixel basis, the corrected attenuation was obtained by multiplying the calculated path length by the linear attenuation coefficient of water (nominally μ water = 0.2). 32

52 In the implementation reported here, the non-linear relationship between un-corrected attenuation and water path length was modeled with a sixth order polynomial equation. The shape of the correction curve (and thus the coefficients of the polynomial equation) was determined iteratively by minimizing the difference between CBCT and helical CT numbers for solid-water for a cylindrical phantom imaged with both the CBCT system and a helical CT scanner (Discovery ST 16 slice, GE Healthcare, Milwaukee, WI). The optimization was performed in reconstruction space with a simplex search method 19 for both head and body phantoms and for three different fields-of-view in superior to inferior (SI) direction (FOV z ) of 0.8, 10 and 20 cm. The transverse field-of-view (FOV t ) was held constant at 40 cm. Both the head and body phantoms (16 and 33 cm in diameter) were made of solid water and contained density inserts ranging in relative electron density from 0.28 to The body phantom (RMI, Gammex, Middleton, WI), being only 5 cm in longitudinal extent, was placed between two large blocks of polyethylene to simulate full scatter conditions. The average CT number of the solidwater was calculated for square regions of interest (ROI) of 1 cm 2 distributed on the central slice (1 mm thickness) of the reconstructed data set. The selection of only solid-water-filled ROIs directed the algorithm to prioritize cupping artifact reduction. The choice to optimize image uniformity was favored over CBCT-number accuracy for low and high-density materials because CBCT image non-uniformity in high-scatter conditions can confound the use of autosegmentation tools even for high-density material such as bones. Alternatively, optimization could have been performed to maximize CT accuracy instead of image uniformity by including ROIs that contained a range of CT numbers. 33

53 The relative contributions of the grid and the non-linear correction were evaluated by comparing the head and body phantom images (before and after correction) to the results obtained with a helical CT scanner (Discovery ST 16 slice, GE Healthcare, Milwaukee, WI). The image uniformity was quantified by calculating the relative difference (t 20 cup ) between the minimum and maximum CBCT-numbers for water along a horizontal profile going through the center of the phantoms. The CBCT-number accuracy was then assessed by plotting the CBCT numbers as function of the helical CT numbers for each of the density inserts included in the head and the body phantoms. For each scan, the breast insert (4% contrast relative to water) was also used to evaluate the contrast resolution in the image by calculating the contrast (μ breast μ water ), noise (standard deviation of μ breast and μ water ) and contrast-to-noise ratio (CNR) 20. Finally, this analysis was repeated for a set of head phantom images corrected using the curve corresponding to the body phantom to demonstrate the possibility of using a single correction curve for a range of different phantom sizes. The correction scheme (hardware and software corrections) was further tested by applying it to anthropomorphic phantom 21 and spine patient scans. These patients were enrolled in a research ethics board (REB) approved protocol on the feasibility of the on-line treatment approach. CBCT and helical CT scans of the phantom pelvis, thorax and head and neck were manually registered and compared together in terms of image uniformity and CT-number accuracy using the method of joint histograms 22. The image registration accuracy was limited by the image voxel size (1 mm for CBCT). The anthropomorphic phantom images were also used to study the influence of the correction scheme on dose calculation accuracy for sacral, thoracic and cervical spine treatments. The look up tables used in the TPS for the conversion of the CBCT 34

54 numbers into relative electron density were derived from the CBCT datasets of the body and head phantoms. The dose calculation was performed over a volume of 13x13x13 cm 3 for the pelvis and thorax and 8x8x8 cm 3 for the neck. Three-dimensional gamma analysis 23, 24 was used to estimate the differences between the heterogeneity corrected dose distributions calculated alternatively on CBCT and helical CT images for two and four beam plans. Dose distributions calculated with heterogeneity correction on helical CT images were considered as the reference Process Timing, and Geometric and Dosimetric Performance The final section of the study assessed the duration of the on-line treatment strategy, its dosimetric accuracy and the robustness of the system in a comprehensive test performed on a custom-built spine phantom. The flow chart shown in Fig. 2-1a describes the steps of the online strategy applied to the phantom. Additional steps such as patient intra-fraction management, which might be required for clinical application, are shown in grey. The 30 cm diameter acrylic cylinder used for these measurements was filled with water (Fig. 2-1 b and c). A 5.5 cm diameter cylindrical insert made of a core of Delrin (soft bone, ρ = 1.43 g/cm 3 ) covered with a 2 mm sheet of Teflon (hard bone, ρ = 2.15 g/cm 3 ) simulated a spine. Grooves were made in the Teflon and Delrin to represent the inter-vertebral spaces. The timing consistency was tested by repeating the on-line treatment process 5 times (2 different operators). The middle vertebra of the spine-like insert was chosen as the target and a 3-beam plan, composed of 2 lateral and 1 posterior beams, was designed on-line. Treatment planning scripting was used to streamline the planning process (version 7.6c, Pinnacle 3, Philips Medical Systems, Andover, MA). The scripts supported three different beam arrangements (parallel 35

55 opposed beams, wedged pair and 3 fields). The rectangular field sizes were selected to conform to the target seen on the lateral and posterior DRRs. For each treatment, the phantom was positioned differently with respect to the isocenter, which required the operator to design new fields each time. The treatment dose delivered to the phantom was measured by inserting radiochromic films (International Specialty Products, Wayne, NJ) (2 sagittals and 1 transverse) on the axis of the spine-like insert. Dose measurements were performed in 3 of the 5 sessions. The radiochromic films were scanned with a flatbed scanner 25 (Epson Expression 1680) with a resolution of 0.3 mm and converted to absolute dose using a calibration curve acquired during the same session. The measured dose was compared to the dose map calculated on the same plane with the planning system using a 2D gamma analysis performed with a film dosimetry software (FilmQA, 3Cognition LLC, Port Jefferson, NY). Three small brass markers glued on each film and visible on CBCT images were used to register the measured and calculated dose maps. 36

56 a Online treatment strategy Patient Setup At mid-line and mid-plane CBCT Acquisition and Reconstruction to TPS Online Planning. - Target Definition - Beam Arrangement - Dose Calculation (Inhomogeneous) External Contour Tracking Plan Review Secondary MU Calculation TPS to R&V Setup within Tolerance? No Couch Shift or CBCT #2 Yes Treatment Delivery b c Figure 2-1. Flow chart for the on-line radiotherapy strategy of spine metastases (a). The steps shown in grey were not performed on phantoms but will be part of the clinical implementation of the on-line treatment strategy. Phantom used to assess the duration and dosimetric accuracy of the on-line treatment strategy. Three radiochromic films were inserted in the spine-like insert made of white Teflon (hard bone) and black Delrin (soft bones) (b). The spine-simulating insert was disassembled in (c). 37

57 2.4 Results Image Processing and Impact on Dose Calculation The non-linear corrections shown in Fig. 2-2 minimize the cupping artifact on the body phantom images acquired with FOV z of 0.8, 10 and 20 cm respectively and a constant FOV t of 40 cm. The correction curves are concave in shape and deviate monotonically from the scatter and beam hardening free conditions with the increase of water thickness and FOV z. The correction curves for the head phantom presented the same characteristics and are not shown here for clarity. For the 0.8 cm FOV z, the scatter was reduced by the narrow collimation in the SI direction and beam-hardening effect became more significant. Even for this relatively small FOV z, typically found in conventional CT scanner, the scatter contribution should be managed with a postpatient collimator such as those installed on helical CT scanners 26. The non-negligible scatter contribution for the 0.8 cm-fov z scan was confirmed by comparing the uniformity of body phantom images obtained with and without the grid. The t cup value for a horizontal profile passing through the center of the body phantom decreased from 11.5 to 8.7% with the use of the scatter rejection grid. The differences between the correction curves obtained for the FOV z of 0.8, 10 and 20 cm suggests that the scatter contribution increased rapidly between the first two fields-of-view and seemed almost to saturate for FOV z between 10 and 20 cm. These results confirmed also that the FOV z should be kept below 10 cm to significantly reduce scattering 20. For large FOV z, the scatter contribution from the distal part of the field to the central slice is limited due to the longer path length in the scanned object, the larger scatter angle required to reach the central slice, and the higher probability of multiple scattering and absorption by photoelectric effect. 38

58 Measured attenuation ( μx) FOVz = 0.8 cm Ideal (no scatter and no beam hardening) FOVz = 20 cm FOVz = 10 cm Thickness of water equivalent (cm) Figure 2-2. Correction curves as a function of thickness of water equivalent material for scans of the body phantom acquired with the scatter rejection grid in place. These curves were optimized for different longitudinal fields-of-view (FOV z ) of 0.8, 10 and 20 cm in the superior to inferior direction. The field-of-view in the transverse direction (FOV t ) was held constant at 40 cm. The ideal linear relationship (no scatter and no beam hardening) between measured attenuation μx and the thickness of water is also shown. 39

59 The effect of the combine hardware and software correction on CBCT images of the body phantom acquired with full scatter condition (FOV z = 20 cm and FOV t = 40 cm) are shown in Fig. 2-3 with a normalized window and level. The CBCT images obtained without correction, with the grid only and with both software and hardware corrections were compared to the result obtained with a helical CT scanner. Horizontal profiles passing through the center of the phantom were extracted before image normalization (Fig. 2-3e). The use of the grid and the non-linear correction reduced the CBCT image non-uniformity. The t cup value fell from 30% to 22.5% with the presence of the grid. The software component of the correction scheme made a larger contribution in reducing the image non-uniformity than the hardware component, and reduced the cupping artifact to 4.5%. The correction scheme did not however eliminate the streaking pattern visible around the high-density inserts, which was due to local beam hardening effects and scatter. The profile for the corrected CBCT image also demonstrated an improvement in CBCT number accuracy and an increase in image contrast (e.g. soft bone insert, Fig. 2-3e). This is illustrated better in the graph of the CBCT numbers as function of the helical CT numbers for the density inserts of the body phantom (Fig. 2-4). The largest discrepancies between corrected CBCT and helical CT numbers were observed for the low and the high density inserts and reached values as large as 130 HU. It is interesting to note that the use of the body phantom determined correction for the head phantom dataset (same field-of-view) did not have a substantial effect on the CBCT number accuracy (see head phantom corrected curve in Fig. 2-4). The image uniformity (t cup before correction = 11.5%) however was not reduced as effectively with the body phantom correction (t cup = 6%) as with the non-linear correction optimized for the head phantom (t cup = 40

60 3.5%). The contrast resolution for the CBCT images was assessed using the breast insert (nominally -40 HU). For the corrected CBCT images presented in Fig. 2-3c, the relative contrast (100 (μ breast μ water )/μ water ) increased from 2.3% to 3.4%. The CNR however remained constant at 1.4. This is attributed to the noise being amplified by the software component of the correction scheme and the presence of the grid attenuating some of the primary fluence. Similar trends were observed for the head phantom images. 41

61 a b c d e Helical CT CBCT grid, corrected CT/CBCT numbers CBCT grid, uncorrected Position (mm) CBCT no grid, uncorrected Figure 2-3. CBCT images of a RMI CT calibration phantom acquired (a) uncorrected, (b) with the 10:1 scatter-rejection grid but without non-linear correction, and (c) with grid and non-linear correction. The uncorrected image obtained without grid (a) represents a good example of cupping artifact with the center of the image darker than the edges. The image in (d) was acquired on a state of the art helical CT. The CBCT scans were all acquired with a transverse and SI fields-of-view of 40 and 20 cm, respectively. All images are presented with the same normalized window and level. The ROIs used for the optimization of the non-linear correction are shown in 3c. Horizontal profiles going through the center of the phantom (see dash line on d) are presented for the four images before normalization (e). 42

62 Head, corrected 600 CBCT numbers Body, corrected Body, uncorrected -800 Helical CT numbers Figure 2-4. Average CBCT numbers as a function of the helical CT numbers for the density inserts of the head and the body phantom. The head phantom images were corrected using the curve optimized for the body. The un-corrected results for the body phantom are also shown. The 10:1 grid was used for all scans. 43

63 The joint histograms of CBCT vs. helical CT numbers before and after image correction (grid and body phantom correction curve) are shown in Fig. 2-5 for the shoulders and neck of an anthropomorphic phantom. The histograms were rescaled (grey-scale value) by calculating the logarithm of the pixel numbers. Note that the CBCT and helical CT number scales were shifted by a constant of one thousand to eliminate negative values for display purposes. The peaks on the histograms represented anatomic structures such as the tip of the lungs, soft tissue and bones. After correction, the CBCT number distribution was clustered along the line of identity, demonstrating a reduction in image non-uniformity and an improvement in CBCT number accuracy. The remaining spread of the distribution after correction represents the residual variations in CBCT numbers. These residuals include any small registration errors and differences in artifacts between the CBCT and helical CT images. 44

64 a b Figure 2-5. Joint histogram of CBCT vs. helical CT numbers for a scan of the shoulders and the neck of an anthropomorphic phantom (a) uncorrected, and (b) corrected using the 10:1 grid and the nonlinear correction. The grey scale represents the logarithm in base 10 of the number of pixels. 45

