GPU-based Monte Carlo Dose Calculation Research Articles

POD-DOSI: A dedicated dosimetry system for GammaPod commissioning and quality assurance.

GammaPod, a stereotactic partial breast irradiator allowing highly conformal radiation dose delivery, has its unique mechanical design and treatment planning system (TPS). However, the uniqueness of the system poses challenges regarding initial GammaPod system commissioning and routine quality assurance (QA). In this study, we report POD-DOSI, a dedicated dosimetry system for accurate and efficient commissioning and QA of GammaPod. The POD-DOSI system consists of two subsystems, POD-Scanner and POD-Calculator. The POD-Scanner is an automatic ion-chamber positioning system driven by two translational stepper motors for anterior-posterior, longitudinal, and lateral beam scanning. The stepper motors are controlled by a microcomputer through an in-house-developed graphical user interface, which can be remotely accessed by a laptop via wireless connection. The POD-Calculator is a commissioned GPU-based Monte Carlo dose calculation engine, which calculates dose by transporting particles from phase space constructed for GammaPod. In our institution, the POD-DOSI system was used for GammaPod TPS commissioning and dose verification. The POD-Calculator was further developed as a secondary dose calculation tool performing patient-specific plan QA before each treatment. The POD-DOSI system has been fully evaluated and tested, both mechanically and dosimetrically, and applied successfully to drive the commissioning of our GammaPod system. The POD-Scanner achieved 0.1mm accuracy in ion-chamber positioning tests. The POD-Calculator generated dose profiles matched well with water phantom measurements and TPS calculations to <0.5mm accuracy. For end-to-end test on 56 different treatment plans, in-water point dose measurements by POD-Scanner were within ±2.20% of the doses calculated by POD-Calculator (range: -2.01% to 2.20%, mean: 0.04%, std_dev: 1.10%). Correspondingly, when switching the calculation medium from water to breast tissue, the point doses calculated by the POD-Calculator were within ±1.60% of the point doses calculated by the GammaPOD TPS (range: -1.59% to 1.51%, mean: -0.02%, std_dev: 0.73%). The average three-dimensional gamma passing rate between the GammaPod TPS doses and the POD-Calculator doses was 97.10% under the 2%/1mm gamma criteria. The POD-DOSI system substantially shortened the GammaPod dosimetry commissioning time from weeks to days. The developed POD-DOSI system resolves the challenges and streamlines the process of GammaPod commissioning and QA. It improves the efficiency and accuracy for both GammaPod commissioning and routine patient-specific QA.

A Treatment Planning System for Intensity-Modulated Small Animal Radiotherapy Realized Using an Existing Movable Rectangular Collimator

Precise small animal radiotherapy is of paramount importance for the advancement of human radiotherapy. Using collimators of fixed sizes and shapes, current standard small animal irradiator cannot cover the tumor, while sparing organs at risk (OARs) to a satisfactory degree. However, current irradiators typically have a movable rectangular collimator. The purpose of this study is to realize intensity modulated radiotherapy (IMRT) for small animal irradiation using the movable rectangular collimator. We report our progress on the development of a treatment planning system and evaluation of the performance. The small animal IMRT treatment planning is formulated as a direct aperture optimization problem with the constraint of using only rectangular aperture shapes. We compute the dose deposition matrix using our in-house developed fast GPU-based Monte Carlo dose calculation engine. Solve the optimization problem consists of two sequential steps. The first step uses a column generation algorithm to determine a series of rectangular apertures and corresponding optimal beam-on time. The second step determines the most efficient aperture order at each beam angle to deliver the generated apertures. As such, we generate a graph with vertices being the apertures and links being transition time between apertures. The optimal order is obtained by finding the shortest-path traversing the graph. We evaluate the developed treatment planning system on a simulated water phantom case with a C-shaped target around a cylindrical OAR and a real mouse case with a lung tumor. We compare the plan quality and delivery efficiency of IMRT plans with those plans generated using cone-shaped collimators that have the smallest sizes to cover the tumor as seen from each beam’s eye view. In both cases, when IMRT plan and cone-based plan are normalized to the same target coverage, OARs are spared much more effectively in IMRT plans. Average dose can be reduced for OAR in water phantom case and lung in real mouse case by 25.3% and 13.9%, respectively. Max doses for OARs in IMRT plan and cone-based plan are almost the same for both cases. The beam-on time of IMRT plan for the mouse case is ∼80 s, approximately doubling that of cone-based plan because of reduced beam output under the apertures. The IMRT plan requires additional 6.9 s of collimator motion time. We have developed a treatment planning system to achieve IMRT for small animal irradiation using a movable rectangular collimator typically available on a current irradiator. The IMRT plan quality surpasses that of the cone-based plan. Treatment delivery time is prolonged mainly due to reduced beam output.Abstract 3824; Table 1CasePlanCTV quantityOARs quantityTreatment time (s)Normalized to prescription doseBeam-onCollimator motionD95D5DmeanDmaxWater phantomIMRT1.01.0390.7691.041----Cone-based1.01.0241.0291.047----Real mouseIMRT1.01.2380.174 (lung)1.242 (heart)82.206.9Cone-based1.01.2240.202 (lung)1.239 (heart)40.730.0 Open table in a new tab

