Light dosimetry for Low-Level Laser therapy: Accounting for differences in tissue and depth

Light dosimetry for Low-Level Laser therapy: Accounting for differences in tissue and depth Robert Weersink a, Roger White b, Lothar Lilge c a Laboratory for Applied Biophotonics, University Health Network, 61 University Ave, Toronto; b Theralase Inc. Markham Ont. c Ontario Cancer Institute, 61 University Ave, Toronto ABSTRACT While Low-level Light Therapy (LLLT) has demonstrated efficacy for certain indications, some aspects of the technology are still controversial. Clinical studies on LLLT range from low quality anecdotal studies to blinded, randomied, control clinical studies. These have used a variety of wavelengths, optical powers and variations in other laser parameters. While these studies show a large range in treatment outcome, comparison of treatment efficacy between these studies with respect to light dose is all but impossible since the light dose characteriation in the LLLT field has not been properly defined and is not standardied. Surface irradiance is typically used in the LLLT field as the light dose parameter, ignoring factors such as tissue optical properties, beam divergence, pulsing of the source and tissue thickness to the organ or joint of interest. Drawing on experience with light dosimetry for photodynamic and photothermal therapy, we will provide an overview of light transport and dosimetry in tissue and its implications for LLLT dosimetry. In particular, we suggest that the proper measure of dose is the light fluence rate delivered to the organ or tissue of interest, usually several millimeters below the tissue surface. We have developed a technique that provides an estimate of the subsurface fluence rate based on the diffuse reflectance measured at the tissue surface. Using Monte Carlo simulations and measurements on tissue simulating phantoms, we demonstrate that this technique can be used to predict the subsurface fluence rate to within 3% of the actual value at 3 1 mm below the tissue surface. Keywords: Low level laser therapy, dosimetry 1. INTRODUCTION Low-level Light Therapy (LLLT) has long been known to have curative effects and aid in the healing of numerous ailments in varying species of both humans and animals. 1, 2 The market for therapeutic laser systems is rapidly growing as the understanding and acceptance of this technology is positively supported by clinical evidence of its efficacy in common indications such as carpal tunnel syndrome and arthritis. LLLT generally requires the injured tissue to be exposed directly to the light for predetermined intervals of time, leading to pain reduction and faster healing of the treated tissue. Each disorder or ailment has its own unique treatment protocol, including the light exposure or dose requirements. These dose requirements are determined by the wavelength, intensity and exposure time of the light. The wavelength of the laser light affects its ability to penetrate through overlaying tissues, such as skin, to reach the tissues and molecules of interest. Red light has a higher attenuation than near-infrared (NIR) light in tissue, with a penetration depth (1/e attenuation) less than 1 mm into such tissues, whereas near-infrared (NIR) light can penetrate several millimeters into such tissues. With sufficient incident intensity, effective levels of light can be safely delivered over 1 cm into the tissue. The wavelength of the laser light also affects it ability to promote biological pathways for healing injured tissues. The quantum energy of near-infrared photons is small, and thus near-infrared photons have a relatively low potential to electronically excite biomolecules but may generate thermal gradients across cells. On the other hand, the quantum energy of red wavelength photons is sufficient to achieve electronic excitation of most biomolecules, potentially promoting direct photochemical and photobiological effects in target tissues. 3 Mechanisms for Low-Light Therapy II, edited by Michael R. Hamblin, Ronald W. Waynant, Juanita Anders, Proc. of SPIE Vol. 6428, 64283, (27) 165-7422/7/$18 doi: 1.1117/12.71353 Proc. of SPIE Vol. 6428 64283-1