65 Dose distributions for 2 or 4 field treatment plans, calculated alternatively on the registered CBCT and helical CT images of the anthropomorphic phantom, were compared using a 3D gamma analysis. The CT to density conversion tables used in the TPS for corrected and uncorrected CBCT data were derived from Fig. 2-4 by averaging the head and the body phantom results to be representative for both large and small sections of the body. For sacral and cervical spine treatment plans, more than 99% of the dose voxels calculated on corrected CBCT images agreed within 2% of the dose difference or 3 mm of distance to agreement with the dose calculated on the helical CT. The distance to agreement tolerance was dictated mainly by the calculation grid resolution of 2 mm. In the case of the thoracic spine, for which portions of lung were included in the field, the agreement between corrected CBCT and CT was 93.4% for 2%/3mm and reached 99.4% for 3%/3mm. For the uncorrected CBCT images of the 3 sites, 99% of the dose voxels calculated on CBCT agreed with the dose obtained on helical CT images within 5% to 7% of dose difference and 3 mm of distance to agreement. Although the uncorrected CBCT images can be highly non-uniform (e.g. pelvis), only 3% to 5% of additional dose errors were introduced by these artifacts due to their symmetric and smooth nature and the insensitivity of dose calculation to CT number errors 27. Corrected and uncorrected CBCT images of sacral and thoracolumbar spine patients are shown in Fig. 2-6 with a normalized window and level along with the helical CT images obtained for the same patients. For the sacral spine patient, the CBCT image corrected with the grid and the non-linear correction (Fig. 2-6b) presents a good uniformity and an image contrast, which is approaching the helical CT results (Fig. 2-6c). The corrected CBCT image is, however, qualitatively noisier than the uncorrected image. Residual cupping artifact in the rectum region 46

66 and shading artifacts due to detector lag 28 are however still visible (anterior to left femoral head and in the right gluteus muscle). The thoracolumbar spine patient was treated for a lesion in the T12 vertebra. The difference between the CBCT images before and after correction (Fig. 2-6d and e) is less striking than for the pelvis patient as the grid was used for both datasets. The nonlinear correction improved the bone to soft-tissue contrast and reduced the cupping artifact visible in the anterior portion of the abdomen (Fig. 2-6e). 47

67 Pat #1 a b c Pat #2 d e f Figure 2-6. Comparison of CBCT and helical CT images of sacral (pat #1) and thoracolumbar (pat #2) spine patients. For patient 1, the CBCT image (a) was acquired without the grid and no correction was applied. The grid and the non-linear correction were used for the image shown in (b). The corresponding helical CT image is shown in (c). The CBCT images of patient #2 with and without non-linear correction (d and e, respectively) were both acquired with the grid. The corresponding helical CT image is shown in (f). All CBCT and helical CT images are presented with a normalized window and level. The voxel size for the CBCT and helical CT scans was 1 mm 3 and 0.81 x 0.81 x 2 mm, respectively. 48

68 2.4.2 Process Timing, and Geometric and Dosimetric Performance The duration of the entire on-line treatment strategy performed on the cylindrical phantom with the spine-like insert took on average 23.1 min for the five measurement sessions (range of 22.7 to 26.8 min). The average timing for the individual steps is presented in Table 2-1. The on-line planning was the longest task with 6.4 minutes followed by the dose delivery (8 Gy) and the image reconstruction with 4.7 and 4.0 min, respectively. The radiochromic films exposed during the application of the on-line treatment procedure were compared offline to the TPS results using 2D gamma analysis. For the 3 measurement sessions, 97% to 99% of the pixels of the films (1 transverse and 2 sagittal) agreed with the calculated dose maps within 5% of dose difference or 2 mm of distance to agreement. The film scanner geometric distortion was measured using a grid template and was equal to 0.3 mm or less over 10 cm. The accuracy of the film to dose map registration was limited mainly by the resolution of the calculated dose map (1.5mm) and was within 1 mm. Procedures Phantom Setup (aligned with lasers) Cone-beam CT Acquisition and Processing Cone-beam CT Reconstruction Transfer to Planning System On-line Planning (Outlining, beam arrangement and plan evaluation) Transfer Plan to Record and Verify Treatment Delivery (8Gy) Total Time 3.0 min 3.3 min 4.0 min 1.2 min 6.4 min 0.5 min 4.7 min* 23.1 min *A dose delivery of 20Gy with the same beam arrangement would require for example 9.3 min. Table 2-1. Average duration of the on-line treatment strategy performed on phantom. 49

69 2.5 Discussion The implementation of the on-line treatment strategy for patients with bone metastases of the spine requires CBCT images suitable for target definition and treatment planning. It has been shown that local tomography (reduced FOV in both transverse and SI directions) can improve CBCT image uniformity 11. This approach however was not suitable for the on-line treatment strategy, which required a large FOV in both directions to identify the target while also capturing the skin line for dose calculation. In this work, CBCT image uniformity and CBCT number accuracy were significantly improved using a correction scheme that employs a grid and a non-linear software-based attenuation correction. The non-linear correction is signal-based and does not take into account the composition or the shape of the subject to be imaged. Its shape was optimized for two phantom diameters and three FOV z. In practice, the correction obtained for the body phantom is general enough for most anatomic sites and produced results adequate for our purpose even for geometries as complex as the shoulders (including lungs) and neck, as demonstrated in the study of the anthropomorphic phantom. Based on these encouraging results, the initial approach of reducing the scatter via the use of the grid is being re-examined and studies have been initiated to explore the performance of the software correction on its own. The use of the grid is an effective way to reject some of the scatter but serves also to attenuate up to 35% of the primary beam, which can result in an increase in image noise for the same dose to the patient 16. Further, more complex corrections to reduce localized artifacts, such as the white and dark streaks around the high-density inserts in Fig. 2-3c, are also under investigation. 50

70 In addition to improving CBCT image uniformity and CBCT number accuracy, the image corrections implemented in this work also allowed for dose calculation on CBCT images with reasonable accuracy (usually within 2%/3mm). The overall dosimetric accuracy of the on-line treatment strategy was assessed on a phantom and satisfied dose tolerances accepted in conventional 3D conformal radiation therapy Bone-type structures of interest could be identified on corrected CBCT images and covered with treatment beams with a geometric accuracy of 2 mm or less as demonstrated by the agreement between measured and calculated field penumbra for the three-beam plan delivered during the comprehensive test. The dose tolerances mentioned above for external beam radiotherapy treatment are derived generally from the summation of individual error contributions related to treatment delivery, dose calculation and patient setup and organ motion. In the case of this phantom study, setup error and organ motion are non-existent. Measurement uncertainty and possible registration errors between film and calculated dose map represented however additional sources of uncertainties, which also contribute to the discrepancies observed between measured and calculated doses. Based on the application of the complete on-line treatment strategy on phantom, a duration of about 25 min for the clinical application of the on-line process can be assumed. This time does not include however the patient displacements in and out of the room. The use of a concomitant acquisition and reconstruction algorithm and a faster flat panel, both available on the Elekta commercial system, will reduce the imaging time by about 3 to 4 minutes and help us decrease the current process duration. Aside from the duration, the CBCT image quality for target delineation represents another potential limiting factor in the clinical implementation of the online treatment method. The corrected CBCT images of the anthropomorphic phantom and of 51

71 the patients enrolled in our study presented clear bony anatomy definition. Soft tissue extensions of the disease around the bony structures represent a challenge for CBCT imaging. The influence of the imaging modality (CBCT vs. CT) on target delineation especially for patients presenting bone metastases with soft tissue extensions is underway in our department and will be presented in a subsequent report. With its application at the treatment unit, the on-line treatment strategy is free of the setup errors potentially introduced in the conventional treatment method by patient displacement between machines and multiple setups. Besides the limited mechanical accuracy of the linear accelerator, intra-fraction motion thus represents the main source of setup error for the on-line process. Cervical metastases patients might be more prone to intra-fraction motion due to the 33, 34 high degree of flexibility of the neck. As shown in Fig. 2-1a, an optical tracking system (e.g. infra-red cameras) will be used to survey intra-fraction motion. The adequacy of the external contour as a surrogate for spine position is already under investigation as part of the REB approved protocol on the clinical feasibility of the on-line treatment strategy. The fast acquisition of a low-quality CBCT scan prior to treatment is also included in this protocol to validate the patient position 12 and would add only few minutes to the process. The on-line planning and delivery technique, if performed in a reasonable time, has the potential to reduce patient discomfort, increase department efficiency and consequently improve patient access to palliative radiation therapy treatments. A timing study of 45 patients is underway to estimate the duration of the on-line planning and delivery process in clinical environment. Preliminary results for 8 spine patients demonstrate that online planning (2-beam arrangement) 52

72 can be performed in 10 min on average. The clinical deployment of the on-line treatment strategy will be followed by an accurate cost-benefit analysis of this novel on-line process. The improvements in image quality presented in this work will also allow us to study the efficacy of model-based auto-segmentation algorithm 35 (Philips Medical Systems, Andover, MA) in a second phase of development of the online treatment strategy. If fast and reliable, this tool, in conjunction with careful management of intra-fraction motion, could allow the online design of 3D conformal plans for metastases of the spine. 2.6 Conclusion In this work, the feasibility of an on-line treatment strategy for patients with spinal metastases who require treatments for symptom palliation was demonstrated through a concerted effort involving image quality, system integration, process timing, and geometric and dosimetric performance. This treatment method was developed on a linear accelerator capable of conebeam CT acquisition and required a close integration of the imaging, planning and delivery systems. The CBCT images were made suitable for planning with the implementation of a hardware and software image correction scheme, which improved image uniformity and CBCT number accuracy. The on-line planning process for simple plans was streamlined with scripted processes in the TPS. The overall dosimetric and geometric robustness of this new treatment method was verified using a simplified anthropomorphic spine phantom and was found to satisfy accepted standards in conventional external beam RT. The clinical implementation of the on-line planning and delivery technique will be performed in the last phase of our REB protocol pending the results of target delineation and intra-fraction motion studies. 53

73 2.7 Acknowledgement The authors would like to thank Anita Varma, Elizabeth White, Fanny Sie, Harald Keller, Michael Kaus, Jean-Pierre Bissonnette, Gavin Disney and Stuart Rose (Princess Margaret Hospital) for their support and helpful discussions. 54

74 2.8 References 1. D. Hoegler, "Radiotherapy for palliation of symptoms in incurable cancer," Curr Probl Cancer. 21, (1997). 2. J. Huang, S. Zhou, P. Groome, S. Tyldesley, J. Zhang-Solomans, W. J. Mackillop, "Factors affecting the use of palliative radiotherapy in Ontario," J Clin Oncol. 19, (2001). 3. C. Lindholm, E. Cavallin-Stahl, J. Ceberg, J. E. Frodin, B. Littbrand, T. R. Moller, "Radiotherapy practices in Sweden compared to the scientific evidence," Acta Oncol. 42, (2003). 4. W. J. Mackillop, S. Zhou, P. Groome, P. Dixon, B. J. Cummings, C. Hayter, L. Paszat, "Changes in the use of radiotherapy in Ontario ," Int J Radiat Oncol Biol Phys. 44, (1999). 5. J. S. Welsh, R. R. Patel, M. A. Ritter, P. M. Harari, T. R. Mackie, M. P. Mehta, "Helical tomotherapy: an innovative technology and approach to radiation therapy," Technol Cancer Res Treat. 1, (2002). 6. M. Uematsu, A. Shioda, K. Tahara, T. Fukui, F. Yamamoto, G. Tsumatori, Y. Ozeki, T. Aoki, M. Watanabe, S. Kusano, "Focal, high dose, and fractionated modified stereotactic radiation therapy for lung carcinoma patients: a preliminary experience," Cancer. 82, (1998). 7. D. A. Jaffray, D. G. Drake, M. Moreau, A. A. Martinez, J. W. Wong, "A radiographic and tomographic imaging system integrated into a medical linear accelerator for localization of bone and soft-tissue targets," Int J Radiat Oncol Biol Phys. 45, (1999). 8. A. S. Shiu, E. L. Chang, J. S. Ye, M. Lii, L. D. Rhines, E. Mendel, J. Weinberg, S. Singh, M. H. Maor, R. Mohan, J. D. Cox, "Near simultaneous computed tomography image-guided stereotactic spinal radiotherapy: an emerging paradigm for achieving true stereotaxy," Int J Radiat Oncol Biol Phys. 57, (2003). 9. D. A. Jaffray, J. H. Siewerdsen, "Cone-beam computed tomography with a flat-panel imager: initial performance characterization," Med Phys. 27, (2000). 10. D. A. Jaffray, J. H. Siewerdsen, J. W. Wong, A. A. Martinez, "Flat-panel cone-beam computed tomography for image-guided radiation therapy," Int J Radiat Oncol Biol Phys. 53, (2002). 55

75 11. D. Letourneau, J. W. Wong, M. Oldham, M. Gulam, L. Watt, D. A. Jaffray, J. H. Siewerdsen, A. A. Martinez, "Cone-beam-CT guided radiation therapy: technical implementation," Radiother Oncol. 75, (2005). 12. J. R. Sykes, A. Amer, J. Czajka, C. J. Moore, "A feasibility study for image guided radiotherapy using low dose, high speed, cone beam X-ray volumetric imaging," Radiother Oncol. 77, (2005). 13. L. A. Feldkamp, Davis, L.C., Kress, J.W., "Practical Cone-Beam Algorithm," J. Opt. Soc. Am. A. 1, (1984). 14. D. J. Moseley, M. B. Sharpe, J. H. Siewerdsen, G. A. Wilson, S. M. Ansell, T. A. Haycocks, T. G. Purdie, M. Islam, D. A. Jaffray. Design and Implementation of a Cone- Beam CT Image-Guidance System for High-Precision Radiotherapy. In Proceedings of the XIVith International Conference on the Use of Computers in Radiation Therapy. Seoul, South Korea: Jeong Publishing; pp M. B. Sharpe, D. J. Moseley, T. G. Purdie, M. Islam, J. H. Siewerdsen, D. A. Jaffray, "The stability of mechanical calibration for a kv cone beam computed tomography system integrated with linear accelerator," Medical Physics. 33, (2006). 16. J. H. Siewerdsen, D. J. Moseley, B. Bakhtiar, S. Richard, D. A. Jaffray, "The influence of antiscatter grids on soft-tissue detectability in cone-beam computed tomography with flat-panel detectors," Med Phys. 31, (2004). 17. P. K. Kijewski, B. E. Bjarngard, "Correction for beam hardening in computed tomography," Med Phys. 5, (1978). 18. J. Hsieh, R. C. Molthen, C. A. Dawson, R. H. Johnson, "An iterative approach to the beam hardening correction in cone beam CT," Med Phys. 27, (2000). 19. J. C. Lagarias, J. A. Reeds, M. H. Wright, P. E. Wright, "Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions," SIAM J. Opt. 9, (1998). 20. J. H. Siewerdsen, D. A. Jaffray, "Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter," Med Phys. 28, (2001). 21. C. B. Chiarot, J. H. Siewerdsen, T. Haycocks, D. J. Moseley, D. A. Jaffray, "An innovative phantom for quantitative and qualitative investigation of advanced x-ray imaging technologies," Phys Med Biol. 50, N (2005). 22. J. P. Pluim, J. B. Maintz, M. A. Viergever, "Mutual-information-based registration of medical images: a survey," IEEE Trans Med Imaging. 22, (2003). 23. D. A. Low, W. B. Harms, S. Mutic, J. A. Purdy, "A technique for the quantitative evaluation of dose distributions," Med Phys. 25, (1998). 56