Dosimetric evaluation of 4D‐CBCT reconstructed by Simultaneous Motion Estimation and Image Reconstruction (SMEIR) for carbon ion therapy of lung cancer

Motion management is critical for the efficacy of carbon ion therapy for moving targets such as lung tumors. We evaluated the feasibility of using four-dimensional cone beam computed tomography (4D-CBCT) reconstructed by Simultaneous Motion Estimation and Image Reconstruction (SMEIR) for dose calculation and accumulation in carbon ion treatment of lung cancer. Motion-compensated 4D-CBCT images were reconstructed with the SMEIR algorithm to capture the most updated anatomy and motion with an updated interphase motion model on the treatment day. Projections of all CBCT phases were simulated from the planning 4D-CT by the ray tracing technique. Treatment planning and dose calculation were performed with a GPU-based Monte Carlo dose calculation software for carbon ion therapy. The treatment plan was optimized on the average computed tomography (CT) to obtain optimal intensity of the carbon ions. From the optimized plan, dose distributions on individual phases of 4D-CT and 4D-CBCT were calculated by the Monte Carlo-based dose engine. Dose accumulation was performed on 4D-CBCT images using deformable vector fields (DVF) generated by SMEIR. The accumulated planning target volume (PTV) dose based on 4D-CBCT was then compared to the accumulated dose calculated on 4D-CT, where the DVFs between different phases were obtained by the demons deformable registration algorithm. Dose value histograms (DVH) as well as absolute deviations of the maximum dose ( ), mean dose ( ), and dose coverage metrics ( and ) for PTV were quantitatively evaluated for the two sets of plans. Good agreement was found between the 4D-CT and 4D-CBCT-based PTV-DVH curves. The average values of , , and calculated between the 4D-CT and SMEIR-4D-CBCT-based plans were , , 2.12%, and , respectively, for the PTVs of ten patient case studies. Based on these results, SMEIR-reconstructed 4D-CBCTs can potentially be used for motion estimation, dose evaluation, and adaptive treatment planning in lung cancer carbon ion therapy.

Efficiency improvement in proton dose calculations with an equivalent restricted stopping power formalism

