Beamlet Dose Distributions Research Articles

TriB-RT: Simultaneous optimization of photon, electron and proton beams

Purpose. To develop a novel treatment planning process (TPP) with simultaneous optimization of modulated photon, electron and proton beams for improved treatment plan quality in radiotherapy. Methods. A framework for fluence map optimization of Monte Carlo (MC) calculated beamlet dose distributions is developed to generate treatment plans consisting of photon, electron and spot scanning proton fields. Initially, in-house intensity modulated proton therapy (IMPT) plans are compared to proton plans created by a commercial treatment planning system (TPS). A triple beam radiotherapy (TriB-RT) plan is generated for an exemplary academic case and the dose contributions of the three particle types are investigated. To investigate the dosimetric potential, a TriB-RT plan is compared to an in-house IMPT plan for two clinically motivated cases. Benefits of TriB-RT for a fixed proton beam line with a single proton field are investigated. Results. In-house optimized IMPT are of at least equal or better quality than TPS-generated proton plans, and MC-based optimization shows dosimetric advantages for inhomogeneous situations. Concerning TriB-RT, for the academic case, the resulting plan shows substantial contribution of all particle types. For the clinically motivated case, improved sparing of organs at risk close to the target volume is achieved compared to IMPT (e.g. myelon and brainstem −37%) at cost of an increased low dose bath (healthy tissue V 10% +22%). In the scenario of a fixed proton beam line, TriB-RT plans are able to compensate the loss in degrees of freedom to substantially improve plan quality compared to a single field proton plan. Conclusion. A novel TPP which simultaneously optimizes photon, electron and proton beams was successfully developed. TriB-RT shows the potential for improved treatment plan quality and is especially promising for cost-effective single-room proton solutions with a fixed beamline in combination with a conventional linac delivering photon and electron fields.

An investigation into the robustness of dynamically collimated proton therapy treatments.

To investigate the dosimetric robustness of dynamically collimated proton therapy (DCPT) treatment plans delivered using a dynamic collimation system (DCS) with respect to random uncertainties in beam spot and collimator position as well as systematic offsets in the DCS mounting alignment. This work also demonstrates a technique that can increase plan robustness while preserving target conformity. Variability in beam spot and collimator positioning can result in changes to a beamlet's dose distribution and incident fluence. The robustness of the DCPT treatment plans was evaluated for three intracranial treatment sites by modeling treatment variability as normally distributed random variables with standard deviations reflecting a clinical system. The simulated treatment plans were then recalculated and compared against their nominal, idealized dose distribution among several trials. It was hypothesized that a plan's robustness to these delivery variables could be reduced by restricting a trimmer's placement toward a beamlet's central axis during collimation. By introducing a minimum trimmer offset of 1.5mm, the variation of the planning target volume (PTV) D95% coverage was reduced to within 2% of the prescribed dose. The treatment plans with trimmers that were placed within 0.5mm of a collimated beamlet's central axis resulted in the greatest healthy tissue sparing but deviations as high as 11.4% to the PTV D95% were observed. The nominal conformity of these treatment plans utilizing the 1.5 mm trimmer offset was also well maintained. For each treatment plan studied, the 90% conformity index remained within 6.25% of the conformity index achieved without a minimum trimmer offset, and the D50% of surrounding healthy tissue increased by no more than 3.1Gy relative to a plan without a trimmer offset. While DCPT can offer a significant reduction in healthy tissue irradiation, the results from this work indicate that special care must be taken to ensure proper PTV coverage amid uncertainties associated with this new treatment modality. A simple approach utilizing a minimum trimmer offset was able to preserve the majority of the target conformity and healthy tissue sparing the DCS technology affords while minimizing the uncertainties in this treatment approach.

Robust mixed electron-photon radiation therapy optimization.

