Figure 1: Radiation beams through a multi-leaf collimator(mlc)

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1 Sabbatical Proposal Athula Gunawardena Department of Mathematical and Computer Sciences Algorithms and Treatment Quality Comparisons in Intensity Modulated Radiation Therapy 1. Objectives and Project Plan A. Sabbatical/fellowship project objectives Intensity Modulated Radiation Therapy (IMRT), introduced in the mid-1990s, is an advanced mode of high-precision radiotherapy that utilizes a linear accelerator and a computer-controlled multi-leaf collimator (MLC) to deliver precise radiation doses via a collection of patient-specific beam apertures (Figure 1). Figure 1: Radiation beams through a multi-leaf collimator(mlc) These apertures focus the radiation on the malignant tumor or specific areas within the tumor while minimizing radiation exposure to surrounding normal tissues. This research focuses on algorithms for and comparisons of treatment plans of traditional IMRT and Intensity Modulated Arc Therapy (IMAT), a recently developed advanced treatment modality that is a form of IMRT. The specific goals for this proposal are: (1) to exploit a high-throughput computing (HTC) environment (e.g., via software provided by the CONDOR project at the University of Wisconsin-Madison, freely available) for intensity-map segmentation algorithmic development and to generate multiple IMRT and IMAT plans under a fixed set of treatment plan parameters for given prostate, head and neck and pancreas cases, and (2) to develop a plan comparator to aid in the ranking of the IMRT and IMAT treatment plans generated in the multi-plan framework. B. Procedures to be used to accomplish each objective In practice, Intensity Modulated Radiation Therapy can be delivered in two ways, namely, fixed gantry and rotational. Fixed-gantry IMRT (i.e., traditional) is achieved by delivering over- lapping fields from a small number of fixed beam directions. Rotational IMRT is achieved by dynamically changing the aperture shapes as the gantry moves around the patient in one or more sweeps along an arc during radiation delivery. IMAT, an alternative rotational IMRT delivery technique, is 1

2 performed with a conventional linear accelerator(linac), and the large set of tungsten leaves of the MLC is used to change the shape of the aperture as the gantry rotates during delivery. The treatment is delivered along arcs with a single sweep or multiple sweeps, each with a start and stop position and the patient remains stationary during the delivery process. The MLC field shape changes continuously during gantry rotation from the beginning to the end of each arc. The multiple overlapping arcs provide multiple apertures at each angle which thereby achieve a modulated intensity distribution from a given delivery angle. M a n u a l B e a m P e n c i l B e a m I n t e n s i t y M a p A p e r t u r e w e i g h t A n g l e S e l e c t i o n D o s e C a l c u l a t i o n S e g m e n t a t i o n o p t i m i z a t i o n a n d ( s i n g l e p l a n I M R T o r I M A T ) a n d o p t i m i z a t i o n f o r t h e s e l e c t e d a n g l e s f i n a l O A R / P T V d o s e c a l c u l a t i o n s Figure 2: Present IMRT planning system We can represent a presently available commercial RTP system for IMRT/IMAT by the block diagram shown in Figure 2. Note that commercial IMAT is only available in a single sweep case (Varian Rapid Arc, Elekta VMAT) and, due to optimization complexity, multiple arc IMAT as covered in this investigation is still under research and development. Both IMRT and IMAT can be delivered with a conventional LINAC with the capability for dynamic delivery. According to Figure 2, a clinician chooses either IMRT or IMAT subjectively. Then a treatment planner chooses a set of beam angles manually and goes through very time consuming steps in: (1) pencil beam dose calculation/optimization, (2)intensity map segmentation, and (3) aperture weight otimization and final dose calculation. A short term goal of our proposed research is to provide algorithms and decision support systems that will allow clinicians with comparative effectiveness data to choose between IMRT and IMAT for a given tumor site. Our long term goal is to improve (i.e., avoid human errors) the present RTP system by developing a fully automated RTP system as shown in Figure 3. In this direction, the proposed research will develop an I-Map segmentation module and a plan comparator for a future automated RTP system based on HTC. P e n c i l b e a m d o s e c a l c u l a t i o n s f o r p o s s i b l e a n g l e s B e a m a n g l e s e l e c t i o n a n d p e n c i l b e a m d o s e o p t i m i z a t i o n I n t e n s i t y M a p S e g m e n t a t i o n ( m u l t i p l e I M R T / I M A T p l a n s ) A p e r t u r e w e i g h t o p t i m i z a t i o n a n d f i n a l O A R / P T V d o s e c a l c u l a t i o n s P l a n C o m p a r a t o r Figure 3: Future Fully Automated IMRT/IMAT Planning System We plan to parallelize and implement our algorithms in Condor which is a HTC platform. The Condor software and complete documentation is freely available from the Condor project s website at URL Our goal is to produce high quality IMRT and IMAT treatment plans under comparable input parameters for a given tumor site and compare them by using a clinically meaningful plan comparator. We plan to use Pinnacle software on Varian machines to generate intensity maps for our RTP plans. These maps will be generated at the University of Maryland through collaboration with Professor Warren D Souza. For intensity map segmentation, we plan to implement our own software and generate results as proposed below. 2