The equivalent restricted stopping power formalism is introduced for proton mean energy loss calculations under the continuous slowing down approximation. The objective is the acceleration of Monte Carlo dose calculations by allowing larger steps while preserving accuracy. The fractional energy loss per step length ϵ was obtained with a secant method and a Gauss–Kronrod quadrature estimation of the integral equation relating the mean energy loss to the step length. The midpoint rule of the Newton–Cotes formulae was then used to solve this equation, allowing the creation of a lookup table linking ϵ to the equivalent restricted stopping power Leq, used here as a key physical quantity. The mean energy loss for any step length was simply defined as the product of the step length with Leq. Proton inelastic collisions with electrons were added to GPUMCD, a GPU-based Monte Carlo dose calculation code. The proton continuous slowing-down was modelled with the Leq formalism. GPUMCD was compared to Geant4 in a validation study where ionization processes alone were activated and a voxelized geometry was used. The energy straggling was first switched off to validate the Leq formalism alone. Dose differences between Geant4 and GPUMCD were smaller than 0.31% for the Leq formalism. The mean error and the standard deviation were below 0.035% and 0.038% respectively. 99.4 to 100% of GPUMCD dose points were consistent with a 0.3% dose tolerance. GPUMCD 80% falloff positions () matched Geant’s within 1 μm. With the energy straggling, dose differences were below 2.7% in the Bragg peak falloff and smaller than 0.83% elsewhere. The positions matched within 100 μm. The overall computation times to transport one million protons with GPUMCD were 31–173 ms. Under similar conditions, Geant4 computation times were 1.4–20 h. The Leq formalism led to an intrinsic efficiency gain factor ranging between 30–630, increasing with the prescribed accuracy of simulations. The Leq formalism allows larger steps leading to a algorithmic time complexity. It significantly accelerates Monte Carlo proton transport while preserving accuracy. It therefore constitutes a promising variance reduction technique for computing proton dose distributions in a clinical context.

Abstract ID: 104 Efficiency improvement in proton dose calculations with an equivalent restricted stopping power formalism

To maintain the dose accuracy of Monte Carlo simulations, the mean energy loss calculation usually requires a step restriction (dmax). It leads to a O(n) algorithmic time complexity, where n is the subdivision number imposed by dmax. A new formalism is proposed to accelerate Monte Carlo dose calculations, allowing the removal of dmax in the step selection leading to a O(1) algorithmic time complexity. In this formalism, the midpoint rule of the Newton–Cotes formulae was used to solve the integral equation relating the mean energy loss to the step. The fractional energy loss was obtained with a secant method and a Gauss–Kronrod quadrature, revealing within the midpoint rule the equivalent restricted stopping power (Leq), used here as a key physical quantity. For any step, the mean energy loss was simply defined as the product of the step with Leq. Proton inelastic collisions with electrons were added to GPUMCD, a GPU-based Monte Carlo dose calculation code. The proton continuous slowing-down was modelled with the Leq formalism. First, the dose and time impacts of dmax were studied within Geant4. Second, in voxelized geometries, GPUMCD was compared to Geant4 using a high accuracy simulation setup (dmax = 10 μm). The ionization processes alone were activated and the energy straggling was first switched off to validate alone the Leq formalism. The default settings (dmax = 1 mm) in Geant4 led to an error of up to 16.5% in the falloff region, up to 4.8% elsewhere and the computation times were inversely proportional to the maximal step length allowed. Dose differences between Geant4 and GPUMCD were smaller than 0.31% in the Bragg peak for the Leq formalism. GPUMCD 80% falloff positions (R80) matched Geant R80 within 1 μm. With the energy straggling, dose agreements were within 2.7% in the falloff, below 0.83% elsewhere and R80 positions matched within 100 μm. The overall computation times per million transported protons with GPUMCD were 31–173 ms. Under similar conditions, Geant4 computation times were 1.4–20 h. The Leq formalism allows larger steps while preserving the accuracy. It significantly accelerates Monte Carlo proton transport. The Leq formalism constitutes a promising variance reduction technique for computing proton dose distributions in a clinical context.

MO-FG-202-08: Real-Time Monte Carlo-Based Treatment Dose Reconstruction and Monitoring for Radiotherapy