76 24. D. A. Low, J. F. Dempsey, "Evaluation of the gamma dose distribution comparison method," Med Phys. 30, (2003). 25. S. Devic, J. Seuntjens, G. Hegyi, E. B. Podgorsak, C. G. Soares, A. S. Kirov, I. Ali, J. F. Williamson, A. Elizondo, "Dosimetric properties of improved GafChromic films for seven different digitizers," Med Phys. 31, (2004). 26. J. Hsieh. Computed Tomography: Principles, Design, Artifacts, and Recent Advances. First Edition ed. Bellingham: SPIE - The International Society for Optical Engineering; AAPM. Tissue Inhomogeneity Corrections for Megavoltage Photon Beams, Report of Task Group No. 65. Madison, WI: American Association of Physicists in Medicine; J. H. Siewerdsen, D. A. Jaffray, "Cone-beam computed tomography with a flat-panel imager: effects of image lag," Med Phys. 26, (1999). 29. ICRU. Determination of Absorbed Dose in a Patient Irradiated by Beams pf X or Gamma Rays in Radiotherapy Procedures, Report No. 24. Washington, DC: International Commission on Radiation Units and Measurements; IAEA. Absorbed Dose Determination in Photon and Electron Beams. An International Code of Practice, Report Series No Vienna: International Atomic Energy Agency; J. Van Dyk, R. B. Barnett, J. E. Cygler, P. C. Shragge, "Commissioning and quality assurance of treatment planning computers," Int J Radiat Oncol Biol Phys. 26, (1993). 32. J. Venselaar, H. Welleweerd, B. Mijnheer, "Tolerances for the accuracy of photon beam dose calculations of treatment planning systems," Radiother Oncol. 60, (2001). 33. F. J. Bova, J. M. Buatti, W. A. Friedman, W. M. Mendenhall, C. C. Yang, C. Liu, "The University of Florida frameless high-precision stereotactic radiotherapy system," Int J Radiat Oncol Biol Phys. 38, (1997). 34. G. Baroni, G. Ferrigno, R. Orecchia, A. Pedotti, "Real-time three-dimensional motion analysis for patient positioning verification," Radiother Oncol. 54, (2000). 35. V. Pekar, T. R. McNutt, M. R. Kaus, "Automated model-based organ delineation for radiotherapy planning in prostatic region," Int J Radiat Oncol Biol Phys. 60, (2004). 57

77 Chapter 3 INTEGRAL TEST PHANTOM FOR DOSIMETRIC QUALITY ASSURANCE OF IMAGE GUIDED AND INTENSITY MODULATED STEREOTACTIC RADIOTHERAPY Daniel Létourneau 1, M.Sc. Harald Keller 1,2, Ph.D. Michael B Sharpe 1,2, Ph.D. David A Jaffray 1,2,3, Ph.D. 1 Radiation Medicine Program, Princess Margaret Hospital, Toronto, ON, Canada 2 Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada 3 Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada This chapter was published in the Med. Phys. 34, p (2007) 58

78 3.1 Abstract The objective of this work is to develop a dosimetric phantom quality assurance (QA) of linear accelerators capable of cone-beam CT (CBCT) image guided and intensity-modulated radiotherapy (IG-IMRT). This phantom is to be used in an integral test to quantify in real-time both the performance of the image guidance and the dose delivery systems in terms of dose localization. The prototype IG-IMRT QA phantom consisted of a cylindrical imaging phantom (CatPhan) combined with an array of 11 radiation diodes mounted on a 10 cm diameter disk, oriented perpendicular to the phantom axis. Basic diode response characterization was performed for 6 and 18 MV photons. The diode response was compared to planning system calculations in the open and penumbrae regions of simple and complex beam arrangements. The clinical use of the QA phantom was illustrated in an integral test of an IG-IMRT treatment designed for a clinical spinal radiosurgery case. The sensitivity of the phantom to multi-leaf collimator (MLC) calibration and setup errors in the clinical setting was assessed by introducing errors in the IMRT plan or by displacing the phantom. The diodes offered good response linearity and long-term reproducibility for both 6 an 18 MV. Axial dosimetry of coplanar beams (in a plane containing the beam axes) was made possible with the nearly isoplanatic response of the diodes over 360 of gantry (usually within ±1%). For single beam geometry, errors in phantom placement as small as 0.5 mm could be accurately detected (in gradient 1%/mm). In clinical setting, MLC systematic errors of 1 mm on a single MLC bank introduced in the IMRT plan were easily detectable with the QA phantom. The QA phantom demonstrated also sufficient sensitivity for the detection of setup errors as small as 1 mm for the IMRT delivery. These results demonstrated that the prototype can accurately and efficiently verify the entire IG- 59

79 IMRT process. This tool, in conjunction with image guidance capabilities has the potential to streamline this QA process and improve the level of performance of image-guided and intensitymodulated radiotherapy. 60

80 3.2 Introduction Linear accelerators capable of kilovoltage cone-beam CT (CBCT) imaging 1 and intensity modulated dose delivery are used in radiotherapy to improve treatment guidance while achieving highly conformal dose distributions. In an online image guidance model, 2-8 patient images acquired in the treatment position are registered to the reference planning CT to determine the patient setup error. The patient position is then corrected by translating the couch accordingly before delivering the treatment. The uncertainty of the CBCT image guidance process pertains to the image quality (contrast, scale, orientation, etc), the CT to CBCT registration accuracy, the treatment and imaging isocenter calibration and the couch mechanical accuracy. The accuracy of CBCT image guided radiotherapy (IGRT) was shown to be within 1mm for unambiguous objects using detection of known phantom displacements 2 and imagebased residual error assessment Although, these results are exciting, it is the combined performance of imaging and dose delivery systems, which ultimately determines the delivery accuracy in terms of the amount of dose and its position. In the context of intensity modulated radiotherapy (IMRT), simple field shape and position validation under image guidance is important but does not provide full quality assurance (QA) of the dose delivery process. 3D imaging allows the dosimeters themselves to be localized with respect to the isocenter, giving combined geometric and dosimetric verification. For example, the combination of CBCT imaging and radiochromic films has been used in this context to assess the overall dosimetric accuracy of palliative radiotherapy treatments planned on-line

81 The objective of this work is to develop a dosimetric phantom for QA of linear accelerators capable of CBCT image guided and intensity modulated radiotherapy (IG-IMRT). This phantom is to be used in an integral test to quantify both the performance of the image guidance and the dose delivery systems in terms of dose localization. A prototype of an IG-IMRT QA phantom was inspired by the experience of Rowbottom et al 12 and uses diodes instead of MOSFET in a reusable cylindrical phantom. It was designed for quality assurance of coplanar radiosurgery-type fields. Real-time point-dose measurements performed in the integral test will be compared to planned dose in a pass/fail analysis 13, 14 to verify the entire treatment process. In this work, the prototype IG-IMRT QA phantom is described and its response is characterized for simple and complex coplanar irradiation conditions. In addition, the sensitivity of the phantom to setup and multi-leaf collimator (MLC) calibration errors is assessed with clinical radiosurgery IMRT distributions. 3.3 Methods and Materials System Description The prototype IG-IMRT QA phantom is shown in Figure 3-1. Eleven n-type diodes (bare dies of 0.8 x 0.8 mm 2 ) were mounted using a wire bounding technique on a circular 10 cm diameter printed circuit board (PCB). The diodes and their connections were sandwiched between two other identical fibreglass PCB disks. During manufacturing, the amount of high-z material surrounding the dies was kept to a minimum in an attempt to limit the directional dependence of the detectors 15. The diodes were arranged on concentric circles of 1.5 to 3.5 cm of radius and spanned the surface of the disk (see Fig 3-1b). The distribution of the diodes was dictated by the 62

82 available mounting positions on the circular PCB. According to the manufacturer (Sun Nuclear Corporation, Melbourne, FL), each diode was radiation hardened and exhibits a nominal sensitivity degradation of about 0.5%/kGy delivered with a 6 MV photon beam. The triple PCB assembly of 5.5 mm thickness was inserted into the homogeneous section of a commercial cylindrical CT phantom (Catphan 500, Phantom Laboratories, Salem, NY). This 20 cm diameter phantom contains other imaging inserts for image analysis. A copper shielded flat cable was soldered to the edge of the PCB and conducted the diode signals to a 12-channel analog-to-digital electrometer developed for an in-vivo dosimetry system (Sun Nuclear Corporation, Melbourne, FL). The electrometer system measures the diode leakage current before each measurement and subtracts it automatically from the total diode reading. No temperature correction is performed. Preliminary studies of an individual diode inserted on the central axis of a 10 cm acrylic cylinder was easily visible on CBCT images (Fig. 3-1c). The visibility of the diodes in the phantom was more challenging even on the helical CT due in particular to the higher density of the fibreglass material (ρ 1.8 g/cm 3 ) surrounding the detectors. For planning purposes, the metal solder used in the electrical connections to the diode provided contrast to determine the position of the detectors in the phantom. 63

83 Figure 3-1. a): Prototype of IG-IMRT QA phantom. The construction details of the circular diode array are shown in b). c): An individual diode embedded in a 10 cm diameter cylindrical phantom made of acrylic is clearly visible on an axial CBCT image. 64

84 In order to characterize their relative response, the diodes were calibrated in a flat calibration phantom to take into account the diode variation in radiation sensitivity. The disk assembly was placed horizontally in a solid water phantom at 10 cm depth with adequate backscatter material and was irradiated (6 and 18 MV, gantry = 0 ) 16. The exposure was performed with a 20 x 20 cm 2 field size and a source-detector distance of 145 cm to ensure beam flatness at the diode plane. The measured response provided a calibration factor for each diode and these were saved in the memory of the electrometer. The calibration in the flat phantom was performed once at the beginning of the 3-month period required to complete the experimental work in this project. The dose measurements presented in the following sections are relative as the diodes were not cross-calibrated against a reference ion chamber System Characterization System characterization was performed with the diode array oriented vertically within the Catphan 500 housing. All measurements presented in this section were acquired after setting up the phantom using the CBCT image guidance process 10. In this procedure, the phantom was first mounted at the end of the treatment unit couch (Synergy, Elekta, Crawley, UK) with the long axis parallel to the axis of rotation of the gantry and was levelled with the room lasers. A CBCT scan was then acquired and transferred to a commercial planning system (Pinnacle 3, Philips Medical Systems, Madison, WI) for manual registration with the reference planning CT of the phantom (Discovery ST 16 slice, GE Healthcare, Milwaukee, WI). The disk assembly itself and the four air holes in it (Fig 3-1b) were used as fiducial markers for the CBCT to CT registration. The image registration accuracy for this unambiguous object was limited by the 65

85 image voxel size (1mm for CBCT). Care was taken to eliminate any rotation in the phantom position during the levelling process and this was confirmed during the registration. Once the setup correction was determined by registration, the phantom position was corrected by translating the couch, accordingly. An infrared camera system (Polaris, Northern Digital Inc, Ontario, Canada) was used to verify that the couch displacements were applied as intended and to assure that the phantom was not accidentally displaced during the remainder of the experiment. The accuracy of the camera system measurements was evaluated to ±0.1 mm for linear displacements of 1 to 10 mm by mounting infrared reflectors on a motorized micrometer stage (Parker Automation, Hollis, NH) and by comparing the camera measurements to the stage shifts (resolution of 30 μm). The short-term reproducibility of the diode response was assessed by calculating over a measurement session the standard deviation (SD) of 16 consecutive readings for each diode. Photon beams of 6 and 18 MV at a field size of 20 x 20 cm 2 were used to deliver 100 monitor units (MU) per reading. The planning isocenter was located on the axis of the cylindrical phantom at 2 cm from the diode plane in the superior to inferior direction (SI). This experiment was repeated 5 times over a one-month period to assess the long-term reproducibility of the phantom response. For a given diode, the average response variation within a given session (short-term or intra-session variation) was estimated as the RMS value of the SD of all sessions. The inter-session variation was estimated by calculating the SD of the session mean values. A conservative estimate of the long-term reproducibility was calculated by adding the intra and inter-variation in quadrature. 66

86 The dose response of the phantom was evaluated using the same setup and irradiation parameters as for the reproducibility analysis described above with the exception that the diodes were in the plane of rotation of the source. To test for linearity, beams of 1 to 500 MU were delivered with a maximum isocentric dose of 3.75 and 4.35 Gy for 6 and 18 MV, respectively. The directional dependence of the phantom s central diode was assessed over 360 of gantry angle. A small field of 3 x 3 cm 2 (6 and 18 MV) was selected in this case to limit the smoothing effect of the phantom scatter and evaluate potential interference from neighbouring diodes. For comparison, the directional dependence of a single diode embedded in a 10 cm diameter cylindrical phantom made of acrylic was also evaluated under the same conditions. The agreement between the phantom measurements and the calculations of the planning system at the diode locations was assessed for a simple one-beam plan in terms of dose-difference (ΔD). The measurements acquired for the short-term reproducibility analysis were normalized with respect to the central diode readings and were used for the in-field comparison. Translation of a narrow posterior beam across the phantom in the right to left (RL) direction provided a method to sample the beam penumbra without moving the phantom. During the translation, the phantom was exposed 18 times with a 6 MV photon beam (5.1 x 20 cm 2 ), resulting in sampling points every 1 to 2 mm. These measurements were performed with the collimator at to use the focus jaws as the travelling field edges and were compared to dose from the planning system calculations. Once the penumbra sampling experiment completed, the sensitivity of the phantom to translation errors was assessed by displacing the phantom in the RL direction by known amounts (0.5, 1.2 and 2.3 mm) as measured by the infrared camera 67