3 1. We plan to use the HTC environment available in CONDOR and build an efficient and robust system that is capable of producing high quality segmentations for both traditional IMRT and IMAT plans. The top-down structure for plan generation through this system is given in Figure 4. I M R T / I M A T I n p u t : I M a p s I M R T I M A T P r o s t a t e H e a d / N e c k P a n c r e a s P r o s t a t e H e a d / N e c k P a n c r e a s Figure 4: The top-down structure of the IMRT/IMAT plan generation We propose to use 3 tumor sites, prostate, head/neck, and pancreas. At the top level (IMRT/IMAT) in Figure 4, we receive input data (i.e., I-Maps, plan type, tumor identity) and deliver output. Depending on the plan type, IMRT/IMAT module provides patient data to the IMRT or IMAT module at the second level. At the third level, we develop 6 sub-modules (3 for IMRT, 3 for IMAT) to work with each type of tumor. These 3rd level IMRT and IMAT sub-modules will be developed by using our previously published algorithms. These frameworks have flexibility to allow us to incorporate different strategies and segment counts. Thus each 3rd level sub-module will be refined to the 4th level sub-modules (leaf arrows) which represent plans generated with different segment counts for segmentations (see Table 1 for an example). Those leaf sub-modules can be run simultaneously in a HTC platform and produce a collection of plans which is used as input for a plan evaluator. 2. For a given disease site, we use the IMRT/IMAT segmentation module to generate the IMRT and IMAT plans exemplified in Table 1. We have selected our parameters so that each row in Table 1 has comparable IMRT and IMAT plans with respect to segment count (the number of apertures). These plans correspond to the 4th level sub-modules in Figure 4 and will be used to decide the best treatment for a given disease site by using a plan evaluator. Table 1: Segment count (# of sweeps in IMAT) K for IMRT plans with the number of beam angles P = 5,7,9 comparable to an IMAT plan with P = 36 Segment Count (# of sweeps) K IMAT IMRT P = 36 P = 5 P = 7 P =

4 The plan evaluator will consider cutoffs in terms of dose-volume levels for the most important organ-at-risk (OAR)(s). Following that we will consider the level of overdose (or underdose) for the next most important OAR. Such importance assignments will be based on disease sites. For example, in the case of a pancreas case where the OARs are liver, kidneys, spinal cord and stomach, the kidneys would be considered the most important OAR due to their proximity to the pancreas followed by the spinal cord due its function. In another objective, weighting coefficients would be assigned to each OAR (based on their level of importance) and the resulting weighted sum of the underdose (positive score) or overdose (negative score) would be considered. Quadratic penalties could also be used in this context if large deviations from targets are to be highly penalized. C. Schedule The proposed project is planned to be completed in a 17 week period during Spring 2012 as shown in the following table. Week 1 - Week 6 Week 7 - Week 12 Week 13 - Week 17 Develop and implement Test the segmentation module Extend the module to other HTC parallel algorithms. for efficiency and robustness. tumor sites. Obtain preliminary results Develop and implement Prepare papers with one specific site. a plan evaluator. for reporting the results. D1. Reading List Ahuja, R. K. and H. Hamacher, 2005: A network flow algorithm to minimize beam-on time for unconstrained multileaf collimator problems in cancer radiation therapy. Networks, 45, Alber, M. and R. Reemsten, 2007: Intensity modulated radiotherapy treatment planning by use of a barrier-penalty multiplier method. Optimization Methods and Software, 22(3), Bentel, G., 1995: Radiation Therapy Planning, Second Edition. McGraw-Hill Professional. Boland, N., H. W. Hamacher, and F. Lenzen, 2004: Minimizing beam-on time in cancer radiation treatment using multileaf collimators. Networks, 43, Bortfeld, T. and S. Webb, 2009: Single-arc imrt? Physics in Medicine and Biology, 54, N9 N20. Bortfeld, T. R., D. L. Kahler, T. J. Waldron, and A. L. Boyer, 1994: X-ray field compensation with multileaf collimators. Int. J. Radiat. Oncol. Biol. Phys., 28, Boyer, A. L. and C. X. Yu, 1999: Intensity-modulated radiation therapy with dynamic multileaf collimators. Semin. Radiat. Oncol., 9, Bratengeier, K., 2005: 2-step imat and 2-step imrt: A geometrical approach. Med. Phys., 32(3), Carol, M., D. Dawson, and R. Spied, 1997: A binary volume delivery system temporal-based intensity modulation radiation therapy. Med. Phys., 24, Chui, C. S., T. LoSasso, and S. Spirou, 1994: Dose calculation for photon beam with intensity modulation generated by dynamic jaw or multileaf collimation. Med. Phys., 21,