Purpose: This proof-of-concept study is to develop a real-time Monte Carlo (MC) based treatment-dose reconstruction and monitoring system for radiotherapy, especially for the treatments with complicated delivery, to catch treatment delivery errors at the earliest possible opportunity and interrupt the treatment only when an unacceptable dosimetric deviation from our expectation occurs. Methods: First an offline scheme is launched to pre-calculate the expected dose from the treatment plan, used as ground truth for real-time monitoring later. Then an online scheme with three concurrent threads is launched while treatment delivering, to reconstruct and monitor the patient dose in a temporally resolved fashion in real-time. Thread T1 acquires machine status every 20 ms to calculate and accumulate fluence map (FM). Once our accumulation threshold is reached, T1 transfers the FM to T2 for dose reconstruction ad starts to accumulate a new FM. A GPU-based MC dose calculation is performed on T2 when MC dose engine is ready and a new FM is available. The reconstructed instantaneous dose is directed to T3 for dose accumulation and real-time visualization. Multiple dose metrics (e.g. maximum and mean dose for targets and organs) are calculated from the current accumulated dose and compared with the pre-calculated expected values. Once the discrepancies go beyond our tolerance, an error message will be send to interrupt the treatment delivery. Results: A VMAT Head-and-neck patient case was used to test the performance of our system. Real-time machine status acquisition was simulated here. The differences between the actual dose metrics and the expected ones were 0.06%–0.36%, indicating an accurate delivery. ∼10Hz frequency of dose reconstruction and monitoring was achieved, with 287.94s online computation time compared to 287.84s treatment delivery time. Conclusion: Our study has demonstrated the feasibility of computing a dose distribution in a temporally resolved fashion in real-time and quantitatively and dosimetrically monitoring the treatment delivery.

Monte Carlo dose calculations for high-dose–rate brachytherapy using GPU-accelerated processing

PurposeCurrent clinical brachytherapy dose calculations are typically based on the Association of American Physicists in Medicine Task Group report 43 (TG-43) guidelines, which approximate patient geometry as an infinitely large water phantom. This ignores patient and applicator geometries and heterogeneities, causing dosimetric errors. Although Monte Carlo (MC) dose calculation is commonly recognized as the most accurate method, its associated long computational time is a major bottleneck for routine clinical applications. This article presents our recent developments of a fast MC dose calculation package for high-dose–rate (HDR) brachytherapy, gBMC, built on a graphics processing unit (GPU) platform. Methods and MaterialsgBMC-simulated photon transport in voxelized geometry with physics in 192Ir HDR brachytherapy energy range considered. A phase-space file was used as a source model. GPU-based parallel computation was used to simultaneously transport multiple photons, one on a GPU thread. We validated gBMC by comparing the dose calculation results in water with that computed TG-43. We also studied heterogeneous phantom cases and a patient case and compared gBMC results with Acuros BV results. ResultsRadial dose function in water calculated by gBMC showed <0.6% relative difference from that of the TG-43 data. Difference in anisotropy function was <1%. In two heterogeneous slab phantoms and one shielded cylinder applicator case, average dose discrepancy between gBMC and Acuros BV was <0.87%. For a tandem and ovoid patient case, good agreement between gBMC and Acruos BV results was observed in both isodose lines and dose-volume histograms. In terms of the efficiency, it took ∼47.5 seconds for gBMC to reach 0.15% statistical uncertainty within the 5% isodose line for the patient case. ConclusionsThe accuracy and efficiency of a new GPU-based MC dose calculation package, gBMC, for HDR brachytherapy make it attractive for clinical applications.

An analytic linear accelerator source model for GPU-based Monte Carlo dose calculations

Recently, there has been a lot of research interest in developing fast Monte Carlo (MC) dose calculation methods on graphics processing unit (GPU) platforms. A good linear accelerator (linac) source model is critical for both accuracy and efficiency considerations. In principle, an analytical source model should be more preferred for GPU-based MC dose engines than a phase-space file-based model, in that data loading and CPU-GPU data transfer can be avoided. In this paper, we presented an analytical field-independent source model specifically developed for GPU-based MC dose calculations, associated with a GPU-friendly sampling scheme. A key concept called phase-space-ring (PSR) was proposed. Each PSR contained a group of particles that were of the same type, close in energy and reside in a narrow ring on the phase-space plane located just above the upper jaws. The model parameterized the probability densities of particle location, direction and energy for each primary photon PSR, scattered photon PSR and electron PSR. Models of one 2D Gaussian distribution or multiple Gaussian components were employed to represent the particle direction distributions of these PSRs. A method was developed to analyze a reference phase-space file and derive corresponding model parameters. To efficiently use our model in MC dose calculations on GPU, we proposed a GPU-friendly sampling strategy, which ensured that the particles sampled and transported simultaneously are of the same type and close in energy to alleviate GPU thread divergences. To test the accuracy of our model, dose distributions of a set of open fields in a water phantom were calculated using our source model and compared to those calculated using the reference phase-space files. For the high dose gradient regions, the average distance-to-agreement (DTA) was within 1 mm and the maximum DTA within 2 mm. For relatively low dose gradient regions, the root-mean-square (RMS) dose difference was within 1.1% and the maximum dose difference within 1.7%. The maximum relative difference of output factors was within 0.5%. Over 98.5% passing rate was achieved in 3D gamma-index tests with 2%/2 mm criteria in both an IMRT prostate patient case and a head-and-neck case. These results demonstrated the efficacy of our model in terms of accurately representing a reference phase-space file. We have also tested the efficiency gain of our source model over our previously developed phase-space-let file source model. The overall efficiency of dose calculation was found to be improved by ~1.3–2.2 times in water and patient cases using our analytical model.