While LLLT has demonstrated efficacy, some aspects of the technology are still controversial. Clinical studies on LLLT range from low quality anecdotal studies to blinded, randomied, control clinical studies. These studies have used a variety of wavelengths, optical powers and variations in other laser parameters. The bulk of these clinical studies show a positive outcome using laser and non-laser light (such as LEDs) to aid in the healing of tissue ailments. Other studies showed poor to no benefit, in many cases probably due to the use of laser systems incapable of supplying a sufficient dose of photons at the desired wavelength to the tissues of interest at depths below the skin surface. Clinical studies show a large range in treatment outcome, but comparison of treatment efficacy between these studies with respect to light dose is all but impossible since the characteriation of light dose in this field has not been properly defined and is not standardied. It is also apparent to those practicing LLLT that response to light therapy varies widely across the patient population. Factors that appear to affect the tissue response include the age of the patient, weight, metabolism, and cardi-vascular health. While the clinician can assess these patient attributes, he/she can only adjust the light dose delivered to the injured tissue. Again, however, a systematic assessment of the effects these attributes have on LLLT response is hindered by poor characteriation of the delivered light dose. Light dose delivered to the tissue surface is typically used in the LLLT field as the light dose parameter, since it is noninvasive and is the simplest measurement that can be made. However, such a measurement ignores properties of the light source (such as wavelength, beam divergence, pulsing characteristics) and tissue properties (such as optical properties, thickness of tissue to the organ or joint of interest) that affect the light delivery to the injury site. In other therapeutic fields that use radiative fields, dose is assessed by what is delivered to the injured site, (or the clinical treatment volume in the terminology of radiation therapy). By analogy, the proper measure of light dose for LLLT should be the fluence (J/cm 2 ) of light delivered to the organ or tissue of interest, typically located at several millimeters below the tissue surface. In fields such as radiation 4, 5 and photodynamic therapy 6, developing accurate measurements of the delivered dose at the clinically relevant target site is a subject of intense research. Accurately measuring the delivered dose to the clinical target volume enables an assessment of the biological response to the treatment that is independent of the method of light delivery, such as wavelength, beam divergence, etc. These dose response curves are then used to develop standard protocols and plan future treatments. For PDT and photocoagulation therapies, a clear threshold effect is observed, with highly demarcated boundaries between responding and non-responding tissue. In radiation therapy, response can be assessed via biopsies after treatment to confirm that cancer has been eliminated. For these therapies, dose response curves can therefore be created from physical observations of the tissue response. Different tissues and indications will have different dose responses, but with standardied dosimetry, these responses can be accurately measured, and clinicians can implement effective dose delivery prescriptions for each indication and tissue type. For LLLT to gain further clinical acceptance, similar approaches towards treatment delivery will need to be incorporated. As a first step, this will require methods of measuring the light dose at the target site, and not at the tissue surface. Since LLLT is a non-invasive technique, clinically acceptable measurements of the subsurface light dose must also be non-invasive. We have developed a concept for subsurface light dosimetry based on spatially resolved diffuse reflectance measurements of the delivered light. The primary aim of this project is to validate and test this concept using Monte Carlo simulations and tissue-simulating phantom measurements. 2. ONLINE DOSIMETRY: THE CONCEPT We have devised a method of estimating the fluence rate, (W/cm 2 ), at a depth, o, below the tissue or ( o ). The energy density or fluence (J/cm 2 ) delivered to the tissue is ( o ) = ( o )*t, where t is the treatment time. The dosimetry concept requires the monitoring of the diffuse reflectance, R, of the treatment light emitting from the surface as a function of distance from the source light entering the tissue, and then relating this information to the intensity as a function of tissue depth. (Figure 1). Such spatially resolved diffuse reflectance measurements 7 have been used to estimate the optical absorption and scattering properties of tissue with reasonable success for homogeneous tissues incorporating ~1 source-collector separations,, ranging from 1 1 mm, i.e. R(). The original analytical model used to analye this data broke down when used to estimate optical properties of heterogeneous tissue 8, 9, but more complex models that also incorporate frequency domain measurements have since been devised that account for layered tissue. 