87 system. For each shift, the phantom was irradiated with a static beam (5.1 x 20 cm 2 ) and the measured dose was overlaid on the graph obtained from the penumbra measurements with the moving field (before phantom shifts). The dose-based translations were then estimated by calculating the distance to agreement (DTA) 13, 17, 18 and were compared to the camera results Integral Testing and Sensitivity Analysis in the Clinical Setting. To illustrate the use of the QA phantom, the integral testing of an IG-IMRT treatment designed for a clinical spinal radiosurgery case was performed. This type of treatment was selected as a clinical application for the phantom prototype due to the small dimension of the target crosssection and the high precision required. In this integral test, once the image-guided setup was performed, the radiosurgery 7-beam IMRT plan (35 segments total) was delivered to the phantom using a 4 mm leaf width MLC (Elekta Synergy S). The planning isocenter was located 2 mm inferior to the diode plane in order to position the detectors in the high dose region and away from the interleaf leakage. The diode readings were converted into dose using an openfield calibration factor and were compared to the planning system results on an individual beam basis. For each beam, the number of measurement points which agreed with the plan within given tolerances of ΔD and DTA in 3D was determined 14. A 1-mm calculation grid was used in the planning system (Pinnacle 3, V7.6c, adaptive convolution algorithm) to provide sufficient resolution for the DTA analysis. The measured doses corresponding to <5% of the maximum dose value per beam were excluded from the comparison to eliminate the favourable bias associated with points located outside of the nominal fluence. The dose-difference tolerance was expressed as a function of the computed dose at a given diode location. For low dose 68

88 points, for which the relative dose-difference tolerance corresponded to prohibitively small absolute dose, a maximum ΔD of 1 cgy was used as an alternative passage criterion. The inclusion criterion and the tolerances are summarized in Table 3-1. Inclusion criterion for comparison: Tolerances: D 5% of max dose meas Dmeas Dplan % ΔD Dplan or D D 1 cgy meas plan or tolerance DTAmeas DTA tolerance Table 3-1. Inclusion criterion and tolerances for comparison between measured (D meas ) and planned (D plan ) doses at a diode location. The dose comparison was done on an individual beam basis. %ΔD tolerance and DTA tolerance represent the relative dose-difference tolerance and the distance to agreement tolerance, respectively. The sensitivity of the QA phantom to MLC and setup errors was assessed for the radiosurgery plan described above. Systematic MLC calibration errors were introduced in the plan by contracting or expanding the right, left and both sides of each MLC segment alternatively by a fixed distances (0.5, 1 and 2 mm). These systematic errors represent the worst-case scenario for MLC calibration. A total of 24 modified plans containing systematic MLC errors were produced and delivered to the phantom. The phantom sensitivity to MLC errors was characterized in terms of number of measurement points satisfying given tolerances. The same approach was used to estimate the phantom sensitivity to setup errors. In this case, the original 69

89 spinal radiosurgery plan was delivered to the phantom after shifting it in the RL direction by ±0.5 to ±2.5 mm in steps of 0.5 mm. 3.4 Results System Characterization The short-term response reproducibility (intra-session variation) of the phantom eleven diodes was equal to within 0.1% to 0.3% for a dose ranging from 58 to 95 cgy at the diode locations delivered with a 6 MV photon beam. The inter-session variation assessed over a one-month period (5 sessions) was larger than the intra-session variation and reached 0.7% to 1%. The long-term reproducibility of the diode response for any single measurement was estimated to be 1.1% by adding the intra and inter-session variation in quadrature. These variations included variations in machine dose rate, which was estimated to be about 0.5% from our monthly QA. Slightly better results were obtained for an 18 MV photon beam. The diodes exhibited good linearity over a range of 1 to 500 MU and demonstrated a slightly higher sensitivity for 6 MV than for 18 MV photons. The average slopes for the best linear fit for 6 and 18 MV were and counts/cgy respectively with both regression coefficients larger or equal to In comparison, the maximum diode response for the kilovoltage CBCT imaging dose was equal on average to 602 ± 17 counts (1 SD) for a 650- projection scan ( 0.8 mas/projection, 120 kvp). The diode response was proportional to the number of mas per projection and the total number of projections. The ratio between the inner 70

90 and outer diodes reached 1.2 to 1.3 and was consistent with the literature For all the following treatment dose measurements, the non-negligible imaging signal was not included in the cumulated doses. The directional response of the central diode as a function of the gantry angle for a 6 MV photon beam is shown in Figure 3-2a. These measurements were repeated 3 times within the same session and the error bars on the graph correspond to ± 1 SD. The anisotropy of the central diode response was usually within ± 1% over 360 of radiation incidence. The largest positive variations of 1.8% and 2.7% at 176 and 4 respectively were due in part to air gaps (around the cable and the fiducial holes) in the construction of the phantom. High-density material used for the flat cable connection on the left side of the disk assembly (Fig. 3-2b) also introduced additional attenuation at 120 and -76 (negative variations of 1.2 and 1.4%) 16. In comparison, the response of the single diode embedded in a homogeneous 10 cm diameter cylindrical phantom made of acrylic was ± 1% and confirmed the impact of the phantom construction on its directional response. For 18 MV photons, the phantom response (not shown) presented the same general shape as for 6 MV but was less sensitive to the construction details (variations within 0.9% to 1.4%). The measurements were not corrected for variations of linear accelerator dose rate as function of gantry angle. 71

91 Figure 3-2. a): Directional response of the central diode of the QA phantom as a function of gantry angle. The error bars on this graph are the same size as the symbols. The directional response of a single diode embedded in a 10 cm diameter cylindrical phantom made of acrylic is also shown. b): A CT slice of the phantom is shown to illustrate the air gaps and the high-density material at the flat cable connection. 72

92 The agreement between measured and planned doses was assessed in the open and penumbrae regions of a simple beam arrangement (gantry = 180 ) 16. For the open region, the difference between measured and calculated dose for a 20 x 20 cm 2 field ranged from 1.2% to 1% and 1.4% to 0.2% for 6 and 18 MV, respectively (Figure 3-3). The measured dose was obtained from the average of five measurements and was normalized to the central diode readings. The error bars on the graph are equal to ± 0.3% and were derived from the short-term reproducibility analysis described earlier. Figure 3-3. Relative dose differences between measured and planned doses for the 11 diodes in the phantom (20 x 20 cm 2 field). The error bars are equal to ± 0.3% and are estimated from the short-term reproducibility analysis. 73

93 The diode response in the penumbra was assessed by translating a narrow (5.1 cm) beam with respect to the diodes. The readings of 6 diodes located in the penumbra of the translating 6 MV beam are plotted in Figure 3-4a (dash lines and symbols) along with the corresponding planning system estimates in full line. The data are averages of three diode readings acquired in a single session and were normalized to the central diode, which was always located in the open central region of the beam. Although the planning system predicted a sharper dose gradient than the measurements at the diode locations, both set of curves agreed usually within ± 1mm in the 10% to 90% dose region. This discrepancy in the high dose gradient is most likely due to inaccuracy in the penumbra model of the planning system. The agreement beyond the penumbra was good with the measurements reporting slightly higher results than the calculations. The absolute position of the measured and calculated penumbra (50% transition point or nominal field edge) agreed on average within 0.4 mm for all diodes. This experiment was repeated 8 times over a 2- month period and produced an average discrepancy of -0.4 ± 0.5 mm (1 SD) between measured and calculated field edge (range of 1 to 0.7 mm). The phantom sensitivity to setup errors in a simple one-beam plan was assessed by keeping the narrow beam stationary and translating the phantom in the RL direction. Figure 3-4b shows the measured dose obtained before and after 3 progressive phantom shifts (0.5, 1.2 and 2.3 mm). Phantom displacements were estimated from the distance to agreement between the dose measurements before and after shifts. These displacements were compared to the distances reported from the camera system and were plotted as a function of the calculated dose gradient at the diode locations (Fig. 3-4c). The error bars on this graph correspond to the estimated dose measurement uncertainties (0.5%) converted to millimetres using the local dose gradient 74

94 calculated from the planned dose distribution. For dose gradients of 1%/mm or greater (corresponding to the 5-95% penumbra region), the phantom displacement obtained from the measured dose agreed with the camera system within ± 0.2 mm and most typically within ± 0.1 mm. 75

95 Figure 3-4. a): Responses of 6 diodes located in the penumbra of a 6 MV beam translated across the phantom (dash lines and symbols). The corresponding planning system calculations are shown in full line. b): Dose measurements performed after phantom shifts (0.5, 1.2 and 2.3 mm) are plotted with the measurement obtained before shift. c): Differences between dose-based and camera system displacements were plotted as a function of the dose gradient at the diode locations. 76

96 3.4.2 Integral Testing and Sensitivity Analysis in the Clinical Setting. The dose distribution (at diode plane) of the spinal radiosurgery plan used for the phantom sensitivity analysis is shown in Figure 3-5a. The impact of inducing systematic MLC errors (right bank only) on the number of measurement points in agreement with the treatment plan is demonstrated with the histogram of Figure 3-5b. The plotted data are averages of 3 different measurement sessions and the error bars represent ± 1 SD. For the non-modified plan (0 mm MLC error), 97.1% ± 1.5% of the 68 measurement points (7 beams x 11 diodes, minimum dose threshold of 5%) agreed with the calculated dose within 3% of ΔD or 2 mm of DTA (3%/2mm). For tighter tolerances of 3%/1mm, the number of passing points decreased to 82.4% ± 1.5% with the failing points located mainly in the steep penumbrae. The differences (in terms of ΔD and DTA) could be due in part to observed discrepancies in penumbra modeling (Fig. 3-4a), potential residual setup error after IG ( 0.6 mm) 10 and calibration errors in the MLC. The sensitivity to a 1-mm miscalibration of the right side of each MLC segment is high, decreasing the agreement between measurement and calculation to 62.3% and 59.8% (3%/1mm), for the field contraction and expansion, respectively. This reduction of passing points was statistically significant with a p-value < 0.01 (t-test). The IG-IMRT QA phantom presented a similar sensitivity to MLC errors introduced on the left MLC bank (not shown). The contraction or expansion of the both MLC banks by 1 mm (not shown) further decreased the number of passing points to 42.2% ± 4.5% (3%/1mm). The sensitivity of the phantom to setup errors in the RL direction is shown in Figure 3-6 for the IMRT plan of Figure 3-5a. For each phantom displacement, the IMRT plan was delivered three 77

97 times in a single session and the resulting diode readings were averaged. Inter-session averaging was not performed in this case due to the difficulty to reproduce the same small displacements with the accelerator couch. The percentage of measurement points in agreement with the treatment plan (3%/1mm) decreased from 85.3% to 67.6% and 63.2% for setup errors of ± 1 mm, respectively. For phantom displacements tested here (few millimetres), the measurements were also less sensitive to setup errors than to systematic MLC calibration errors (both MLC banks) of equivalent magnitude. This is easily explained by the fact that MLC displacements affect the location of the dose gradient in the phantom directly whereas displacements of the phantom will not necessarily be moving in the direction of the steepest dose gradient. For example, a lateral displacement of 2 mm would not affect the dose for the lateral beams significantly. 78

98 Figure 3-5. a): Phantom sensitivity to systematic MLC calibration errors for a 7-beam IMRT plan designed for a lumbar metastasis. b): The histogram gives the number of measurement points, which agreed with the dose calculation within given tolerances of dose difference and distance to agreement. Positive errors correspond to MLC expansions and negative errors to contractions of the right MLC bank. 79

99 Figure 3-6. Phantom sensitivity to setup errors in the RL direction for the IMRT plan of Figure 3-5a. This histogram gives the number of measurement points, which agreed with the dose calculation within given tolerances of dose difference and distance to agreement. 80

100 3.5 Discussion The basic properties of a first prototype IG-IMRT QA phantom were assessed using simple and complex beam arrangements. The QA phantom offered good response linearity, long-term reproducibility and nearly isoplanatic directional response well suited for coplanar beams, which represent the most common beam arrangement in radiotherapy. Errors in phantom placement as small as 0.5 mm could be detected with an accuracy of ± 0.1 to 0.2 mm for single beam geometry when the dose gradient at the point of measurement was at least 1%/mm. These results agree well with the sensitivity of the MOSFET phantom reported by Rowbottom et al 12 (0.2 to 1 mm). Although tested indirectly with IMRT distributions, the diode response for cumulative small MU deliveries ( 2 MU) 22 looks promising but remains to be verified systematically. The QA phantom was used in an integral test to verify the dosimetric and geometric accuracy of an IG-IMRT treatment designed for a clinical spinal radiosurgery case. The comparison between measured and planned dose performed on an individual beam basis has two advantages over a composite dose comparison: First, in the presence of uncertainties, the higher number of measurement points assist in powering conclusions, and second, the individual beam analysis facilitate the identification of error sources. Systematic MLC calibration errors of 1 mm on one or both MLC banks introduced in the radiosurgery plan were easily detectable with the QA phantom. The phantom demonstrated also sufficient sensitivity for the detection of setup errors as small as 1 mm in the RL direction. In general, the phantom sensitivity to both MLC and setup errors depends not only on its characteristics but also on the dose distribution selected for 81