Mixed beam electron-photon radiation therapy (MBRT) is an emerging technique that has the potential to reduce dose to normal tissue while improving target coverage for cancer sites with superficial tumors. Advances in optimization algorithms and robotic linear accelerators have made the creation and delivery of complex MBRT plans realistic without the need for special additional collimators, devices, or resetup of the patient. However, no study has been performed on the robustness of MBRT dose distributions to patient setup errors. Intensity-modulated delivery of other charged particles such as protons have been shown to require robust planning techniques to maintain adequate target coverage under positioning errors. We therefore assess the sensitivity of MBRT treatment plans to positioning uncertainties when created under the traditional planning target volume (PTV)-based planning paradigm and present a novel optimization model for the creation of robust MBRT plans. The column generation method was applied to robust MBRT treatment planning by deriving the pricing problem for stochastic and "worst case" minimax optimization models, two common formulations of robustness. Robust treatment plans were created for two patient cases representative of the cancer sites which stand to benefit from MBRT: soft tissue sarcoma (STS) irradiation and chest wall irradiation with deep-seated internal mammary, axillary, and supraclavicular nodes (CW-N). For both patient cases, beamlet dose distributions for electrons and photons were generated for positioning shifts in six directions, in addition to a nominal unshifted scenario, for a total of seven sets of beamlets. Robust plans were created by specifying dose coverage constraints to the clinical target volume (CTV), as opposed to the PTV. Comparisons were performed against traditional PTV-based plans created with a single set of unshifted beamlets. The dose distributions of traditional PTV-based MBRT plans showed significant degradation in target coverage homogeneity when patient positioning errors were considered. For both cancer sites, cold spots below 95% and hot spots above 108% of the prescription dose appeared within the CTV when shifting the patient by 5mm, corresponding to the margin added to the CTV to form the PTV. In contrast, CTV-based robust plans created with the new optimization model maintained target coverage within the 95%-108% limits, for all positioning errors. The quality of MBRT treatment plans created using a traditional PTV-based optimization model was highly sensitive to patient positioning errors. For both patient cases, positioning errors resulted in perturbations to the nominal dose distributions which would have rendered PTV-based plans clinically unacceptable. In contrast, CTV-based robust plans were able to maintain adequate target coverage under all positioning error scenarios considered. We therefore conclude that to ensure the fidelity of the dose distribution delivered to the patient, robust optimization is critical when creating MBRT plans.

SU‐F‐T‐428: An Optimization‐Based Commissioning Tool for Finite Size Pencil Beam Dose Calculations

Purpose:Finite size pencil beam (FSPB) algorithms are commonly used to pre‐calculate the beamlet dose distribution for IMRT treatment planning. FSPB commissioning, which usually requires fine tuning of the FSPB kernel parameters, is crucial to the dose calculation accuracy and hence the plan quality. Yet due to the large number of beamlets, FSPB commissioning could be very tedious. This abstract reports an optimization‐based FSPB commissioning tool we have developed in MatLab to facilitate the commissioning.Methods:A FSPB dose kernel generally contains two types of parameters: the profile parameters determining the dose kernel shape, and a 2D scaling factors accounting for the longitudinal and off‐axis corrections. The former were fitted using the penumbra of a reference broad beam's dose profile with Levenberg‐Marquardt algorithm. Since the dose distribution of a broad beam is simply a linear superposition of the dose kernel of each beamlet calculated with the fitted profile parameters and scaled using the scaling factors, these factors could be determined by solving an optimization problem which minimizes the discrepancies between the calculated dose of broad beams and the reference dose.Results:We have commissioned a FSPB algorithm for three linac photon beams (6MV, 15MV and 6MVFFF). Dose of four field sizes (6*6cm2, 10*10cm2, 15*15cm2 and 20*20cm2) were calculated and compared with the reference dose exported from Eclipse TPS system. For depth dose curves, the differences are less than 1% of maximum dose after maximum dose depth for most cases. For lateral dose profiles, the differences are less than 2% of central dose at inner‐beam regions. The differences of the output factors are within 1% for all the three beams.Conclusion:We have developed an optimization‐based commissioning tool for FSPB algorithms to facilitate the commissioning, providing sufficient accuracy of beamlet dose calculation for IMRT optimization.

SU‐E‐T‐422: Fast Analytical Beamlet Optimization for Volumetric Intensity‐Modulated Arc Therapy