1 Hence the LLLT dosimetry concept relates the measurement of spatially-resolved diffuse reflectance, R() to the depthdependent fluence rate, (). Since accurate estimates of the optical properties is not required, we anticipate that a reasonable estimate of () will require reflectance measurements at fewer source-collector separations than in calculating the tissue optical properties, despite the complex tissue structures of typical treatment sites. Proc. of SPIE Vol. 6428 64283-2

a) Source Detectors (Reflectance, R) Tissue b) Measure Reflectance Estimate Fluence Rate, =fn(r()) R R a R b Figure 1: The on-line dosimetry concept. a) Measurement arrangement, with surface detectors measuring diffuse reflectance of the treatment light. b) The measured reflectance is indicative of the attenuation of light in tissue, and hence an analytical model can be derived from the measured reflectance that provides an accurate estimate of the fluence rate at any depth below the tissue surface. 3. METHODS 3.1. Monte Carlo Simulations Forward simulations using Monte Carlo techniques have become standard tools in defining light propagation in complex tissues. 11 They are typically used to examine light propagation in the forward direction, i.e. given a light source, the average distribution of the light in the tissue can be calculated. For solving the inverse problem in a timely manner, i.e. estimating () based on measurements of R(), Monte Carlo simulations are computationally too slow. As a method of testing the dosimetry concept, we have run large numbers of MC simulations encompassing a large range of optical properties. For each simulation, R() and () are calculated. Based on this database of calculations, models were derived at each depth, = 2 15 mm at every millimeter, relating () to R(), i.e. () = function(r()). The Monte Carlo simulations were performed using a modified version of the program mcml originally from Wang and Jacques. The original program assumes a semi-infinite tissue with layers along the -dimension and cylindrical symmetry, i.e. results were given for depth and radial distance from a symmetric source. The beam profile and divergence of a Theralase 95 nm diode laser device (TLC1) was measured. These parameters were incorporated into the source term of the Monte Carlo software. The diffuse reflectance and depth resolved fluence information were collected every.5 mm out to 15 mm from source. Two types of tissue were simulated: homogeneous and layered tissue. For homogeneous tissue a large range of optical properties was used. Here the goal was to determine if a model could be built that encompassed a vast range of tissue optical properties: µ a =.2.5 mm -1, µ s =.5 1.7 mm -1. Layered tissue simulations modeled 4 layers of tissue from the epidermis to muscle. Parameters for each layer are given in Table 1 and are taken from recent literature. In this analysis, we have varied those parameters that are likely to vary significantly between subjects, and that will have the most significant influence on the light propagation in tissue. These include: a b o Proc. of SPIE Vol. 6428 64283-3

thickness of fat layer melanin content in epidermis based on typical skin types. For Type I (i.e. northern European), µ a =.2 mm -1 ), for Type IV (i.e. African), µ a = 2.5 mm -1. blood oxygenation in dermis layer from 1% (µ a =.18 mm -1 ) to 1% (µ a =.92 mm -1 ) Table 1: Parameters used for Monte Carlo simulations of layered tissue Layer Name Thickness (mm) µ a (mm -1 ) µ s (mm -1 ) 1 Epidermis.8.2 2.5 1.34 2 Dermis 1..18,.46,.92 1.1 3 Fat 2-1.3 1. 4 Muscle 1.2.7 Once an analytical model was derived relating () to R(), the accuracy of the model was assessed by percentage error: 2 2 1 ˆ p m (1) 2 N m where p is the predicted fluence rate (based on the analytical model), and m is the actual fluence rate (i.e. Monte Carlo simulations). N is the number of samples used in the calculation. 