101 the integral test. Future work on the assessment of the phantom sensitivity to dose delivery errors could also include the combination of multiple calibration errors (gantry rotation, MLC calibration, etc). The prototype IG-IMRT QA phantom with its 7 cm diameter active area is likely to be less sensitive to image guidance and delivery errors for large field IMRT plans than for radiosurgery type plans. The next version of the IG-IMRT QA phantom is under design and could count up to 100 diodes over a 20 cm diameter area, which should widen its application to large field IMRT. The number of diodes usable in a single plane is limited by the complexity of the assembly and also by potential interference from neighbouring detectors. The fibreglass PCB in the actual prototype is to be replaced by water-equivalent plastic to improve the visibility of the diodes on CT and CBCT images and reduce the overall phantom heterogeneity for planning purposes. The fiducal markers (air holes) and metal solder will be moved away from the diode plane to minimize variations in the directional response of the diodes. The phantom construction could also be modified to house an ion chamber, which would allow the crosscalibration of the diodes for absolute dosimetry. The insertion of two rings of steel ball bearings at both ends of the cylindrical phantom could allow the integration of geometrical tests of source and imaging detector positions (kv and MV) 9. Image testing modules (see Fig 3-1a) inserted in the next prototype could also allow concomitant dosimetric IG-IMRT QA and CBCT image quality assessment in terms of spatial resolution, contrast to noise ratio, etc. The integral test of the IG-IMRT process performed on a linear accelerator capable of CBCT acquisition took about 45 to 60 minutes using the first QA phantom prototype and the current 82

102 level of system integration. The use of an ergonomic table attachment to expedite phantom setup, automatic CBCT to CT registration using fiducial markers and the implementation of an integrated dose acquisition and analysis software could significantly reduce the duration of the integral test. The remote controlled couch now available on the Elekta accelerator should also streamline the setup correction process and allow testing of non-isocentric conditions. With the advent of the next phantom prototype and the improvements in system integration, the integral test of the IG-IMRT process could be implemented as part of the linear accelerator routine daily or weekly QA. The IMRT plan delivered to the phantom would be selected randomly from a library of 10 to 15 pre-calculated plans representative of a range of clinical dose distributions and isocenter positions. The integration of a few orthogonal beams to these clinical plans would take advantage of the phantom sensitivity to dose gradients localization and increase its sensitivity to setup errors. With its sparse measurement point density, the IG-IMRT QA phantom was not designed to replace continuous 2D or 3D dosimeters (such as film or gel), which might be better adapted for commissioning of new treatment techniques. However, realtime dose measurements and analysis combined with the high spatial resolution and reproducibility of the diodes make the phantom an attractive tool for routine QA of IG-IMRT treatments. Although the IG-IMRT QA phantom was designed originally for linear accelerator based IG-IMRT, it could also potentially be used for the routine QA of other image guidance and delivery systems such as Tomotherapy 3, In-room CT 5, 6, robotic mounted accelerator 23 or even Gamma Knife

103 3.6 Conclusion In this work, an integral test phantom was developed for the dosimetric and geometric quality assurance of linear accelerator capable of IG-IMRT. The first prototype IG-IMRT QA phantom demonstrated the feasibility of axial dosimetry using diodes with good reproducibility and acceptable angular response (usually within ±1%). The phantom offered sufficient sensitivity for the detection of remarkably small systematic MLC calibration and setup errors (0.5 mm for single field). The next generation of the QA phantom will focus on incorporating a larger number of diodes and improving system integration to streamline the QA process. The opportunity to produce a highly integrated, high performance measurement system has been made possible through a concerted effort involving electronics, software and image guidance technology. These tools could demonstrate simplification of the QA process and increase the level of performance of image guided and intensity modulated radiotherapy. 3.7 Acknowledgment The authors would like to thank Douglas Moseley and Gregory Bootsma (Princess Margaret Hospital), as well as William Simon, Jeff Hildreth and Jie Shi (Sun Nuclear Corporation, Melbourne, FL) for their support and helpful discussions. This work was supported in part by Sun Nuclear Corporation and Elekta Inc. 84

104 3.8 References 1. D. A. Jaffray, J. H. Siewerdsen, J. W. Wong, A. A. Martinez, "Flat-panel cone-beam computed tomography for image-guided radiation therapy," Int J Radiat Oncol Biol Phys. 53, (2002). 2. M. Oldham, D. Letourneau, L. Watt, G. Hugo, D. Yan, D. Lockman, L. H. Kim, P. Y. Chen, A. Martinez, J. W. Wong, "Cone-beam-CT guided radiation therapy: A model for on-line application," Radiother Oncol. 75, (2005). 3. T. R. Mackie, J. Kapatoes, K. Ruchala, W. Lu, C. Wu, G. Olivera, L. Forrest, W. Tome, J. Welsh, R. Jeraj, P. Harari, P. Reckwerdt, B. Paliwal, M. Ritter, H. Keller, J. Fowler, M. Mehta, "Image guidance for precise conformal radiotherapy," Int J Radiat Oncol Biol Phys. 56, (2003). 4. J. Lattanzi, S. McNeely, A. Hanlon, I. Das, T. E. Schultheiss, G. E. Hanks, "Daily CT localization for correcting portal errors in the treatment of prostate cancer," Int J Radiat Oncol Biol Phys. 41, (1998). 5. D. S. Mohan, P. A. Kupelian, T. R. Willoughby, "Short-course intensity-modulated radiotherapy for localized prostate cancer with daily transabdominal ultrasound localization of the prostate gland," Int J Radiat Oncol Biol Phys. 46, (2000). 6. M. Uematsu, A. Shioda, K. Tahara, T. Fukui, F. Yamamoto, G. Tsumatori, Y. Ozeki, T. Aoki, M. Watanabe, S. Kusano, "Focal, high dose, and fractionated modified stereotactic radiation therapy for lung carcinoma patients: a preliminary experience," Cancer. 82, (1998). 7. A. S. Shiu, E. L. Chang, J. S. Ye, M. Lii, L. D. Rhines, E. Mendel, J. Weinberg, S. Singh, M. H. Maor, R. Mohan, J. D. Cox, "Near simultaneous computed tomography image-guided stereotactic spinal radiotherapy: an emerging paradigm for achieving true stereotaxy," Int J Radiat Oncol Biol Phys. 57, (2003). 8. D. Letourneau, A. A. Martinez, D. Lockman, D. Yan, C. Vargas, G. Ivaldi, J. Wong, "Assessment of residual error for online cone-beam CT-guided treatment of prostate cancer patients," Int J Radiat Oncol Biol Phys. 62, (2005). 9. Y. Cho, D. J. Moseley, J. H. Siewerdsen, D. A. Jaffray, "Accurate technique for complete geometric calibration of cone-beam computed tomography systems," Med Phys. 32, (2005). 10. M. B. Sharpe, D. J. Moseley, T. G. Purdie, M. Islam, J. H. Siewerdsen, D. A. Jaffray, "The stability of mechanical calibration for a kv cone beam computed tomography system integrated with linear accelerator," Med Phys. 33, (2006). 85

105 11. D. Letourneau, R. K. Wong, D. J. Moseley, M. B. Sharpe, S. Ansell, M. K. Gospodarowicz, D. A. Jaffray, "On-line planning and delivery technique for radiotherapy of spinal metastases using cone-beam CT: image quality and system performance," Int J Radiat Oncol Biol Phys. Accepted for publication. 12. C. G. Rowbottom, D. A. Jaffray, "Development of an integral system test for imageguided radiotherapy," Med Phys. 31, (2004). 13. J. Van Dyk, R. B. Barnett, J. E. Cygler, P. C. Shragge, "Commissioning and quality assurance of treatment planning computers," Int J Radiat Oncol Biol Phys. 26, (1993). 14. D. A. Low, W. B. Harms, S. Mutic, J. A. Purdy, "A technique for the quantitative evaluation of dose distributions," Med Phys. 25, (1998). 15. AAPM. Diode In Vivo Dosimetry for Patients Receiving External Beam Radiation Therapy, Report of Task Group No. 62. Madison, WI: American Association of Physicists in Medicine; IEC. Radiotherapy equipment coordinates, movements and scales, Report Geneva, Switzerland: International Electrotechnical Commission; ICRU. Use of Computers in External Beam Radiotherapy Procedures with High-Energy Photons and Electrons. Report No. 42. Washington, DC: International Commission on Radiation Units and Measurements; A. S. Shiu, S. Tung, K. R. Hogstrom, J. W. Wong, R. L. Gerber, W. B. Harms, J. A. Purdy, R. K. Ten Haken, D. L. McShan, B. A. Fraass, "Verification data for electron beam dose algorithms," Med Phys. 19, (1992). 19. D. Letourneau, J. W. Wong, M. Oldham, M. Gulam, L. Watt, D. A. Jaffray, J. H. Siewerdsen, A. A. Martinez, "Cone-beam-CT guided radiation therapy: technical implementation," Radiother Oncol. 75, (2005). 20. J. R. Sykes, A. Amer, J. Czajka, C. J. Moore, "A feasibility study for image guided radiotherapy using low dose, high speed, cone beam X-ray volumetric imaging," Radiother Oncol. 77, (2005). 21. M. K. Islam, T. G. Purdie, B. D. Norrlinger, H. Alasti, D. J. Moseley, M. B. Sharpe, J. H. Siewerdsen, D. A. Jaffray, "Patient dose from kilovoltage cone beam computed tomography imaging in radiation therapy," Med Phys. 33, (2006). 22. M. B. Sharpe, B. M. Miller, D. Yan, J. W. Wong, "Monitor unit settings for intensity modulated beams delivered using a step-and-shoot approach," Med Phys. 27, (2000). 86

106 23. J. R. Adler, Jr., S. D. Chang, M. J. Murphy, J. Doty, P. Geis, S. L. Hancock, "The Cyberknife: a frameless robotic system for radiosurgery," Stereotact Funct Neurosurg. 69, (1997). 24. D. G. Leksell, "Stereotactic radiosurgery. Present status and future trends," Neurol Res. 9, (1987). 87

107 Chapter 4 SEMI-AUTOMATIC VERTEBRAE VISUALIZATION, DETECTION AND IDENTIFICATION FOR ON-LINE PALLIATIVE RADIOTHERAPY OF BONE METASTASES OF THE SPINE Daniel Létourneau 1, M.Sc. Michael Kaus 2, Ph.D. Rebecca Wong 1, MD Anita Vloet 1, MRT(T) David A Fitzpatrick 1, MD Mary Gospodarowicz 1, MD David A Jaffray 1,3,4, Ph.D. 1 Radiation Medicine Program, Princess Margaret Hospital, Toronto, ON, Canada 2 Philips Medical Systems, Fitchburg, WI, USA, Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada 4 Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada This chapter was published in the Med. Phys. 35, p (2008) 88

108 4.1 Abstract A treatment process which integrates simulation, planning and delivery in one single session of <30 min on a treatment unit capable of cone-beam CT imaging (CBCT) is under development in our institution for palliation of spinal metastases. The objective of this work is to develop and validate a semi-automatic vertebra detection and identification algorithm to streamline the target definition process and improve the consistency of online planning on cone-beam CT datasets while the patient is on the treatment couch. Key issues pertaining to this work are the limited field of view and image quality of CBCT, the inter and intra-patient variation of vertebra morphology, and the spine curvature. An initial library of 10 patient CBCT datasets were used to derive the vertebra detection and identification method and set the parameters used by the algorithm. In this method, sagittal and coronal curved digitally reconstructed radiographs (cdrrs) are first created by projecting a sub-volume of the CBCT data orthogonally to the centerline of a cylinder model positioned manually. The detection of the vertebra centers is then performed on the cdrrs based on an edge detection algorithm. The identification of the vertebrae by name is based on the detection of 1 or more of 4 different reference anatomical landmarks on cdrrs. The validation of the vertebra detection and identification algorithm was performed on a library of 27 patient CBCT datasets with an average detection success rate of 92.8% and 89.9% for sagittal and coronal cdrrs, respectively for three different users. The entire process including manual steps and user approval was performed on average in 3.23 to 3.45 min (n = 37, three users) with only 0.14 min for the automatic detection and identification of the vertebrae. The semi-automatic identification and segmentation of vertebrae on CBCT images was shown to be robust and effective. The next step will be the clinical implementation 89

109 of the algorithm within the on-line planning and delivery treatment technique for patients with spinal bone metastases. 90

110 4.2 Introduction An on-line planning and delivery technique for radiotherapy of patients with painful spinal metastases using cone-beam CT (CBCT) is under development in our institution 1. This technique, performed at the treatment unit, integrates patient imaging, treatment planning and dose delivery in a single session equivalent to an initial treatment appointment. The feasibility of the on-line planning and delivery technique was reported previously demonstrating a duration of 25 min on phantom 1 and a dosimetric accuracy satisfying accepted radiotherapy standards 2-5. The clinical implementation of this method of treatment has the potential to improve patient access to palliative radiotherapy and increase efficiency in the workflow for these cases. The planning step in the on-line treatment technique described above has been identified as the most time consuming task due predominantly to the manual nature of target definition 1. For spine metastasis patients, the process of target definition is divided into two steps: the user first identifies by name the vertebrae imaged in the dataset and then localizes and contours the vertebra to be treated. Delineation of vertebrae on CBCT can be semi-automated using for example an atlas-based segmentation algorithm 6-8. The identification of the specific vertebrae in the CBCT dataset remains however a manual process, which is based mainly on user observations of normal anatomy and knowledge of bone deterioration of the vertebra to be treated. This manual detection and identification is the initialization step of any atlas-based contouring method and is the focus of this work. Automatic detection of anatomical features is a valuable tool that could be employed to accelerate the on-line planning step and shorten the overall on-line treatment process. 91