A study of potential numerical pitfalls in GPU-based Monte Carlo dose calculation

The purpose of this study was to evaluate the impact of numerical errors caused by the floating point representation of real numbers in a GPU-based Monte Carlo code used for dose calculation in radiation oncology, and to identify situations where this type of error arises. The program used as a benchmark was bGPUMCD. Three tests were performed on the code, which was divided into three functional components: energy accumulation, particle tracking and physical interactions. First, the impact of single-precision calculations was assessed for each functional component. Second, a GPU-specific compilation option that reduces execution time as well as precision was examined. Third, a specific function used for tracking and potentially more sensitive to precision errors was tested by comparing it to a very high-precision implementation.Numerical errors were found in two components of the program. Because of the energy accumulation process, a few voxels surrounding a radiation source end up with a lower computed dose than they should. The tracking system contained a series of operations that abnormally amplify rounding errors in some situations. This resulted in some rare instances (less than 0.1%) of computed distances that are exceedingly far from what they should have been. Most errors detected had no significant effects on the result of a simulation due to its random nature, either because they cancel each other out or because they only affect a small fraction of particles. The results of this work can be extended to other types of GPU-based programs and be used as guidelines to avoid numerical errors on the GPU computing platform.

TU-EF-304-07: Monte Carlo-Based Inverse Treatment Plan Optimization for Intensity Modulated Proton Therapy

Purpose: Intensity-modulated proton therapy (IMPT) is increasingly used in proton therapy. For IMPT optimization, Monte Carlo (MC) is desired for spots dose calculations because of its high accuracy, especially in cases with a high level of heterogeneity. It is also preferred in biological optimization problems due to the capability of computing quantities related to biological effects. However, MC simulation is typically too slow to be used for this purpose. Although GPU-based MC engines have become available, the achieved efficiency is still not ideal. The purpose of this work is to develop a new optimization scheme to include GPU-based MC into IMPT. Methods: A conventional approach using MC in IMPT simply calls the MC dose engine repeatedly for each spot dose calculations. However, this is not the optimal approach, because of the unnecessary computations on some spots that turned out to have very small weights after solving the optimization problem. GPU-memory writing conflict occurring at a small beam size also reduces computational efficiency. To solve these problems, we developed a new framework that iteratively performs MC dose calculations and plan optimizations. At each dose calculation step, the particles were sampled from different spots altogether with Metropolis algorithm, such that the particle number is proportional to the latest optimized spot intensity. Simultaneously transporting particles from multiple spots also mitigated the memory writing conflict problem. Results: We have validated the proposed MC-based optimization schemes in one prostate case. The total computation time of our method was ∼5–6 min on one NVIDIA GPU card, including both spot dose calculation and plan optimization, whereas a conventional method naively using the same GPU-based MC engine were ∼3 times slower. Conclusion: A fast GPU-based MC dose calculation method along with a novel optimization workflow is developed. The high efficiency makes it attractive for clinical usages.