3.2. Phantom Measurements 3.2.1. Phantoms Tissue simulating phantoms were made in 2 layers. The lower solid layer was made of agar gel, containing TiO 2 as the scattering agent and India ink as the absorber. The upper liquid layer consisted of Intralipid (for scattering) and Napthol green (for absorption). Optical properties of each layer are given in Table 2 below. There were nine possible combinations of optical properties for the lower layer, and four possible phantoms for the top layer. All possible combinations of top and lower layer optical properties were measured. Table 2: Optical properties of tissue simulating phantoms. The thickness of the liquid top layer was varied by the Scattering (cm -1 ) Absorption (cm -1 ) separation of the probe head from the surface of the Top Layer 1.5,.2,.5, 2. lower layer. This thickness of the top layer was, 1, 2, or Lower Layer 7, 12, 18.5,.15,.3 5 mm. 3.2.2. Diffuse Reflectance and Fluence Rate Probes Figure 3 shows a schematic of the fluence measurements. Diffuse reflectance measurements were made using a 15-fiber probe. At the proximal end of the probe, each fiber was placed in the specially designed head, with fibers spaced approximately 1 mm apart. At the distal end, each fiber was connected to 1 of 2 8-channel light dosimetry systems with absolute calibration. Depth profiling of the fluence rate in the phantoms was made using an isotropic probe placed in a catheter directly under the laser light source. (C in Figure 2) This fiber was connected to the remaining channel on the light dosimetry units. The catheter was first positioned relative to the top of the lower layer of the phantoms using a manually controlled translation stage (A in Figure 2). The probe/catheter was withdrawn automatically with a computercontrolled actuator for precise positioning (8 m accuracy) (B in Figure 2). The fluence rate was recorded between 2 2 mm below the surface in 1 mm increments. A manually controlled translation stage was used to position the measuring head on the surface of the gel phantoms and at one, two and five millimeters above it (F in Figure 2). Diffuse reflectance measurements were made on the phantom surface with corresponding measurements of the fluence versus depth. Liquid phantom was added to the sample container, and the probe head raised to the next height (1, 2, or 5 mm from the surface). The measurements were then repeated. Considering the combinations of phantoms (upper and lower layer) and depths of the top layer (, 1, 2, & 5 mm), the number of diffuse reflectance surface measurements was approximately 9. Proc. of SPIE Vol. 6428 64283-4

Laser Driver F G E D B C H Figure 2: Schematic of phantom Light Dosimeter Unit measurement. Components are: A) Manual translation stage to move isotropic probe and actuator unit to absolute ero position of the phantom surface; B) computer-controlled actuator Computer Control to move isotropic probe in phantom; C) and Detection istropic probe in catheter; D) phantom; E) probe head with diode laser source A and collection fibers, which is attached to; F) translation stage to move probe head to positions of, 1, 2, or 5 mm from lower layer surface; G) connection from laser driver to laser diode on probe head; and H) diffuse reflectance collection fibers connected to light dosimetry unit. The computer controls the movement of the actuator (B) and collects light intensity date from the diffuse reflectance fibers and the isotropic probe. 4. RESULTS 4.1. Monte Carlo Simulations Predictions of the fluence were best for depths close to the surface and using the largest number of surface detectors. The predicted vs measured fluence rates for a typical example are given in Fig 3. In this case, the fluence rate predictions were made at a depth of 5 mm using 2 reflectance positions. As can be seen the fit is excellent, with r 2 =.97 and percent error of 8%. The most accurate models used 2 detection positions on the tissue surface. Fits were excellent for all depths, although the error in prediction increased for models deeper in the tissue. Reducing the number of collection positions did reduce the goodness of fits, although reasonable predictions could be made when only 4 detector positions were used (Figure 4). With 4 positions, the fluence rate 12 mm below the tissue could be predicted with ~±3% accuracy. Predicted Fluence (mw/cm 2 ) 25 2 15 1 5 5 1 15 2 25 Measured Fluence (mw/cm 2 ) Figure 3: Predicted fluence vs. measured fluence at 5 mm below surface for simulations of homogeneous tissue. The model used 2 reflectance positions. Fit has 8% error with r 2 =.97. Predicted Fluence Rate (mw/cm 2 ) 1-1 1-2 1-3 1-4 1-5 1-5 1-4 1-3 1-2 1-1 Actual Fluence Rate (mw/cm 2 ) Figure 4: Predicted fluence vs. measured fluence at 5 mm below surface for simulations of layered tissue. The model used 4 reflectance positions. Fit has 3% error with r 2 =.91. Proc. of SPIE Vol. 6428 64283-5