111 The objective of this work is to develop a semi-automatic vertebra detection and identification algorithm for on-line treatment planning of spinal lesions. The key technical challenges pertaining to the semi-automatic vertebra detection and identification are related to CBCT image quality 9-12 and limited field of view, intra and inter-patient variation in terms of vertebra morphology, and spine curvature. Currently, the CBCT images acquired as part of the on-line treatment strategy are often characterized by cupping artefacts, limited low contrast resolution and an inaccurate representation of CT-numbers, caused in particular by the elevated presence of radiation scatter reaching the detector in the cone-beam geometry The method of vertebra detection and identification developed in this work was inspired by the experience of Kasai et al 14 on vertebra fracture detection on planar radiographs. A customized rule-based approach that operates on two-dimensional (2D) projection images was selected for reason of speed and improved image signal to noise ratio. In this paper, the algorithm for semi-automatic detection and identification of vertebrae is described in a detailed fashion along with its interface with a research version of a commercial treatment planning system (TPS, Pinnacle 3, version 7.9u, Philips Medical Systems, Andover, MA). The performance of the algorithm was assessed on a library of patient CBCT images (normal and abnormal bony anatomy) and is reported here in terms of the success rate for vertebra detection, identification, and elapsed time. 92

112 4.3 Methods and Materials Image datasets Patient CBCT images used in this study to design and validate a semi-automatic method for detection and identification of vertebrae were acquired on linear accelerators 13 (Synergy, Elekta, Crawley, UK) using an imaging dose of about 0.5 to 2 cgy. These patients were enrolled in a research ethics board (REB) approved protocol on the feasibility of an on-line planning and delivery technique 1. Head and neck patient images acquired under a REB approved protocol on patient immobilization evaluation were used retrospectively to supplement for the absence of patients with cervical metastases. All CBCT images were reconstructed with a cubic voxel resolution of 1 mm 3 and a field of view of 400 x 400 x 200 to 256 mm 3 and were imported into the TPS. The first 10 patient datasets were used to derive the detection and identification method and set the value of the empirical parameters used by the algorithm to be described below. Six of these patients had bone metastases of the spine, three had soft tissues masses located in the thorax or the pelvis and one was a head and neck cancer patient. Twenty-seven patient CBCT datasets (8 spinal metastases, 11 soft tissue masses and 8 head and neck) were then used to assess the performance of the semi-automatic vertebra detection and identification method. Ten out of the total of 14 patients with bone metastases of the spine presented with clear vertebra deterioration such as erosion (holes) and deformation (collapse). 93

113 4.3.2 Algorithm description The semi-automatic vertebra localization and identification algorithm, referred to hereafter as the vertebra finder (VF), was implemented as a stand-alone application (Matlab, Natick, MA) and was interfaced to a research version of a commercial TPS (Pinnacle 3, version 7.9u, Philips Medical Systems, Andover, MA) via the TPS s scripting tools. The overall process (Fig. 4-1) consisted of a sequence of manual (shown in grey) and automated steps. Once the CBCT images for a given patient are imported into the TPS, the coarse localization of the spine is performed by loading a cylinder model 6 with the curvature of an average full-length spine from the TPS organ model library (step 1 in Fig. 4-1). This library contains various surface organ models provided by the manufacturer or generated by the user. The curved cylinder model is manually positioned and adapted to encompass the patient vertebral bodies and follow the spine curvature. The user next selects in the TPS organ model library the name (C1 to L5) of one or more vertebrae to be localized (step 2 in Fig. 4-1). The VF is then launched through the script menu of the TPS interface. The name(s) of the vertebra to be treated, the points forming the manually adapted cylinder model and the file location of the CBCT dataset on disk storage are communicated by the TPS script to the VF, which loads the cylinder model and vertebra information as well as the CBCT images in memory. The VF will first calculate the curved cylinder model centerline by averaging the coordinates of the contour formed by the intersection of the cylinder model with the corresponding CBCT slice (step 3 in Fig. 4-1). The cylinder model centerline, which is located approximately at the center of the vertebral bodies, is shown in Fig 4-2a for a lumbo-thoracic patient along with the three-dimensional (3D) rendering of the corresponding CBCT dataset. The 3D search space is therefore reduced to 2 orthogonal (sagittal and coronal) curved digitally reconstructed radiographs (cdrr) by projecting a sub- 94

114 volume of the CBCT data orthogonally to the centerline of the cylinder model (step 4 in Fig. 4-1). The projected data is limited to ± 15 mm around the centerline in both anterior-to-posterior (coronal cdrr) and left-to-right direction (sagittal cdrr) to enhance the visibility of the individual vertebral bodies. This process is similar to curved planar reformation used in CT angiography 15 and MR 16 and has the advantage to display a curved anatomical structure on a plane. The region of the 3D data used in the creation of the 2D images along with the corresponding sagittal and coronal cdrrs are shown in Fig. 4-2a, b and c, respectively. A second coronal cdrr (not shown in Fig. 4-2) located 40 mm posterior to the cylinder model centerline is also created and will be used for vertebra identification in subsequent steps. 95

115 Figure 4-1: Flow chart describing the semi-automatic vertebra detection and identification algorithm. The tasks shown on grey background represent manual steps. The vertebra finder (VF) is launched via scripting through the treatment planning system (TPS) interface. Steps 3 to 9 (within dashed line) are performed outside TPS. Once the VF results are approved by the user, the coordinates of the vertebra centers are transferred back to the TPS. The plan can then be completed (step 10). 96

116 The next task is to identify different gross anatomical regions (head and neck, thorax or abdomen-pelvis) imaged in the dataset (step 5 in Fig. 4-1). This is performed on the coronal cdrrs and is the first step toward the identification of the individual vertebrae imaged in the CBCT dataset. The gross anatomical regions are identified sequentially beginning superiorly at the head and neck region and no overlap is allowed. The patient external contour is segmented along rows based on a greyscale threshold (30% of maximum cdrr value) and a binary mask is generated (0 outside and 1 inside the contour). The width of the patient cross-section on the coronal cdrr is estimated by summing the content of the binary mask across rows. The head and neck region is characterized by a maximum width of 250 mm, which includes the top of the shoulders (trapezius muscles). The detection of the thoracic region is based on lung segmentation and is illustrated in Fig A greyscale histogram (200 bins) of the coronal cdrr (Fig. 4-3a) is produced and the cdrr is normalized to the highest greyscale bin containing 5% of the histogram highest peak value (maximum frequency). Pixels within 10-65% of highest greyscale value are identified as lung (see outline in Fig. 4-3b). The lung segmentation is constrained to a region of ± 100 mm each side of the cylinder model centerline to prevent the segmentation of the space between the arms and the thorax (patients with arms along the side). The thoracic region is characterized by a minimum lung width of 60 mm (Fig.4-3c), which allowed for thoracic region identification in patients with partially obstructed lungs. Depending of the lung shape, the thoracic identification method might truncate slightly the apices and inferior tips of the lungs (see dash lines on Fig. 4-3b). The remaining region of the coronal cdrr not identified as head and neck or thorax is finally designated as part of the abdomen-pelvis region. A marker (V-shaped indentation) on the cylinder model between L5 97

117 and sacrum is used as a division between abdomen and pelvis. Therefore, the location of this boundary relies on the placement of this point by the user. 98

118 Figure 4-2: Steps 3 and 4 of flowchart (Fig. 4-1). Formation of sagittal and coronal curved digitally reconstructed radiographs (cdrr) from a cone-beam CT dataset (CBCT). The centerline of a cylinder model, which encompasses the vertebral bodies is shown in (a) with a bold dash line. The CBCT data was projected orthogonally to the centerline to produce the sagittal and coronal 2D images (b and c). The projected data was limited to ± 15 mm around the centerline in both anterior-to-posterior (coronal cdrr) and left-to-right direction (sagittal cdrr). The position of a second coronal cdrr located 40 mm posterior to the centerline is shown also in (a) as a thin dash line. The 3D dataset (a) was acquired with an imaging dose of about 2 cgy. The 2 spheres and the linear structures visible on the top of the patient abdomen (a) is a reflector tool used for optical tracking of the patient external contour (not part of this study). 99

119 The vertebra identification by name (C1 to L5) is based on the semi-automatic detection of 4 distinct reference anatomical landmarks (steps 6 and 7 in Fig. 4-1). The base of the skull, apices of the lungs, T12-ribs (last pair of ribs) and iliac bones were selected as surrogates for the joint between skull and C1, C7 - T1, T12 - L1 and L5 - sacrum, respectively. These four anatomical structures (referred hereafter as reference landmarks) provide the algorithm with a frame of reference for naming the vertebrae. The gross anatomical regions identified previously dictate to the VF which of the 4 reference landmarks should be found on the cdrr. The location of a given landmark is estimated automatically by registering a template of the landmark (described below) with the coronal cdrr. The apices of the lung are detected on the coronal cdrr containing the vertebral bodies (Fig. 4-2a) while the base of the skull, the T12-ribs and the iliac bones are identified on the 40-mm-posterior coronal cdrr (Fig. 4-4a), which usually offers good visualization of one or more of these structures. The choice of the 40-mm-posterior coronal cdrr for the T12-ribs detection was also dictated by the absence of the diaphragm on this image, which could represent an important confounding feature. The templates of the 4 reference landmarks were all generated using the same method. For the rib template for example, the location of the edges of the T12-ribs (on coronal cdrr) for 5 different patients was averaged together in space. The derivative of the average rib contours was performed in the superior to inferior (SI) direction to produce a sharp gradient image of the rib s edges (bottom part of Fig. 4-4a), which is used as the rib template. The derivative of the coronal cdrr is also performed in the SI direction and a normalized cross-correlation coefficient map 17 (Fig. 4-4b) is produced by convolving the template with the cdrr starting from the superior aspect of the image to the bottom of the cdrr. The VF finds the maximum 100

120 cross-correlation coefficient in a region constrained to ± 10 mm around the cylinder model centerline and overlays the template on the corresponding coronal cdrr. This registration of the template was limited to translations only. A line corresponding to the mid-height of the template (SI direction) is also displayed on the coronal cdrr containing the vertebral bodies (Fig. 4-4c). The user can then approve the results of the registration or adjust if necessary the template to match the proper inter-vertebral space. The registration of the rib template may also produce cross-correlation coefficient peaks for other ribs than the T12-ribs. The algorithm seeks in that case the most inferiorly located maxima, which satisfy a threshold of 70% of the maximum cross-correlation coefficient map. In the case of Fig. 4-4b, the maximum occurred at the T12-ribs. 101

121 Figure 4-3: Step 5 of flowchart (Fig. 4-1). Automatic identification of the thoracic region. A greyscale histogram (a) of the coronal curved digitally reconstructed radiographs (cdrr) is produced and the cdrr (b) is normalized to the highest greyscale bin containing at least 5% of the histogram highest peak value (maximum frequency). Pixels within 10-65% of highest greyscale value are identified as lung (the grey shade area on the histogram corresponds to the contoured region on the cdrr). The width of the lung cross-section on the coronal cdrr is estimated by summing the pixels identified as lung along rows (c). The thoracic region is characterized by a minimum lung width of 60 mm. 102

122 Finally, the vertebra center detection is performed sequentially on both sagittal and coronal cdrrs (steps 8 and 9 in Fig. 4-1). The derivative of the cdrr is first calculated in the SI direction and converted to an absolute value (Fig. 4-5a). A summation window of 10 mm of height (SI direction) and 20 mm of depth (sagittal cdrr) or 40 mm of width (coronal cdrr) is then translated in the SI direction along the model centerline to produce a cumulative gradient curve. This process sums together the image gradients (Fig 4-5a) located at the interface between subsequent vertebrae and reduces the gradient image noise. The curve shown in Fig. 4-5b represents the cumulative gradient curve minus the local median value calculated for a window width of 50 mm. The vertebra center candidates correspond to the minima located between the zero-crossing points of the offset-subtracted cumulative gradient curve. The actual position of each center candidate is obtained by locating the minima of a third order polynomial equation fitted to the cumulative gradient curve between the corresponding pair of zero-crossing points. The standard deviation calculated on the distance between the vertebra center candidates (SD dist ) is compared to the distance between individual center candidates in an attempt to identify false positives candidates (i.e. extra vertebra centers). Starting from the bottom of the image, for each pair of vertebra center candidates separated by less than 1.5 SD dist (default value), the candidate located superiorly in the image is deleted. The remaining vertebra center candidates are overlaid on the cdrr (Fig 4-5c) and named (C1 to L5) based on their position with respect to the reference landmark and corresponding inter-vertebral space identified previously by the VF. This image is saved automatically in jpeg format for subsequent performance evaluation. The user can then approve the VF results or make corrections and approve the modified results. In case of multiple erroneous center candidates, the user can also repeat the detection using a different distance threshold for elimination of the false positive 103

123 candidates (choice of 1.25, 1.5, 2 and 2.5 SD dist ). Upon approval, the VF saves a second copy of the cdrrs with the approved vertebra centers marked for comparison. 104

124 Figure 4-4: Steps 6 and 7 of flowchart (Fig. 4-1). Reference landmark (T12-ribs) detection on posterior coronal curved digitally reconstructed radiographs (cdrrs) (40 mm posterior to the cylinder centerline). The derivative in the superior to inferior direction of the coronal cdrr was convolved with the derivative of a rib template (a) to obtain a map of normalized cross correlation coefficients (b). The maximum value of this map corresponds to the T12-rib position and the corresponding inter-vertebral space between T12 and L1 is shown with a dashed line on the cdrr (c). 105

125 Figure 4-5: Steps 8 and 9 of flowchart (Fig. 4-1). Vertebra center detection on curved digitally reconstructed radiographs (cdrrs). a) The image gradient in superior to inferior (SI) direction was first computed on the coronal cdrr and a summation window translated in the SI direction produced a cumulated gradient curve. The curve shown in (b) represents the cumulated gradient curve minus the local median value calculated for a window width of 50 mm. The vertebra center candidates (c) correspond to the minima located between the zero crossing points of the cumulated gradient curve. They are identified by name based on their positions with respect to the T12-L1 inter-vertebral space (dashed line). 106