Purpose:To implement a fast optimization algorithm on CPU/GPU heterogeneous computing platform and to obtain an optimal fluence for a given target dose distribution from the pre‐calculated beamlets in an analytical approach.Methods:The 2D target dose distribution was modeled as an n‐dimensional vector and estimated by a linear combination of independent basis vectors. The basis set was composed of the pre‐calculated beamlet dose distributions at every 6 degrees of gantry angle and the cost function was set as the magnitude square of the vector difference between the target and the estimated dose distribution. The optimal weighting of the basis, which corresponds to the optimal fluence, was obtained analytically by the least square method. Those basis vectors with a positive weighting were selected for entering into the next level of optimization. Totally, 7 levels of optimization were implemented in the study.Ten head‐and‐neck and ten prostate carcinoma cases were selected for the study and mapped to a round water phantom with a diameter of 20cm. The Matlab computation was performed in a heterogeneous programming environment with Intel i7 CPU and NVIDIA Geforce 840M GPU.Results:In all selected cases, the estimated dose distribution was in a good agreement with the given target dose distribution and their correlation coefficients were found to be in the range of 0.9992 to 0.9997. Their root‐mean‐square error was monotonically decreasing and converging after 7 cycles of optimization. The computation took only about 10 seconds and the optimal fluence maps at each gantry angle throughout an arc were quickly obtained.Conclusion:An analytical approach is derived for finding the optimal fluence for a given target dose distribution and a fast optimization algorithm implemented on the CPU/GPU heterogeneous computing environment greatly reduces the optimization time.

A new Monte Carlo-based treatment plan optimization approach for intensity modulated radiation therapy

Intensity-modulated radiation treatment (IMRT) plan optimization needs beamlet dose distributions. Pencil-beam or superposition/convolution type algorithms are typically used because of their high computational speed. However, inaccurate beamlet dose distributions may mislead the optimization process and hinder the resulting plan quality. To solve this problem, the Monte Carlo (MC) simulation method has been used to compute all beamlet doses prior to the optimization step. The conventional approach samples the same number of particles from each beamlet. Yet this is not the optimal use of MC in this problem. In fact, there are beamlets that have very small intensities after solving the plan optimization problem. For those beamlets, it may be possible to use fewer particles in dose calculations to increase efficiency. Based on this idea, we have developed a new MC-based IMRT plan optimization framework that iteratively performs MC dose calculation and plan optimization. At each dose calculation step, the particle numbers for beamlets were adjusted based on the beamlet intensities obtained through solving the plan optimization problem in the last iteration step. We modified a GPU-based MC dose engine to allow simultaneous computations of a large number of beamlet doses. To test the accuracy of our modified dose engine, we compared the dose from a broad beam and the summed beamlet doses in this beam in an inhomogeneous phantom. Agreement within 1% for the maximum difference and 0.55% for the average difference was observed. We then validated the proposed MC-based optimization schemes in one lung IMRT case. It was found that the conventional scheme required 106 particles from each beamlet to achieve an optimization result that was 3% difference in fluence map and 1% difference in dose from the ground truth. In contrast, the proposed scheme achieved the same level of accuracy with on average 1.2 × 105 particles per beamlet. Correspondingly, the computation time including both MC dose calculations and plan optimizations was reduced by a factor of 4.4, from 494 to 113 s, using only one GPU card.

A method for modeling laterally asymmetric proton beamlets resulting from collimation.

To introduce a method to model the 3D dose distribution of laterally asymmetric proton beamlets resulting from collimation. The model enables rapid beamlet calculation for spot scanning (SS) delivery using a novel penumbra-reducing dynamic collimation system (DCS) with two pairs of trimmers oriented perpendicular to each other. Trimmed beamlet dose distributions in water were simulated with MCNPX and the collimating effects noted in the simulations were validated by experimental measurement. The simulated beamlets were modeled analytically using integral depth dose curves along with an asymmetric Gaussian function to represent fluence in the beam's eye view (BEV). The BEV parameters consisted of Gaussian standard deviations (sigmas) along each primary axis (σ(x1),σ(x2),σ(y1),σ(y2)) together with the spatial location of the maximum dose (μ(x),μ(y)). Percent depth dose variation with trimmer position was accounted for with a depth-dependent correction function. Beamlet growth with depth was accounted for by combining the in-air divergence with Hong's fit of the Highland approximation along each axis in the BEV. The beamlet model showed excellent agreement with the Monte Carlo simulation data used as a benchmark. The overall passing rate for a 3D gamma test with 3%/3 mm passing criteria was 96.1% between the analytical model and Monte Carlo data in an example treatment plan. The analytical model is capable of accurately representing individual asymmetric beamlets resulting from use of the DCS. This method enables integration of the DCS into a treatment planning system to perform dose computation in patient datasets. The method could be generalized for use with any SS collimation system in which blades, leaves, or trimmers are used to laterally sharpen beamlets.