This analysis shows that the diffuse reflectance can be used to provide a reasonably accurate prediction of the fluence rate at any particular depth below the tissue surface. With this information, the clinician can adjust the light delivered to the surface so that the fluence rate delivered to the target is consistent for all patients, i.e. if the dosimetry indicates highly attenuating tissue, then the clinician can increase the light delivered to the surface. For the clinician, the question may be whether the on-line dosimetry at the level of accuracy developed here represents a significant improvement in consistently delivering the light dose to the target, especially when compared to not using any on-line dosimetry. Without on-line dosimetry, the clinician inherently assumes that for a given surface irradiance, the subsurface fluence rate is the same for every patient. We can estimate this standard or average fluence rate by using the average fluence rate calculated by the Monte Carlo simulations. The accuracy of using this average fluence rate can be tested by comparing it to the actual value using the same definition of percentage error as used in equation 1, where p now equals the average fluence rate. Figures 5 shows plots of 2 for both the homogeneous and layered cases using four reflectance distances in the models. These are compared to 2 values when the predicted fluence rate is always the average value. For the homogeneous case, (Figure 6a) the error becomes extremely large by a depth of 5 mm. The range in optical properties is quite large in the homogeneous model, and at larger depths, the fluence rates vary by 2 orders of magnitude. In the layered (skin on fat) case, (Figure 6b) the variation in the optical properties below the dermis is minor, with the largest variation in fluence rates due to the absorption of the epidermis and variations in the dermis oxygenation. The variation in fluence rates is not as large as in the homogeneous case since the optical properties have been restricted in the top layer to simulate that of skin and in the lower layer that of muscle or fat. Predictions using the diffuse reflectance modeling (with only a small number of detectors) improves the prediction by a factor of ~3 when compared to using the average fluence rate. a) 25 b) % Prediction Error) 2 15 1 5 Average Fluence Rate Model (4 distances) 2 (% Prediction Error) 7 6 5 4 3 2 1 Average Fluence Rate Model (4 Distances) 5 1 Depth (mm) 5 1 Depth (mm) Figure 5: Comparison of error in fluence rate using either the average fluence rate for all simulations, or the fluence rate as predicted by a diffuse reflectance model that incorporates 4 reflectance measurements. a) homogeneous tissue, b) layered tissue (skin on fat or muscle). 4.2. Phantom Measurements The results of the phantom measurements were similar to those predicted by the Monte Carlo simulations, with a slightly reduced accuracy, likely the result measurement errors. In general, the spatially resolved diffuse reflectance measurements can be used to estimate the fluence rate with an accuracy of ~15-3% standard error, depending on the depth probed, the analytical model used and the number of diffuse reflectance positions used in the model. The standard error in prediction of the fluence rate increases with increased depth, to a depth of approximately 7 mm, where it then remains essentially the same for all depths. This result is similar to the Monte Carlo modeling predictions. Figure 6 shows the predicted versus measured fluence rates in phantom measurements using either 15 or 4 diffuse reflectance positions in deriving the analytical model. In both cases, the predictions are reasonably accurate, with 24% and 27% error for 15 and 4 positions respectively. Proc. of SPIE Vol. 6428 64283-6