126 4.3.3 Algorithm validation Validation of the vertebra detection and identification algorithm as described in Fig. 4-1 was performed by three different operators (1 experienced and 2 novice users) using the 37 patient datasets acquired in this study. The impact of the manual positioning of the cylinder model on the reproducibility of the VF results was assessed by comparing the success rate obtained by each user (inter-observer analysis). The analysis of the 37 datasets was also repeated three times by the same user to evaluate the VF intra-observer variation. These analyses were performed at least two weeks apart to minimize recall biases. For each patient dataset and each user, the position of the reference landmark(s) found on the coronal cdrr along with any corrections performed by the user before approval was recorded in a text file. The vertebra center coordinates and name of the vertebra approved by the user on sagittal and coronal cdrrs were also recorded automatically in the same text file along with the time required to perform each step presented in Fig The success of the gross anatomical region identification and reference landmark detection was assessed by the number of landmarks identified correctly and the amount of adjustment done by the user to get the location of the proper inter-vertebral space. The copies of the cdrr showing the vertebra center candidates before and after user approval were presented for review to identify any vertebra detection and identification errors. The detection and identification of a given vertebra was deemed successful if the vertebra center candidate was located within the limits of the corresponding vertebral body as seen on the cdrr. Vertebrae missed by the VF were considered as false negatives. Extra vertebra center candidates or vertebra center candidates identified in an inter-vertebral space were counted as false positives. The success rate of vertebra detection and identification was compiled 107

127 separately for the 10 datasets used to derive the algorithm and the 27 datasets used for its validation. It was defined as the total number of vertebrae included in the datasets minus false negatives divided by the total number of vertebrae included in the datasets plus false positives (given in percent). 4.4 Results Gross Anatomical Region and Reference Landmark Detection For the inter and intra-observer analyses, the VF correctly identified all gross anatomical regions presented on the coronal cdrrs for both the datasets used for the design and the validation of the algorithm. The normalization of the cdrr based on greyscale histogram (Fig. 4-3a) contributed to the reliability of the thoracic region identification by reducing the impact of highdensity material and inter-scan CBCT-number variation 9, 11, 12 on the greyscale-based lung segmentation method. For the inter and intra-observer analysis, the success rate of the automatic detection of the 4 reference landmarks used for the identification of the vertebrae ranged from 81.8% to 100% for the 10 datasets used to derived the algorithm and varied from 84.3% to 91.1% for the 27 datasets used for the algorithm validation. Out of 37 patients, 6 had more than one reference landmark visible on the same coronal cdrr (for example, the base of skull and apices of the lungs). The landmark detection results obtained by one of the 3 users are compiled in Table 4-1 for the 37 patients. At the approval step, the user was allowed to manually correct landmarks, which were not properly identified. Specifically, the VF detected the ribs attached to T11 vertebra instead of T12 for 4 out of 5 failures. For all landmarks detected properly, the average adjustment made by the user to match the corresponding inter-vertebral space and the 108

128 standard deviation on the adjustment depended on the reference landmark (Table 4-1). These results are comparable to the adjustments made by the other users in the inter and intra-observer analyses. The T12-ribs and apices of the lung templates required on average 0.8 ± 4.8 mm and 5.2 ± 3.8 mm of manual adjustment respectively, to match the corresponding inter-vertebral space. The standard deviation for the base of the skull and particularly for the iliac bones was larger than for the two other landmarks, which was attributed to the variation in shape and size of these anatomic structures across patients. The iliac bones for example were detected successfully for all pelvic patients (n=7) but the inter-patient variation in separation between the left and right iliums (as seen on coronal cdrr) contributed to the variation observed for the detection of the L5-sacrum junction. 109

129 a b Figure 4-6: Distribution of vertebrae included in the cone-beam CT (CBCT) datasets of 37 patients enrolled in this study. The numbers of false positive and negative vertebra centers detected by the vertebra finder (VF) on sagittal and coronal curved digitally reconstructed radiographs (cdrrs) are shown in (a) and (b), respectively (user 1, inter-observer study). The false positive vertebra centers are plotted between the tick marks of the histogram (a) while the false negatives are shown at the corresponding vertebrae (b). 110

130 4.4.2 Vertebra Detection and Identification The distribution of vertebrae imaged in the 37 patient CBCT datasets is shown in Fig. 4-6 along with the false positive (Fig. 4-6a) and false negative (Fig. 4-6b) vertebra center candidates detected by the VF on sagittal and coronal cdrrs ( results for user 1, inter-observer study). These distributions of false positive and negative vertebra center candidates are representative of the results obtained by the three users. For the inter-observer study, the success rate of the VF for the 10 datasets (55 vertebrae) used in the development of the algorithms varied from 91.2% to 93% and 91.2% to 94.6% for sagittal and coronal cdrrs, respectively. For the 27 patient datasets used for the validation of the algorithm, the VF detected and identified successfully 187, 191 and 192 out of 205 vertebrae (average of 92.8%) on sagittal cdrrs for the three users. The average detection and identification success rate on the coronal cdrrs was 89.9% (183, 184, 185 vertebrae detected correctly). The average VF sensitivity (true positives/( true positives + false negatives)) was 91.6% and 94.8% on coronal and sagittal cdrrs, respectively. After user correction, the success rate of vertebra detection and identification increased to 100% on both cdrr orientations, as expected. For the 10 patients who presented disease-related vertebra deterioration, the VF detected successfully 87.5%-95.2% and 90%-95.1% of the vertebrae on sagittal and coronal cdrrs respectively for the inter-observer study. Examples of vertebra hole and vertebra deformation are shown on coronal cdrr for two different patients in Fig. 4-7a and b. The VF performance was also compiled for each VF user as a function of spine level (Table 4-2). The lowest success rate per user was observed for cervical vertebrae detected on coronal 111

131 cdrrs (user 3: 81.0%, n=79). The detection and identification rate of cervical vertebrae was higher and more reproducible on the sagittal cdrrs than on the coronal cdrrs with a success rate of 90.2 to 91.5%. The lower detection rate observed on coronal cdrrs is attributed to the irregular shape (non-square) of the cervical vertebral bodies, which produced less well-defined bone edges on coronal projections than sagittal images. The detection results for the intraobserver study are shown in Table 4-2 (numbers in parenthesis for user 1). The VF reproducibility for repeated analysis by a single user was good with a maximum success rate variation of 91% to 92.6% for lumbar and thoracic vertebrae. C1, also known as the atlas, was the vertebra most often missed on both sagittal and coronal cdrr (Fig. 4-6b) due to its small size and the distinct lack of a vertebral body. In general, false negatives were more frequent than false positive results and were observed mostly in the cervical and upper thoracic spine (Fig. 4-6b). The false positive vertebra candidate centers were observed more frequently in the lumbar spine (Fig. 4-6a) where the inter-vertebral spaces are the widest. 112

132 Figure 4-7: Examples of vertebra hole (a) and deformation (b) shown on coronal curved digitally reconstructed radiographs (cdrrs). The erosion of the vertebra (a) did not introduce any detection errors. The deformation of multiple vertebrae (b) produced 3 false negatives, which represent one of the worst results for the VF. 113

133 Landmark Number of landmarks in study patients Landmarks detected successfully Average manual adjustment (mm ± SD) T12 Ribs ± 4.8 Base of Skull ± 11.7 Apices of Lungs ± 3.8 Pelvis ± 16.6 Table 4-1: Compilation and ranking of 4 different landmarks detected on coronal curved digitally reconstructed radiographs (cdrrs) for the user 1 participating in the inter-observer study. For all landmarks detected properly, the adjustment made by the user to match a corresponding intervertebral space was recorded and the average and standard deviation of these adjustments is reported. Spine level Lumbar and Thorax Number of patients Number of vertebrae Cervical 9 79 Total Detection success rate (%) Coronal Sagittal U #1 U #2 U #3 U #1 U #2 U # ( ) 86.6 ( ) 90.8 ( ) ( ) 91.2 ( ) 91.5 ( ) Table 4-2: Success rate of vertebra detection. The data is divided by spine level and the success rate is reported for both coronal and sagittal cdrrs (37 patients). For user #1 (U #1), the single numbers represent the mean success rate for three different analyses of the 37 datasets (intraobserver analysis). The numbers in parenthesis are the minimum and maximum success rates obtained by user #1 for the intra-observer analysis. Abbreviation: U = user. 114

134 4.4.3 Elapsed Time The average total time for vertebra detection and identification described in Fig. 4-1 were 3.24 ± 0.92, 3.36 ± 0.94 and 3.45 ± 0.91 min (37 patients) for the three users participating in the interobserver analysis. On average for 37 patients, the time required for the manual tasks (cylinder positioning and approvals) was comparable for all three users and the differences were not statistically significant (t-test, p>0.05). The average time for the individual steps is presented in Fig. 4-8 for the user with the longest average total time (3.45 min). The error bars on this histogram represent one standard deviation. The manual cylinder model positioning and adaptation was the most time consuming task at 1.68 min followed by the user approvals at 0.65 min. Another 1.12 min was required for image and region of interest uploading, the creation of both cdrrs, and the vertebra detection and identification. From this time, 0.14 min was required to identify the gross anatomical regions present on the cdrrs, find the reference landmark for vertebra identification and detect the vertebrae on both sagittal and coronal cdrr. The user who obtained the longest average total time of 3.45 min repeated the analysis of the 37 datasets a total of 3 times with at least 2 weeks apart. As mentioned above, success rate variations were small in the intra-observer study. However, the repeated analysis of the 37 datasets by the same user demonstrated a learning bias with the time required for manual positioning of the cylinder model decreasing from 1.68 to 1.05 min. 115

135 Figure 4-8: Compilation of the average time required to perform the semi-automatic detection and identification of all imaged vertebrae for 37 patients enrolled in this study (user 1, inter-observer study). The error bars represent 1 standard deviation. The numbers at the extremity of each bar of the histogram correspond to the task number on the flow chart presented in Fig For the Detection/Identification tasks, the grey section of the histogram bar corresponds to the detection and identification process (8 s on average) while the white section represents the time required to display the images and save them for record (17 s on average). The bar sections in white represent time that could be eliminated with a closer integration of the vertebra finder (VF) with treatment planning system (TPS). The total process duration is compiled with and without this extra time. 116

136 4.5 Discussion Computed-aided diagnosis (CAD) schemes involving detection and/or segmentation of vertebrae have been applied for mineral bone density measurements 18, low back pain diagnosis 19 and fracture detection 14. In this work, a method for semi-automatic detection and identification of vertebrae on CBCT images was developed for on-line palliative radiotherapy of spinal metastases. The outputs of the VF are the center coordinates and the name (L5-C1) of the vertebral bodies detected in a CBCT dataset. This vertebra detection method was applied on curved 2D projection images (cdrr) generated from a portion of a 3D CBCT dataset and was inspired from the detection of vertebral fractures on lateral radiographs 14. Both the VF and Kasai et al detection methods begin with an edge enhancement step. For the purpose of vertebra center localization, the Kasai et al edge detection method based on multiple thresholding was replaced by a straightforward detection of minima of a cumulative gradient curve (Fig 4-5b). In addition to being fast and easy to implement, this approach also allowed for a simplification of the complex rule-based feature analysis proposed by Kasai et al for elimination of falsepositives candidates. Indeed, the VF seemed less sensitive than the fracture detection algorithm 14 to the presence of other surrounding anatomical structures (vessel calcification, diaphragm or notches in vertebrae), which reduced the search for false-positive vertebra center candidates to a center-to-center distances comparison. The features detected with these methods being different (vertebra center vs. vertebra top and bottom edge detection), their detection rates cannot be compared directly. However, the average success rate of 89.9% to 92.8% obtained with the VF (inter-observer study, 27 patients, all spine levels) measured up well with the detection rate of 70.9 to 76.6% reported by Kasai et al for the detection of vertebra edges. 117

137 Furthermore, the validation of the VF was performed for all spine levels (L5 to C1) whereas the vertebra fracture detection was limited to the thoracic spine. The CAD schemes designed for mineral bone density measurements 18 and low back pain diagnosis 19 are segmentation methods with the initialization step (initial vertebra position detection) performed mostly manually. For example, in the method proposed by Mastmayer et al, the user first placed points at the center of the vertebrae on sagittal and coronal slices generated from a helical CT dataset. The detection of the inter-vertebral space was then performed semi-automatically on the sagittal slice using the user-defined points and an absolute greyscale value threshold (inter-vertebral space should be within 0 to 200 Hounsfield units, HU). For helical CT datasets, this method required manual adjustments for 20% of normal vertebrae and 38% of degenerated vertebrae 18. For vertebra detection on CBCT datasets, this detection algorithm would not be suitable due in particular to the inaccurate CT-number representation of CBCT images 9, 11, 12. The sensitivity of the VF to the lack of reproducibility and accuracy of the CBCT-number representation was minimized in our implementation by performing the detection of the vertebrae and the anatomical landmarks used for vertebra identification on gradient images. In addition to inter-scan CBCT number variation, the variation of the CBCT greyscale values within a same scan would also limit the application of thresholding method based on absolute greyscale. These intra-scan greyscale variations were observed in particular for head and neck patients and were due the patient scatter contribution changing rapidly with the patient cross-section (shoulders vs. neck) 11. The intra-scan greyscale variations appeared as a low-frequency component on the cumulative gradient curve used by the 118

138 VF for the vertebra detection (Fig. 4-5b) and was efficiently subtracted in our method using a local median filter. The application of on-line radiotherapy planning requires the identification by name of the vertebrae imaged in a given dataset in order to target the right vertebra for irradiation. In this context, the identification algorithm of the VF represents a novelty compared to the other applications and algorithms described above 14, 18, 19. The VF performed the identification of the vertebrae imaged in a given CBCT dataset based on the automatic detection of at least one of 4 different reference landmarks (base of skull, apices of the lungs, T12-ribs and pelvic bones). The CBCT field of view in SI direction (maximum of 25.6 cm) was sufficient to capture at least one of these reference landmarks for all 37 patient datasets used in this study to design and validate the algorithm. The detection of the sacrum-l5 junction required on average larger user adjustment than the other landmarks due to the normal bony anatomy variation among patients. The apices of the lungs were the only soft tissue reference landmark used for the identification of the vertebrae. Although not rigidly attached to the spine, the apices of the lungs in conjunction with the T1-ribs visible on the coronal cdrr proved to be a reliable surrogate for the identification of C7-T1 inter-vertebral space. The approach of sagittal and coronal cdrrs using a sub-volume of the CBCT data was valuable as it provided unambiguous representations of the vertebral bodies and reference landmarks for the detection algorithm. Additionally, it proved to be an original and efficient method of presenting the data for user approval. With the sagittal and coronal cdrrs available for review after automatic detection, the user did not have for example to scroll through the 119