5 D.Souza, W. D., R. R. Meyer, and L. Shi, 2004: Selection of beam orientations in intensity modulated radiation therapy using single-beam indices and integer programming. Physics in Medicine and Biology, 49, D.Souza, W. D., H. H. Zhang, D. P. Nazareth, L. Shi, and R. R. Meyer, 2008: A nested partitions framework for beam angle optimization in intensity-modulated radiation therapy. Physics in Medicine and Biology, 53, Emami, B., J. Lyman, A. Brown, L. Coia, M. Goitein, J. E. Munzenrider, B. Shank, L. J. Solin, and M. Wesson, 1991: Tolerance of normal tissue to therapeutic irradiation. Int. J. Radiat. Oncol. Biol. Phys., 21, Engel, K., 2003: A new algorithm for optimal multileaf collimator field segmentation. TR D-18051, University of Rostock, Germany. Ferris, M. C., R. Einarsson, Z. Jiang, and D. Shepard, 2006a: Sampling issues for optimization in radiotherapy. Annals of Operations Research, 148, Ferris, M. C., J.-H. Lim, and D. M. Shepard, 2003: Optimization approaches for treatment planning on a Gamma Knife. SIAM Journal on Optimization, 13, Ferris, M. C., R. R. Meyer, and W. D.Souza, 2006b: Radiation treatment planning: Mixed integer programming formulations and approaches. In Appa, G., L. Pitsoulis, and H. P. Williams, eds., Handbook on Modelling for Discrete Optimization, springer, New York, NY, pp Gopal, R. and G. Starkschall, 2002: Plan space: representation of treatment plans in multidimensional space. Int. J. Radiat. Oncol. Biol. Phys., 53, Gunawardena, A. and R. R. Meyer, 2008: Discrete approximations to real-valued leaf sequencing problems in radiation therapy. Discrete Applied Mathematics, 156(17), Gunawardena, A. D., W. D. D.Souza, L. D. Goadrich, R. R. Meyer, K. J. Sorensen, S. A. Naqvi, and L. Shi, 2006: A difference-matrix metaheuristic for intensity map segmentation in step-and-shoot IMRT delivery. Physics in Medicine and Biology, 51, Gunawardena, A. D. A., M. C. Ferris, and R. R. Meyer, 2010: A network approach for intensity modulated arc therapy. Optimization Methods and Software, Submitted. Hamacher, H. W. and K.-H. Kufer, 2002: Inverse radiation therapy planning. a multiple objective optimization approach. Discrete Applied Mathematics, 118, Kuijper, I. T., M. Dahele, S. Senan, and W. F. A. R. Verbakel, 2010: Volumetric modulated arc therapy versus conventional intensity modulated radiation therapy for stereotactic spine radiotherapy: A planning study and early clinical data. Radiotherapy and Oncology. Langer, M., V. Thai, and L. Papiez, 2001: Improved leaf sequencing reduces segments or monitor units needed to deliver IMRT using multileaf collimators. Med. Phys., 28, Mell, L. K., A. K. Mehrotra, and A. J. Mundt, 2003: A survey of intensity-modulated radiation therapy use in the united states. Cancer, 98, Meyer, R. R., H. H. Zhang, L. Goadrich, D. P. Nazareth, L. Shi, and W. D. D.Souza, 2009: A multi-plan treatment planning framework: A paradigm shift for imrt. Int. J. Radiat. Oncol. Biol. Phys., 68(4),