GPU-Based Monte Carlo Dose Calculations for High-Dose-Rate Brachytherapy and Evaluations of TG-43 Accuracy in APBI Treatments With SAVI Devices

GPU-Based Monte Carlo Dose Calculations for High-Dose-Rate Brachytherapy and Evaluations of TG-43 Accuracy in APBI Treatments With SAVI Devices

SU‐E‐T‐29: A Web Application for GPU‐Based Monte Carlo IMRT/VMAT QA with Delivered Dose Verification

Purpose:To enable an existing web application for GPU‐based Monte Carlo (MC) 3D dosimetry quality assurance (QA) to compute “delivered dose” from linac logfile data.Methods:We added significant features to an IMRT/VMAT QA web application which is based on existing technologies (HTML5, Python, and Django). This tool interfaces with python, c‐code libraries, and command line‐based GPU applications to perform a MC‐based IMRT/VMAT QA. The web app automates many complicated aspects of interfacing clinical DICOM and logfile data with cutting‐edge GPU software to run a MC dose calculation. The resultant web app is powerful, easy to use, and is able to re‐compute both plan dose (from DICOM data) and delivered dose (from logfile data). Both dynalog and trajectorylog file formats are supported. Users upload zipped DICOM RP, CT, and RD data and set the expected statistic uncertainty for the MC dose calculation. A 3D gamma index map, 3D dose distribution, gamma histogram, dosimetric statistics, and DVH curves are displayed to the user. Additional the user may upload the delivery logfile data from the linac to compute a “delivered dose” calculation and corresponding gamma tests. A comprehensive PDF QA report summarizing the results can also be downloaded.Results:We successfully improved a web app for a GPU‐based QA tool that consists of logfile parcing, fluence map generation, CT image processing, GPU based MC dose calculation, gamma index calculation, and DVH calculation. The result is an IMRT and VMAT QA tool that conducts an independent dose calculation for a given treatment plan and delivery log file. The system takes both DICOM data and logfile data to compute plan dose and delivered dose respectively.Conclusion:We sucessfully improved a GPU‐based MC QA tool to allow for logfile dose calculation. The high efficiency and accessibility will greatly facilitate IMRT and VMAT QA.

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

GPU-based Monte Carlo radiotherapy dose calculation using phase-space sources

A novel phase-space source implementation has been designed for graphics processing unit (GPU)-based Monte Carlo dose calculation engines. Short of full simulation of the linac head, using a phase-space source is the most accurate method to model a clinical radiation beam in dose calculations. However, in GPU-based Monte Carlo dose calculations where the computation efficiency is very high, the time required to read and process a large phase-space file becomes comparable to the particle transport time. Moreover, due to the parallelized nature of GPU hardware, it is essential to simultaneously transport particles of the same type and similar energies but separated spatially to yield a high efficiency. We present three methods for phase-space implementation that have been integrated into the most recent version of the GPU-based Monte Carlo radiotherapy dose calculation package gDPM v3.0. The first method is to sequentially read particles from a patient-dependent phase-space and sort them on-the-fly based on particle type and energy. The second method supplements this with a simple secondary collimator model and fluence map implementation so that patient-independent phase-space sources can be used. Finally, as the third method (called the phase-space-let, or PSL, method) we introduce a novel source implementation utilizing pre-processed patient-independent phase-spaces that are sorted by particle type, energy and position. Position bins located outside a rectangular region of interest enclosing the treatment field are ignored, substantially decreasing simulation time with little effect on the final dose distribution. The three methods were validated in absolute dose against BEAMnrc/DOSXYZnrc and compared using gamma-index tests (2%/2 mm above the 10% isodose). It was found that the PSL method has the optimal balance between accuracy and efficiency and thus is used as the default method in gDPM v3.0. Using the PSL method, open fields of 4 × 4, 10 × 10 and 30 × 30 cm2 in water resulted in gamma passing rates of 99.96%, 99.92% and 98.66%, respectively. Relative output factors agreed within 1%. An intensity modulated radiation therapy patient plan using the PSL method resulted in a passing rate of 97%, and was calculated in 50 s (per GPU) compared to 8.4 h (per CPU) for BEAMnrc/DOSXYZnrc.