139 axial slices of the 3D dataset to verify the VF results. The visibility of reference landmarks used for vertebra identification as well as the vertebral bodies on these projection images allowed the user to detect and correct manually the VF occasional failures before approval. The high average success rate (92.8% and 89.9% on sagittal and coronal cdrrs, respectively) of the VF given the CBCT low contrast resolution 9, 11, 12 raises an interesting question regarding the importance of image quality. The performance of the VF seems to be influenced in particular by the slice thickness of the volumetric CBCT. The algorithm was applied to a small number of patient CBCT datasets reconstructed with a slice thickness of 2 mm and the incidence of false negatives increased systematically for all cases. Similar results were obtained for these patients when the semi-automatic vertebra detection and identification algorithm was applied to their corresponding planning helical CT. The impact of improved image contrast resolution (CT vs. CBCT) on the vertebra detection seemed to be counterbalanced by the lower spatial resolution in SI direction of the reference planning CT (slice thickness of 3 mm) compared to CBCT. The systematic validation of the VF on helical CT datasets was not pursued due to the slice thickness limitation and is planned for the next phase of this clinical study for which planning CT with 1 mm slice thickness will be acquired. The deterioration of bony anatomy for patients with spinal metastases also negatively affected the performance of the VF for the vertebra detection performed on sagittal cdrrs (minimum of inter-observer study: 87.5%). The low bone density of some patients even without spine disease contributed as well to flatten the shape of the cumulative gradient curve (Fig. 4-5b) and introduced false positives and negatives. 120

140 Future steps in the development of the next version of VF include the improvement of the communication with the TPS. In the implementation reported here, the VF takes a total of 0.70 min (average) to upload the CBCT dataset and ROI information and to save the detection results in various files for subsequent analysis (see Fig. 4-8). A tighter integration with the TPS would reduce the total application time obtained by user 1 from 3.45 to 2.75 min on average. Moving forward, the VF will be implemented in C++ to facilitate its interface to the TPS and speed up its execution. The duration of the semi-automatic vertebra detection and identification could be further reduced by replacing the manual positioning and adaptation of the cylinder model by a semi-automatic process. The VF could position the cylinder model based on the patient s external contour 14, the normal location of the spine and the image greyscale values. The VF detection and identification results showed occasional failures. A method of sensing the quality of the detection would be useful. The VF could potentially be made more robust and reliable by using an atlas that contains typical inter-vertebral spaces and vertebra heights. Once the VF had detected and identified the vertebrae imaged in the dataset, the results could be compared to database for example in terms of vertebra height. This comparison might facilitate the detection of outliers, which reflect false negatives and positives. The use of bony anatomy atlas could also be used to extrapolate the position of the C1 vertebra (often missed by the VF due to its small size) based on the detected position of the C2 vertebra. Finally, the introduction of a confidence index derived from the amplitude of the minima of the cumulative gradient curve (Fig. 4-5b) could be calculated for each vertebra detected. This index could provide a measure of vertebra deformation and prevent the elimination of well-detected vertebra candidates based solely on the normal anatomy database. 121

141 4.6 Conclusion A semi-automatic vertebra detection and identification algorithm was developed for online treatment planning on CBCT images. The projection of partial volumetric CBCT data onto curved planes mimicking the curvature of the patient spine represents a key component of the detection and identification algorithm. The cdrrs provided a clear representation of the anatomy of interest suitable for successful automatic detection and for user approval in the presence of disease-related bone deterioration. The success rate of the VF was high and depended in particular on the high spatial resolution of CBCT dataset and the apparent contrast of the bony anatomy. The use of 4 different reference landmarks distributed along the spine for vertebra identification overcame the imaging restriction imposed by the limited field of view of CBCT in the SI direction. The use of the VF in conjunction with an automatic segmentation algorithm 6, 7 will streamline the on-line planning and delivery technique for palliative radiotherapy and enable an efficient clinical implementation of the on-line treatment method. 4.7 Acknowledgement The authors would like to thank Winnie Li, and Harald Keller, (Princess Margaret Hospital) for their support and helpful discussions. This work was supported in part by Philips Medical Systems, Elekta Inc and grants from the Ontario Consortium for Image Guided Therapy and Surgery and Ontario Research & Development Challenge Fund. 122

142 4.8 References 1. D. Letourneau, R. Wong, D. Moseley, M. B. Sharpe, S. Ansell, M. Gospodarowicz, D. A. Jaffray, "Online planning and delivery technique for radiotherapy of spinal metastases using cone-beam CT: image quality and system performance," Int J Radiat Oncol Biol Phys. 67, (2007). 2. ICRU. Determination of Absorbed Dose in a Patient Irradiated by Beams pf X or Gamma Rays in Radiotherapy Procedures, Report No. 24. Washington, DC: International Commission on Radiation Units and Measurements; IAEA. Absorbed Dose Determination in Photon and Electron Beams. An International Code of Practice, Report Series No Vienna: International Atomic Energy Agency; J. Van Dyk, R. B. Barnett, J. E. Cygler, P. C. Shragge, "Commissioning and quality assurance of treatment planning computers," Int J Radiat Oncol Biol Phys. 26, (1993). 5. J. Venselaar, H. Welleweerd, B. Mijnheer, "Tolerances for the accuracy of photon beam dose calculations of treatment planning systems," Radiother Oncol. 60, (2001). 6. V. Pekar, T. R. McNutt, M. R. Kaus, "Automated model-based organ delineation for radiotherapy planning in prostatic region," Int J Radiat Oncol Biol Phys. 60, (2004). 7. D. Létourneau, M. Kaus, D. Grabarz, R. K. S. Wong, M. K. Gospodarowicz, D. A. Jaffray, "Semi-Automatic Vertebrae Localization and Segmentation for Online Palliative Radiotherapy," Int J Radiat Oncol Biol Phys. 66, S645 (2006). 8. M. R. Kaus, V. Pekar, C. Lorenz, R. Truyen, S. Lobregt, J. Weese, "Automated 3-D PDM construction from segmented images using deformable models," IEEE Trans Med Imaging. 22, (2003). 9. J. H. Siewerdsen, D. A. Jaffray, "Cone-beam computed tomography with a flat-panel imager: effects of image lag," Med Phys. 26, (1999). 10. D. A. Jaffray, J. H. Siewerdsen, "Cone-beam computed tomography with a flat-panel imager: initial performance characterization," Med Phys. 27, (2000). 11. J. H. Siewerdsen, D. A. Jaffray, "Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter," Med Phys. 28, (2001). 123

143 12. D. Letourneau, J. W. Wong, M. Oldham, M. Gulam, L. Watt, D. A. Jaffray, J. H. Siewerdsen, A. A. Martinez, "Cone-beam-CT guided radiation therapy: technical implementation," Radiother Oncol. 75, (2005). 13. D. A. Jaffray, J. H. Siewerdsen, J. W. Wong, A. A. Martinez, "Flat-panel cone-beam computed tomography for image-guided radiation therapy," Int J Radiat Oncol Biol Phys. 53, (2002). 14. S. Kasai, F. Li, J. Shiraishi, Q. Li, K. Doi, "Computerized detection of vertebral compression fractures on lateral chest radiographs: preliminary results with a tool for early detection of osteoporosis," Med Phys. 33, (2006). 15. R. W. Prokesch, C. H. Coulam, L. C. Chow, R. Bammer, G. D. Rubin, "CT angiography of the subclavian artery: utility of curved planar reformations," J Comput Assist Tomogr. 26, (2002). 16. T. Vrtovec, B. Likar, F. Pernus, "Automated curved planar reformation of 3D spine images," Phys Med Biol. 50, (2005). 17. R. M. Haralick, L. G. Shapiro. Computer and Robot Vision. Vol II: Addison-Wesley; A. Mastmeyer, K. Engelke, C. Fuchs, W. A. Kalender, "A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine," Med Image Anal. 10, (2006). 19. Y. Zheng, M. S. Nixon, R. Allen, "Automated segmentation of lumbar vertebrae in digital videofluoroscopic images," IEEE Trans Med Imaging. 23, (2004). 124

144 Chapter 5 SPINE INTRA-FRACTION MOTION 125

145 5.1 Introduction In conventional radiotherapy, geometric uncertainty of dose placement is due in part to interfraction setup error, as the patient position defined at the time of simulation and planning might not be reproduced exactly at the time of treatment. With the application of image-guidance technologies in radiotherapy 1-6, inter-fraction setup error can be reduced substantially by imaging the patient just prior to treatment and shifting the couch accordingly. Residual patient positioning errors after image-guided setup pertains to the limited mechanical accuracy of the image-guidance system, rotational setup errors not corrected with couch shifts, organ deformation, and patient intra-fraction motion. Contrary to conventional radiotherapy, the online planning and delivery technique developed in this work has the potential to be free of inter-fraction setup error as the patient position is defined at the treatment unit and the images used for planning are acquired at the beginning of the treatment session. In this context, the only source of residual patient positioning error is related to patient intra-fraction motion between the cone-beam CT (CBCT) acquisition and the treatment delivery. Although radiotherapy patients are generally compliant to treatment instructions given by the therapists, patients with spinal metastasis usually have pain 7 and might shift themselves during the on-line treatment process (30 min or more) to relieve pressure points and reduce discomfort. A reasonable approach to take into account intra-fraction setup errors consists of using appropriate geometric safety margins around the target volume 8, 9. The design of a population 126

146 10, 11 margin requires the knowledge of the distribution of intra-fraction motion in three dimensions for the spinal metastasis patients treated in radiotherapy. In a second approach, intra-fraction displacement of the patient s spine can also be corrected using CBCT image guidance. In this strategy, a second CBCT dataset would be acquired right before treatment and registered to the planning CBCT to determine the couch shift corrections. The image guidance technique is advantageous in limiting the size of the safety margins but does add more time to the on-line treatment process. In order to avoid this time expenditure, other measures, such as, optical tracking of surface markers taped on the patient skin could be employed to detect gross intra-fraction motion and replace the second CBCT scan for patient setup correction. Optical tracking of surface markers could also be used in conjunction with CBCT for setup correction. In this mode of operation, marker displacements would serve as a criterion for the acquisition of a CBCT verification scan. Optical tracking systems have previously been used in radiotherapy and radiosurgery mainly for patient setup assistance and breathing (chest wall) motion measurement for 4D CT acquisition 20 and gating of radiotherapy treatments The primary objective of this work is to measure the spine intra-fraction motion for patients with spinal metastases on CBCT images. This result will be used to estimate a population margin for spine intra-fraction motion for this group of patients. As a second objective, the use of optical tracking of surface markers as a surveillance method for spine intra-fraction motion will be assessed. The quality of the surface marker as a surrogate for spine displacement will be determined by correlating surface marker displacements to spine intra-fraction motion measured on CBCT. The inaccessibility of the spine for marker attachment in supine position, the high degree of deformability of abdominal soft tissues and physiologic motions such as breathing are 127

147 all factors that can work to reduce the strength of this correlation. Finally, a statistical framework inspired by Lam et al 24 was developed to assess the potential of optical tracking of surface markers as a surrogate for spine position correction. The aim of this statistical analysis was to establish if the surface marker displacements can be used to correct the patient position for different action levels or can be used as a decision criterion for the acquisition of a verification CBCT scan. 5.2 Methods and Material System Description The optical tracking system (OTS) used in this study is similar to the systems used for patient setup assistance for radiosurgery and radiotherapy treatments Briefly, it consists of two stereoscopically arranged CCD infrared cameras (Polaris, Northern Digital Inc, Ontario, Canada) mounted on the ceiling of the treatment room above the treatment couch at approximately 2 m from the treatment unit isocenter (Figure 5-1). The camera system can detect the position of passive infrared reflectors or active infrared sources. In the reflective detection mode, a ring of light emitting diodes (LED) located around each camera serve as an infrared illumination source. The camera signal is transmitted via serial connection to a personal computer running an in-house software written in C++. The position of the reflective markers is obtained by stereoscopic vision and triangulation. The field of view of the camera system is about 1 x 1 m 2 at the isocenter with a field depth of 1 m. The accuracy specified by the manufacturer for the position detection of reflective markers is ±0.35 mm. Assessment of the 128

148 OTS performed in a previous study 25 demonstrated a detection accuracy of ± 0.1 mm for displacements of 1 to 10 mm. Spherical reflective markers of 1 cm diameter used in this study for monitoring of patient intrafraction motion are shown in Fig 5-2. The reflective markers can be taped individually on the patient s skin or mounted in groups on a rigid plastic tool, which is in turn secured to the patient s skin with tape. For the rigid tool with 4 markers, the system returns the angular position of the tool and the location of its center of mass with respect to the treatment unit isocenter as a function of time. A calibration of the OTS performed each morning with a test tool located at the treatment unit isocenter produces a transformation matrix, which is used to convert the marker positions from the camera to the treatment unit coordinate system 26. For the tracking of individual markers, the system returns the location of each marker in 3D space as a function of time. Up to 5 reflective markers can be tracked simultaneously with a monitoring rate of 30 Hz. The X (right to left, RL), Y (anterior to posterior, AP) and Z (superior to inferior, SI) coordinates (see Fig 5-1) of the tool or individual markers are displayed in real-time on the computer screen both numerically and graphically in the form of a moving trace. The software allows the user to record the marker position information in a text file as a function of time. 129

149 Figure 5-1: Schematic of the optical tracking system (OTS). Figure 5-2: Two different types of reflective surface markers used in this study. 130