6 Mould, R. F., 1993: A Century of X-Rays and Radioactivity in Medicine: With Emphasis on Photographic Records of the Early Years. Institute of Physics Publishing. Otto, K., 2008: Volumetric modulated arc therapy: Imrt in a single gantry arc. Med. Phys., 35, Romeijn, H. E., R. K. Ahuja, J. F. Dempsey, and A. Kumar, 2005: A column generation approach to radiation therapy treatment planning using aperture modulation. siopt, 15(3), Thieke, C., K. Kufer, A. Monz, F. Scherrer, U. Alonso, P. Oelfke, J. Huber, T. Debus, and T. Bortfeld, 2009: A new concept for interactive radiotherapy planning with multicriteria optimization: First clinical evaluation. Radiotherapy and Oncology, 85(2), Wang, C., S. Luan, G. Tang, D. Z. Chen, M. A. Earl, and C. X. Yu, 2008: Arc-modulated radiation therapy (amat): a single-arc form of intensity-modulated arc therapy. Physics in Medicine and Biology, 53, Webb, S., 1994: Optimizing the planning of intensity-modulated radiotherapy. Physics in Medicine and Biology, 39, Wu, Q., R. Mohan, A. Niemierko, and R. Schmidt-Ullrich, 2002: Optimization of intensity-modulated radiotherapy plans based on equivalent uniform dose. Int. J. Radiat. Oncol. Biol. Phys., 52, Wu, Q. J., F. F. Yin, R. McMahon, X. Zhu, and S. K. Das, 2010: Similarities between static and rotational intensity-modulated plans. Physics in Medicine and Biology, 55, Xia, P. and L. J. Verhey, 1998: Multileaf collimator leaf sequencing algorithm for intensity modulated beams with multiple static segments. Medical Physics, 25, Yu, C. X., 1995: Intensity-modulated arc therapy with dynamic multileaf collimation: an alternative to tomotherapy. Physics in Medicine and Biology, 40, D2. Conferences The investigator plans to present the outcomes of this research at the Institute of Operations Research and Management Sciences (INFORMS) annual meeting to be held in Phoenix, Arizona from October 14, October 17, E. Grant Proposals to be submitted The investigator is presently coordinating with the Research and Sponsored Programs office at UW-Whitewater to submit two extramural grant proposals related to the proposed research to the following programs. 1. Operations Research program, National Science Foundation, Deadline: February 15, (This is a resubmission. The previous proposal received 4 Good ratings.) 2. Academic Research Enhancement Award program, National Institute of Health, Deadline: February 25,

7 2. Rationale for the Project A. Relationship to faculty member s teaching and scholarship The investigator believes that his teaching and scholarship will benefit tremendously from this sabbatical opportunity. The recent tack in computing towards ubiquitous parallelism raises several challenges for computer science educators, mostly related to how we can best prepare students for a world where parallel resources are always available. The proposed research helps investigator update his skills and tools to introduce classroom projects for these newer platforms. The scholarship opportunity is especially exciting because it allows the investigator to effectively utilize his training in Computer Science and Mathematics for an interdisciplinary cutting edge research project. The investigator has collaborated with several experts in this field and developed segmentation algorithms for both IMRT and IMAT. This opportunity will allow continuation and intensification of these collaborations. B. Relationship to faculty member s long-term professional plans One of the investigator s long-term professional plans is to engage in groundbreaking research through extramural grant support from NIH, NSF, and other funding agencies. The outcomes of this sabbatical research will make the investigator s future NSF or NIH grant applications more competitive. The increase in the investigator s research productivity during this sabbatical period would contribute positively towards the scholarly activity requirement for promotion to Full Professor at UW-Whitewater. C. Relationship to the goals and/or priorities of the department, college, and university The proposed research crosses boundaries of several traditional disciplines at the University of Wisconsin-Whitewater (UWW), namely, Computer Science, Mathematics, Management Computer Systems, Biology, and Physics. Thus, the investigator plans to convey its results through seminars and class projects to a wide audience of science and mathematics students. Some of the benefits to UWW are industry oriented undergratuate research, curricular impact, and contribution of new research tools to solve compute-intensive problems. The investigator is the regular instructor for the COMPSCI 412 (Computer Organization and Systems Programming), and MCS 475 (Network Engineering) courses in the department. Since Condor (binaries and source codes are available) is a parallel computing platform, the investigator plans to design and integrate Condor project modules (user and systems) to the UWW computer science curriculum including the above two courses. 3. Project Evaluation The specific contribution of this proposed research is to provide clinicians with comparative effectiveness data to help clinicians choose the better modularity from IMRT and IMAT for a given tumor site. Hence the results produced in this research may contribute to improve the present IMRT framework and help clinicians produce plans that provide a better quality of life for patients after their treatments. The investigator plans to produce two research papers (algorithms and medical physics) for publication in reputed peer reviewed journals. The investigator plans to present the results of this research at the department colloquium and the Institute of Operations Research and Management Sciences (INFORMS) annual meeting to be held in Phoenix, Arizona from October 14, October 17,