TH‐E‐BRE‐08: GPU‐Monte Carlo Based Fast IMRT Plan Optimization

Purpose:Intensity‐modulated radiation treatment (IMRT) plan optimization needs pre‐calculated beamlet dose distribution. Pencil‐beam or superposition/convolution type algorithms are typically used because of high computation speed. However, inaccurate beamlet dose distributions, particularly in cases with high levels of inhomogeneity, may mislead optimization, hindering the resulting plan quality. It is desire to use Monte Carlo (MC) methods for beamlet dose calculations. Yet, the long computational time from repeated dose calculations for a number of beamlets prevents this application. It is our objective to integrate a GPU‐based MC dose engine in lung IMRT optimization using a novel two‐steps workflow.Methods:A GPU‐based MC code gDPM is used. Each particle is tagged with an index of a beamlet where the source particle is from. Deposit dose are stored separately for beamlets based on the index. Due to limited GPU memory size, a pyramid space is allocated for each beamlet, and dose outside the space is neglected. A two‐steps optimization workflow is proposed for fast MC‐based optimization. At first step, rough beamlet dose calculations is conducted with only a small number of particles per beamlet. Plan optimization is followed to get an approximated fluence map. In the second step, more accurate beamlet doses are calculated, where sampled number of particles for a beamlet is proportional to the intensity determined previously. A second‐round optimization is conducted, yielding the final Result.Results:For a lung case with 5317 beamlets, 105 particles per beamlet in the first round, and 108 particles per beam in the second round are enough to get a good plan quality. The total simulation time is 96.4 sec.Conclusion:A fast GPU‐based MC dose calculation method along with a novel two‐step optimization workflow are developed. The high efficiency allows the use of MC for IMRT optimizations.

GPU-Monte Carlo based fast IMRT plan optimization

Purpose: Intensity-modulated radiation treatment (IMRT) plan optimization needs pre-calculated beamlet dose distribution. Pencil-beam or superposition/convolution type algorithms are typically used because of high computation speed. However, inaccurate beamlet dose distributions, particularly in cases with high levels of inhomogeneity, may mislead optimization, hindering the resulting plan quality. It is desire to use Monte Carlo (MC) methods for beamlet dose calculations. Yet, the long computational time from repeated dose calculations for a number of beamlets prevents this application. It is our objective to integrate a GPU-based MC dose engine in lung IMRT optimization using a novel two-steps workflow. Methods : A GPU-based MC code gDPM is used. Each particle is tagged with an index of a beamlet where the source particle is from. Deposit dose are stored separately for beamlets based on the index. Due to limited GPU memory size, a pyramid space is allocated for each beamlet, and dose outside the space is neglected. A two-steps optimization workflow is proposed for fast MC-based optimization. At first step, a rough dose calculation is conducted with only a few number of particle per beamlet. Plan optimization is followed to get an approximated fluence map. In the second step, more accurate beamlet doses are calculated, where sampled number of particles for a beamlet is proportional to the intensity determined previously. A second-round optimization is conducted, yielding the final result. Results: For a lung case with 5317 beamlets, 105 particles per beamlet in the first round, and 108 particles per beam in the second round are enough to get a good plan quality. The total simulation time is 96.4 sec. Conclusion : A fast GPU-based MC dose calculation method along with a novel two-step optimization workflow are developed. The high efficiency allows the use of MC for IMRT optimizations. -------------------------------- Cite this article as: Li Y, Tian Z, Shi F, Jiang S, Jia X. GPU-Monte Carlo based fast IMRT plan optimization. Int J Cancer Ther Oncol 2014; 2 (2):020244. DOI: 10.14319/ijcto.0202.44

SU-E-T-469: The Effects of Patient Anatomy and Parallel Magnetic Fields on Beamlet Dose Distributions.