SU‐E‐T‐193: A Custom Web Application for a GPU‐Based Monte Carlo IMRT/VMAT QA Tool

Purpose: To develop a user friendly web application for a GPU‐based Monte Carlo (MC) 3D dosimetry quality assurance (QA) tool which runs in modern web browsers and interacts remotely with specialized hardware/software resources. Methods: We developed a new QA web application based on existing technologies (HTML5, Python, and Django) to interface with a command line‐based MC QA tool. It eliminates the need for users of this cutting‐edge GPU software to purchase expensive/specialized hardware and use a command‐line based environment. The Results is a web app with a user‐friendly interface enabling control of GPU accelerated software for MC‐based QA. Users upload zipped DICOM RP, CT, and RD data and set the expected statistic uncertainty for the MC dose calculation. After the files are uploaded, the remote software is automatically launched to generate intermediate data necessary for the GPU‐based MC dose calculation. The MC dose calculation is then executed on the remote GPU server and the resultant dose and original plan dose (from uploaded RD file) are displayed in the web GUI for the user to review. A 3D gamma index map and DVH curves are also displayed to the user. Finally, a PDF QA report that summarizes the results can be downloaded. Results: We successfully developed a web app for a GPU‐based QA tool that consists of fluence map generation, CT image processing, GPU based MC dose calculation, gamma index calculation, and DVH calculation. The Results is an IMRT and VMAT QA tool that conducts an independent dose calculation for a given treatment plan. The computation time from data uploading to viewing results and downloading report is less than 2 min. Conclusion: We developed a GPU‐based MC QA tool with a user‐friendly web‐based interface. The high efficiency and accessibility greatly facilitate IMRT and VMAT QA.

GPU-based fast Monte Carlo dose calculation for proton therapy

Accurate radiation dose calculation is essential for successful proton radiotherapy. Monte Carlo (MC) simulation is considered to be the most accurate method. However, the long computation time limits it from routine clinical applications. Recently, graphics processing units (GPUs) have been widely used to accelerate computationally intensive tasks in radiotherapy. We have developed a fast MC dose calculation package, gPMC, for proton dose calculation on a GPU. In gPMC, proton transport is modeled by the class II condensed history simulation scheme with a continuous slowing down approximation. Ionization, elastic and inelastic proton nucleus interactions are considered. Energy straggling and multiple scattering are modeled. Secondary electrons are not transported and their energies are locally deposited. After an inelastic nuclear interaction event, a variety of products are generated using an empirical model. Among them, charged nuclear fragments are terminated with energy locally deposited. Secondary protons are stored in a stack and transported after finishing transport of the primary protons, while secondary neutral particles are neglected. gPMC is implemented on the GPU under the CUDA platform. We have validated gPMC using the TOPAS/Geant4 MC code as the gold standard. For various cases including homogeneous and inhomogeneous phantoms as well as a patient case, good agreements between gPMC and TOPAS/Geant4 are observed. The gamma passing rate for the 2%/2 mm criterion is over 98.7% in the region with dose greater than 10% maximum dose in all cases, excluding low-density air regions. With gPMC it takes only 6–22 s to simulate 10 million source protons to achieve ∼1% relative statistical uncertainty, depending on the phantoms and energy. This is an extremely high efficiency compared to the computational time of tens of CPU hours for TOPAS/Geant4. Our fast GPU-based code can thus facilitate the routine use of MC dose calculation in proton therapy.

SU-E-T-476: GPU-Based Monte Carlo Radiotherapy Dose Calculation Using Phase- Space Sources.

To design an efficient method for utilizing phase-space source models in the GPU-based Monte Carlo (MC) dose calculation engine gDPM. In GPU-based MC algorithms, particles are transported in parallel on different threads. Particles of different types and energies can require significantly different execution times. This can cause "thread divergence" and lower efficiency when source particles are read sequentially from a phase-space file. We have developed a strategy for utilizing phase- space files in a GPU compatible manner whereby the particles are grouped into phase-space-lets (PSLs) by type, energy, and location in the phase- space plane. This allows for dose calculations using only particles inside the field opening defined by the secondary collimators. For validation, the gDPM PSL implementation is compared with DOSXYZnrc using a BEAMnrc phase-space source model as input. Two phase-spaces were generated using a BEAMnrc head model of a 6MV Varian Clinac 21EX, one above the upper jaws used to generate PSLs for gDPM and the other below the lower jaws used for DOSXYZnrc dose calculation. Profiles and depth dose curves for a variety of field sizes were generated in a water phantom. The agreement between gDPM and DOSXYZnrc is within 2% for all field sizes. For the 10 cm × 10 cm field, the calculation times of 650 million histories were 147 CPU hours and 54 GPU seconds for DOSXYZnrc and gDPM, respectively. In addition, we have tested the gDPM PSL implementation for dose calculation in a realistic 7-field IMRT tongue treatment plan. The calculation times were 59 CPU-hours and 66 GPU- seconds for DOSXYZnrc and gDPM for 485 million histories, respectively. Gamma pass rate for the two dose distributions was 99.54% for 3 mm/3% criteria within the 10% isodose. Methods for the efficient use of phase-space sources for GPU-based MC dose calculations have been developed.