The prediction error is smaller closer to the surface, (Figure 7) as expected since the variation in the fluence rates is smallest closest to the surface. While the Monte Carlo simulations showed that using larger number of reflectance measurement positions significantly improved the fluence rate prediction, for the phantom measurements, the effect was minor (Figure 8). This can likely be attributed to measurement precision compromising the development of the predictive models. 6 Predicted Fluence Rate (mw/cm 2 ) 4 2 15 Distances 4 Distances Figure 6: Predicted vs actual fluence rates for phantom measurements at 1 mm from surface. Results are shown for models derived from either 15 or 4 reflectance distances. Prediction error is 24% for the model using 15 distances, and 27% for the model using 4 distances. 2 4 6 Actual Fluence Rate (mw/cm 2 ) 35 3 % Standard Error 3 25 2 15 1 5 15 4 % Standard Error (9mm) 29 28 27 26 25 5 1 15 2 5 1 15 Depth (mm) Number of Reflectance Positions Figure 7: Percent standard error for phantom measurements made at different depths under the surface, and for different number of positions used in creating the predictive model. Figure 8: Percent standard error of prediction for both the model as a function of the number of diffuse reflectance positions used to predict the fluence rate at a depth of 9 mm. Proc. of SPIE Vol. 6428 64283-7

5. DISCUSSION We have developed a non-invasive method of accurately predicting the fluence rate below the tissue surface using measurements of the diffusely reflected treatment light. The method uses spatially-resolved diffuse reflectance measurements of the treatment light as a means of providing information on the subsurface fluence rate. Monte Carlo simulations and measurements on tissue simulating phantoms were used to test and validate the concept. Using this approach, the fluence rate can be predicted to within 27% of the actual value at depths between 2-15 mm. Predictions were better just below the surface than much deeper in the tissue since the variation in the fluence rate is smaller just below the surface. While the accuracy of the approach improved with larger number of surface reflectance measurements, it was found that accurate predictions could be made using only 4 diffuse reflectance measurements, located between 3 15 mm from the source position. Incorporating accurate measures of light dosimetry as a standard clinical practice in low level laser therapy will enable impartial and consistent comparisons of LLLT clinical trials. It will also enable a more methodical assessment of the biological response of the different tissues to LLLT, allowing the field to begin to understand the effect of patient variability on treatment response and should lead to a more rapid development of clinical protocols. 6. ACKNOWLEDGEMENTS This study was supported in part by Theralase Inc., the Ontario Centres of Excellence, and the Industrial Research Assistance Program of the National Research Council of Canada. 7. REFERENCES 1. Chow, R. T., Barnsley, L.: Systematic review of the literature of low-level laser therapy (LLLT) in the management of neck pain. Lasers Surg. Med., 37: 46, (25) 2. Bjordal, J. M., Couppe, C., Chow, R. T. et al.: A systematic review of low level laser therapy with locationspecific doses for pain from chronic joint disorders. Aust. J. Physiother., 49: 17, (23) 3. Karu, T.: Primary and secondary mechanisms of action of visible to near-ir radiation on cells. J. Photochem. Photobiol. B-Biol., 49: 1, (1999) 4. Eell, G. A., Galvin, J. M., Low, D. et al.: Guidance document on delivery, treatment planning, and clinical implementation of IMRT: Report of the IMRT subcommittee of the AAPM radiation therapy committee. Med. Phys., 3: 289, (23) 5. Jacky, J.: 3-D Radiation-Therapy Treatment Planning - Overview and Assessment. Am. J. Clin. Oncol.-Cancer Clin. Trials, 13: 331, (199) 6. Altschuler, M. D., Zhu, T. C., Li, J. et al.: Optimied interstitial PDT prostate treatment planning with the Cimmino feasibility algorithm. Med. Phys., 32: 3524, (25) 7. Farrell, T. J., Patterson, M. S., Wilson, B.: A Diffusion-Theory Model of Spatially Resolved, Steady-State Diffuse Reflectance for the Noninvasive Determination of Tissue Optical-Properties Invivo. Med. Phys., 19: 879, (1992) 8. Farrell, T. J., Patterson, M. S., Essenpreis, M.: Influence of layered tissue architecture on estimates of tissue optical properties obtained from spatially resolved diffuse reflectometry. Appl. Optics, 37: 1958, (1998) 9. Weersink, R. A., Hayward, J. E., Diamond, K. R. et al.: Accuracy of noninvasive in vivo measurements of photosensitier uptake based on a diffusion model of reflectance spectroscopy. Photochem. Photobiol., 66: 326, (1997) 1. Kienle, A., Patterson, M. S., Dognit, N. et al.: Noninvasive determination of the optical properties of twolayered turbid media. Appl. Optics, 37: 779, (1998) 11. Jacques, S. L.: Monte Carlo modeling of light transport in tissues. In: Optical-Thermal Response of Laser Irradiated Tissue. Edited by A. J. Welch and M. J. C. van Gemert. New York: Plenum, 1995 Proc. of SPIE Vol. 6428 64283-8