Beamlets are generated in a patient geometry in the presence of a magnetic field to investigate the effects of tissue density and magnetic field on beamlet dose distributions, which is important for the optimization of photon fluence to be delivered by a linac-MR system. 50×50 mm2 fields were placed with isocenter in the middle of a patient's right lung. Each treatment field was decomposed into 100 beamlets (each 5×5 mm2 ). BEAMnrc scored the particle phase space at 100.2 cm from the source in the linac-MR geometry (isocentre at 126 cm) with parallel magnetic fields of 0, 0.56, and 3T. DOSXYZnrc was modified to score the energy deposited by particles from this phase space as a function of the beamlet the particle passed through. The calculation volume of 70×46×64 voxels encompassed the patient with a voxel size of 3×3×3 mm3 . Each beamlet was normalized to the dose calculated to a 3×3×3 mm3 voxel with isocenter at 5cm depth in a flat water tank without a magnetic field. Beamlet files were calculated on Western Canada's high performance computing cluster (Westgrid) using 100 processors, enabling simulation of 109 histories in less than 3 hours. The resulting files, which contained 3D dose distributions for all 100 beamlets, were 81 MB per field. The Monte Carlo uncertainty was also stored. The gyroradii for 1 MeV electron traversing field lines at 20 degrees are 2.9mm and 0.5mm for 0.56 and 3T fields respectively. The 0.56T parallel magnetic field has a small effect compared to the distortion of the beamlet introduced by the presence of lung. The effect of tissue heterogeneities is more significant than the effect of a 0.56T parallel magnetic field. A 3T field refocuses the dose in lung to the beamlet path and significantly reduces the lateral electron scatter.

A technique for generating phase-space-based Monte Carlo beamlets in radiotherapy applications

As radiotherapy treatment planning moves toward Monte Carlo (MC) based dose calculation methods, the MC beamlet is becoming an increasingly common optimization entity. At present, methods used to produce MC beamlets have utilized a particle source model (PSM) approach. In this work we outline the implementation of a phase-space-based approach to MC beamlet generation that is expected to provide greater accuracy in beamlet dose distributions. In this approach a standard BEAMnrc phase space is sorted and divided into beamlets with particles labeled using the inheritable particle history variable. This is achieved with the use of an efficient sorting algorithm, capable of sorting a phase space of any size into the required number of beamlets in only two passes. Sorting a phase space of five million particles can be achieved in less than 8 s on a single-core 2.2 GHz CPU. The beamlets can then be transported separately into a patient CT dataset, producing separate dose distributions (doselets). Methods for doselet normalization and conversion of dose to absolute units of Gy for use in intensity modulated radiation therapy (IMRT) plan optimization are also described.

Dose optimization for the MRI-accelerator: IMRT in the presence of a magnetic field

A combined system of a 6 MV linear accelerator and a 1.5 T MRI scanner is currently being developed. In this system, the patient will be irradiated in the presence of a 1.5 T magnetic field. This causes a strong dose increase at tissue–air interfaces. Around air cavities in the patient, these effects may become problematic. Homogeneous dose distributions can be obtained around regularly shaped symmetrical cavities using opposing beams. However, for more irregularly shaped cavities this approach may not be sufficient. This study will investigate whether IMRT can be used to cope with magnetic field dose effects, in particular for target volumes adjacent to irregularly shaped air cavities. Therefore, an inverse treatment planning approach has been designed based on pre-calculated beamlet dose distribution kernels. Using this approach, optimized dose distributions were calculated for B = 1.5 T and for B = 0 T. Investigated target sites include a prostate cancer, a laryngeal cancer and an oropharyngeal cancer. Differences in the dose distribution between B = 0 and 1.5 T were minimal; only the skin dose increased for B = 1.5 T. Homogeneous dose distributions were obtained for target structures adjacent to air cavities without the use of opposing beams. These results show that a 1.5 T magnetic field does not compromise the ability to achieve desired dose distributions with IMRT.

SU-FF-T-286: MARS - A Monte Carlo Assisted Research System for Radiotherapy

Purpose: To develop a dedicated and versatile research system for radiotherapy treatment planning that combines Monte Carlo dose calculation accuracy with inverse planning capability. Method and Materials: In this work, a research platform with interactive graphics user interface (GUI) has been developed for Monte Carlo based inverse planning for radiotherapy. It consists of a top-level module that integrates and coordinates the other four sub-modules in the system: a DICOM IMAGE/RT server module that receives all incoming DICOM images and DICOM RT objects, an iPort module that constructs a patient phantom in EGS4 format from DICOM image studies, a phant2optim module that performs ray-tracing in the phantom and prepares input for pre-optimization Monte Carlo simulations of the beamlets, and an optim2mcsim module that optimizes a treatment plan based on Monte Carlo simulated beamlets dose distributions and prepares input for post-optimization Monte Carlo simulations of the plan. Results: A Monte Carlo assisted research system (MARS) with five designed modules has been developed and integrated by an interactive graphics user interface. Interfacing of the MARS with the Varian Eclipse treatment planning system (TPS) via DICOM RT and with the EGS4/BEAM for Monte Carlo dose calculation has been successfully established and verified. Conclusions: A Monte Carlo based inverse planning system has been developed for radiotherapy treatment planning and research. This system is able to import DICOM IMAGE/RT objects from a clinical TPS, convert them into a patient CT phantom, and optimize a plan based on Monte Carlo simulations of beamlet dose distributions in the patient CT phantom. With the dose calculation accuracy of Monte Carlo, multiple inverse planning algorithms, and a user-friendly GUI, we hope this system will further enhance the application and research of Monte Carlo treatment planning in radiotherapy. We plan to have it available free to interested parties in the future.