SU-E-T-213: Development of a Web Wrapper to Facilitate Radiotherapy Research.

Researchers write many computer programs with unique implementations, usually requiring a great amount of effort for other researchers to learn how to install, configure, and use. Some programs require specialized hardware platforms such as GPU workstation or CPU cluster, which may not readily available for many researchers. This work develops a general web platform to 'wrap' radiotherapy software tools into a user friendly, browser-based interface. We developed a web wrapper based on existing technologies (e.g. HTML5, JavaScript, PHP, Python, XML) to interface with command line-based research tools. This wrapper enables users to easily perform various tasks in any modern web browser, while underlying tools are launched remotely. Visitors can upload data, configure settings, process data remotely, then view, share, and download results with minimal effort. This web wrapper is developer friendly; new tools are easily integrated by editing XML configuration files. As a test case, we have successfully wrapped a set of command line tools, developed by our group, into a single web app, providing fluence map generation, CT image processing, and GPU-based Monte Carlo (MC) dose calculation. The result is a web-based quality assurance tool. With this tool, users can upload compressed DICOM-RT files, recompute dose using the MC method, and evaluate the results by viewing dose distribution, 3D gamma index distribution and DVH curves. The entire work-flow can be completed within 2 minutes provided users have a reasonable Internet connection speed. We have developed an web wrapper to increase the accessibility of radiotherapy tools and reduce users' learning curve through a friendly web-based interface. This work also allows quick and easy deployment and distribution of software tools developed by researchers to the whole community.

Fast online Monte Carlo-based IMRT planning for the MRI linear accelerator

The MRI accelerator, a combination of a 6 MV linear accelerator with a 1.5 T MRI, facilitates continuous patient anatomy updates regarding translations, rotations and deformations of targets and organs at risk. Accounting for these demands high speed, online intensity-modulated radiotherapy (IMRT) re-optimization. In this paper, a fast IMRT optimization system is described which combines a GPU-based Monte Carlo dose calculation engine for online beamlet generation and a fast inverse dose optimization algorithm. Tightly conformal IMRT plans are generated for four phantom cases and two clinical cases (cervix and kidney) in the presence of the magnetic fields of 0 and 1.5 T. We show that for the presented cases the beamlet generation and optimization routines are fast enough for online IMRT planning. Furthermore, there is no influence of the magnetic field on plan quality and complexity, and equal optimization constraints at 0 and 1.5 T lead to almost identical dose distributions.

Development of a GPU-based Monte Carlo dose calculation code for coupled electron–photon transport

Monte Carlo simulation is the most accurate method for absorbed dose calculations in radiotherapy. Its efficiency still requires improvement for routine clinical applications, especially for online adaptive radiotherapy. In this paper, we report our recent development on a GPU-based Monte Carlo dose calculation code for coupled electron–photon transport. We have implemented the dose planning method (DPM) Monte Carlo dose calculation package (Sempau et al 2000 Phys. Med. Biol. 45 2263–91) on the GPU architecture under the CUDA platform. The implementation has been tested with respect to the original sequential DPM code on the CPU in phantoms with water–lung–water or water–bone–water slab geometry. A 20 MeV mono-energetic electron point source or a 6 MV photon point source is used in our validation. The results demonstrate adequate accuracy of our GPU implementation for both electron and photon beams in the radiotherapy energy range. Speed-up factors of about 5.0–6.6 times have been observed, using an NVIDIA Tesla C1060 GPU card against a 2.27 GHz Intel Xeon CPU processor.