A finite size pencil beam algorithm for IMRT dose optimization: density corrections

For beamlet-based IMRT optimization, fast and less accurate dose computation algorithms are frequently used, while more accurate algorithms are needed to recompute the final dose for verification. In order to speed up the optimization process and ensure close proximity between dose in optimization and verification, proper consideration of dose gradients and tissue inhomogeneity effects should be ensured at every stage of the optimization. Due to their speed, pencil beam algorithms are often used for precalculation of beamlet dose distributions in IMRT treatment planning systems. However, accounting for tissue heterogeneities with these models requires the use of approximate rescaling methods. Recently, a finite size pencil beam (fsPB) algorithm, based on a simple and small set of data, was proposed which was specifically designed for the purpose of dose pre-computation in beamlet-based IMRT. The present work describes the incorporation of 3D density corrections, based on Monte Carlo simulations in heterogeneous phantoms, into this method improving the algorithm accuracy in inhomogeneous geometries while keeping its original speed and simplicity of commissioning. The algorithm affords the full accuracy of 3D density corrections at every stage of the optimization, hence providing the means for density related fluence modulation like penumbra shaping at field edges.

Direct aperture optimization for IMRT using Monte Carlo generated beamlets

This work introduces an EGSnrc-based Monte Carlo (MC) beamlet does distribution matrix into a direct aperture optimization (DAO) algorithm for IMRT inverse planning. The technique is referred to as Monte Carlo-direct aperture optimization (MC-DAO). The goal is to assess if the combination of accurate Monte Carlo tissue inhomogeneity modeling and DAO inverse planning will improve the dose accuracy and treatment efficiency for treatment planning. Several authors have shown that the presence of small fields and/or inhomogeneous materials in IMRT treatment fields can cause dose calculation errors for algorithms that are unable to accurately model electronic disequilibrium. This issue may also affect the IMRT optimization process because the dose calculation algorithm may not properly model difficult geometries such as targets close to low-density regions (lung, air etc.). A clinical linear accelerator head is simulated using BEAMnrc (NRC, Canada). A novel in-house algorithm subdivides the resulting phase space into 2.5 X 5.0 mm2 beamlets. Each beamlet is projected onto a patient-specific phantom. The beamlet dose contribution to each voxel in a structure-of-interest is calculated using DOSXYZnrc. The multileaf collimator (MLC) leaf positions are linked to the location of the beamlet does distributions. The MLC shapes are optimized using direct aperture optimization (DAO). A final Monte Carlo calculation with MLC modeling is used to compute the final dose distribution. Monte Carlo simulation can generate accurate beamlet dose distributions for traditionally difficult-to-calculate geometries, particularly for small fields crossing regions of tissue inhomogeneity. The introduction of DAO results in an additional improvement by increasing the treatment delivery efficiency. For the examples presented in this paper the reduction in the total number of monitor units to deliver is approximately 33% compared to fluence-based optimization methods.

SU-FF-T-204: Effect of Voxel Size On Monte Carlo Dose Calculation for Intensity Modulated Radiotherapy Treatment Planning

Purpose: In radiotherapytreatment planning, the voxel size plays a role in each step, from the structure contouring, beamlet calculation, treatment optimization to plan analysis. Large voxels may affect the planning accuracy while small voxels will increase computation time significantly. The choice of voxel size is a compromise between planning accuracy and computation speed. This work investigates the effect of voxel size on the accuracy and computational speed of Monte Carlo based treatment planning for IMRT.Method and Materials: Selected patients with various treatment sites were scanned on a CT simulator and the CT data with RT structures were converted to patient geometry files with different voxel sizes for Monte Carlo simulations. Comparisons of the structure volume, beamlet dose distribution, plan optimization, DVHs and dose distributions were made with different voxel sizes. Target and critical structures with small volumes, such as the optical nerves in head and neck cases, were specially considered in the comparison. Results: In Monte Carlo IMRT planning, differences in the open‐field dose calculations prior to the optimization process using different voxel sizes (4 × 4 mm2 and 2 × 2 mm2) were usually small. However, the results from the optimization showed that different voxel sizes could generate different beamlet weights resulting in different treatment plans. The effects could be clinically significant. For the same IMRT plans, the effects of voxel sizes in the dose and DVH calculations vary depending on the treatment sites, anatomy and target/organ volume. Small voxel sizes (1–2mm) are necessary for small structures in and near heterogeneous geometries such as in the head and neck regions. Conclusions: The effect of voxel size on dose calculation and plan optimization can be clinically significant in IMRTtreatment planning. Small voxel (1–2mm) should be used for head and neck cases.

223 Implementation of Monte Carlo calculated beamlet dose distributions in a direct aperture optimization algorithm for IMRT

223 Implementation of Monte Carlo calculated beamlet dose distributions in a direct aperture optimization algorithm for IMRT

SU-FF-J-46: A Probabilistic Method for Online Treatment Plan Modification

Purpose: To develop an IMRT treatment planning method that accounts for increased knowledge of tumor and normal tissue location gained from inter- or intra-fractional imaging. Method and Materials: Knowledge of tumor location in each fraction or sub-fraction is represented by a probability density function (pdf), p(n,n′,t), that describes the probability that a voxel at index n in the planning image will be located at voxel n′ at a later time t. The pdf is changing as the new information becomes available. A set of beam profiles for each fraction or sub-fraction is then obtained by minimizing the quadratic objective function by accounting for probabilistic nature of the beamlet dose distribution: 〈d〉n,t=∑n,dn,p(n,n′,t). in each fraction. This optimization problem is set with the intention that the beamlets that have the highest risk of missing the tumor will not be delivered until adequate knowledge of tumor location becomes available. We apply this method to a case of a disk-shaped tumor inside a disk-shaped phantom with a tomotherapy beam geometry. Results: The method proved to deliver more conservative dose distributions at the beginning of treatment than at the end for both inter- and intra-fractional cases. For early fractions and sub-fractions, low-risk doses were delivered to regions where the probability was high that the tumor would be present during the treatment. As knowledge of tumor location improved, the remaining dose at the periphery of the tumor was filled-in. Conclusion: We showed that incorporating increased knowledge about tumor and normal tissue location, obtained by repeatitive on-line imaging leads to superior treatment plans.

Beamlet dose distribution compression and reconstruction using wavelets for intensity modulated treatment planning.

Intensity modulated radiation therapy (IMRT) treatment planning is often formulated as the optimization of weights of fixed-geometry subfields (beamlets). Efficient optimization techniques can be based on direct storage of the influence matrix relating beamlet weights to dose values. However, direct storage of beamlet dose distributions for IMRT treatment planning can easily exceed several gigabytes, and is therefore often not feasible. We present a method for rapidly calculating full three-dimensional IMRT dose distributions, based on a vector of beamlet weights. The method is based on compressed beamlet dose distributions using fast digital wavelet transforms and so-called hard thresholding. We studied the method with a rectangular beamlet of 0.5 cm x 0.5 cm cross section from a monoenergetic 6 MeV photon point source simulated in homogeneous (water) and heterogeneous (CT-data) phantoms. Dose was calculated using the accurate VMC+ + Monte Carlo engine. The beamlet dose distributions were wavelet transformed and compressed by dropping wavelet coefficients below a given threshold value. Dose is then computed using the remaining wavelets. Selection of the wavelet basis function, decomposition level, and threshold values, for different slice orientations (transverse or parallel to the beam) and varying angles of beamlet incidence are studied. A typical in-slice compression ratio for a plane containing a beamlet was 32:1 using the sym2 wavelet and a threshold of 0.01, with a typical root-mean-square error, for voxels above 50% of the maximum dose, of about 0.04%. The overall compression performance, which includes many planes with little information content, is on the order of 100:1 or greater compared to full matrix storage. Although other methods are available to make the use of stored influence matrix values more feasible in IMRT treatment planning (such as using coarse grids or restricting values to defined volumes of interest) we conclude that wavelet compression facilitates the storage and use of full pencil dose deposition (influence matrix) data in IMRT treatment planning.