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Shi M, Cui S, Chuang C, Oderinde O, Kovalchuk N, Surucu M, Xing L, Han B. A time- and space-saving Monte Carlo simulation method using post-collimation generative adversarial network for dose calculation of an O-ring gantry Linac. Phys Med 2024; 119:103318. [PMID: 38382210 DOI: 10.1016/j.ejmp.2024.103318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 02/06/2024] [Accepted: 02/09/2024] [Indexed: 02/23/2024] Open
Abstract
PURPOSE This study explores the feasibility of employing Generative Adversarial Networks (GANs) to model the RefleXion X1 Linac. The aim is to investigate the accuracy of dose simulation and assess the potential computational benefits. METHODS The X1 Linac is a new radiotherapy machine with a binary multi-leaf collimation (MLC) system, facilitating innovative biology-guided radiotherapy. A total of 34 GAN generators, each representing a desired MLC aperture, were developed. Each generator was trained using a phase space file generated underneath the corresponding aperture, enabling the generation of particles and serving as a beam source for Monte Carlo simulation. Dose distributions in water were simulated for each aperture using both the GAN and phase space sources. The agreement between dose distributions was evaluated. The computational time reduction from bypassing the collimation simulation and storage space savings were estimated. RESULTS The percentage depth dose at 10 cm, penumbra, and full-width half maximum of the GAN simulation agree with the phase space simulation, with differences of 0.4 % ± 0.2 %, 0.32 ± 0.66 mm, and 0.26 ± 0.44 mm, respectively. The gamma passing rate (1 %/1mm) for the planar dose exceeded 90 % for all apertures. The estimated time-saving for simulating an plan using 5766 beamlets was 530 CPU hours. The storage usage was reduced by a factor of 102. CONCLUSION The utilization of the GAN in simulating the X1 Linac demonstrated remarkable accuracy and efficiency. The reductions in both computational time and storage requirements make this approach highly valuable for future dosimetry studies and beam modeling.
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Affiliation(s)
- Mengying Shi
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA; Department of Radiation Oncology, University of California, Irvine, Orange, CA, USA.
| | - Sunan Cui
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA; Department of Radiation Oncology, University of Washington, Seattle, WA, USA
| | - Cynthia Chuang
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | | | - Nataliya Kovalchuk
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | - Murat Surucu
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
| | - Bin Han
- Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA
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Qin P, Lin G, Li X, Piao Z, Huang S, Wu W, Qi M, Ma J, Zhou L, Xu Y. A correlated sampling-based Monte Carlo simulation for fast CBCT iterative scatter correction. Med Phys 2023; 50:1466-1480. [PMID: 36323626 DOI: 10.1002/mp.16073] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 10/03/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND In recent years, cone-beam computed tomography (CBCT) has played an important role in medical imaging. However, the applications of CBCT are limited due to the severe scatter contamination. Conventional Monte Carlo (MC) simulation can provide accurate scatter estimation for scatter correction, but the expensive computational cost has always been the bottleneck of MC method in clinical application. PURPOSE In this work, an MC simulation method combined with a variance reduction technique called correlated sampling is proposed for fast iterative scatter correction. METHODS Correlated sampling exploits correlation between similar simulation systems to reduce the variance of interest quantities. Specifically, conventional MC simulation is first performed on the scatter-contaminated CBCT to generate the initial scatter signal. In the subsequent correction iterations, scatter estimation is then updated by applying correlated MC sampling to the latest corrected CBCT images by reusing the random number sequences of the task-related photons in conventional MC. Afterward, the corrected projections obtained by subtracting the scatter estimation from raw projections are utilized for FDK reconstruction. These steps are repeated until an adequate scatter correction is obtained. The performance of the proposed framework is evaluated by the accuracy of the scatter estimation, the quality of corrected CBCT images and efficiency. RESULTS Overall, the difference in mean absolute percentage error between scatter estimation with and without correlated sampling is 0.25% for full-fan case and 0.34% for half-fan case, respectively. In simulation studies, scatter artifacts are substantially eliminated, where the mean absolute error value is reduced from 15 to 2 HU in full-fan case and from 53 to 13 HU in half-fan case. Scatter-to-primary ratio is reduced to 0.02 for full-fan and 0.04 for half-fan, respectively. In phantom study, the contrast-to-noise ratio (CNR) is increased by a factor of 1.63, and the contrast is increased by a factor of 1.77. As for clinical studies, the CNR is improved by 11% and 14% for half-fan and full-fan, respectively. The contrast after correction is increased by 19% for half-fan and 44% for full-fan. Furthermore, root mean square error is also effectively reduced, especially from 78 to 4 HU for full-fan. Experimental results demonstrate that the figure of merit is improved between 23 and 43 folds when using correlated sampling. The proposed method takes less than 25 s for the whole iterative scatter correction process. CONCLUSIONS The proposed correlated sampling-based MC simulation method can achieve fast and accurate scatter correction for CBCT, making it suitable for real-time clinical use.
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Affiliation(s)
- Peishan Qin
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Guoqin Lin
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Xu Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Zun Piao
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Shuang Huang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - WangJiang Wu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Mengke Qi
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Jianhui Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Linghong Zhou
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Yuan Xu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
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Bedford JL, Nilawar R, Nill S, Oelfke U. A phase space model of a Versa HD linear accelerator for application to Monte Carlo dose calculation in a real-time adaptive workflow. J Appl Clin Med Phys 2022; 23:e13663. [PMID: 35699201 PMCID: PMC9512357 DOI: 10.1002/acm2.13663] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 05/09/2022] [Accepted: 05/13/2022] [Indexed: 11/11/2022] Open
Abstract
PURPOSE This study aims to develop and validate a simple geometric model of the accelerator head, from which a particle phase space can be calculated for application to fast Monte Carlo dose calculation in real-time adaptive photon radiotherapy. With this objective in view, the study investigates whether the phase space model can facilitate dose calculations which are compatible with those of a commercial treatment planning system, for convenient interoperability. MATERIALS AND METHODS A dual-source model of the head of a Versa HD accelerator (Elekta AB, Stockholm, Sweden) was created. The model used parameters chosen to be compatible with those of 6-MV flattened and 6-MV flattening filter-free photon beams in the RayStation treatment planning system (RaySearch Laboratories, Stockholm, Sweden). The phase space model was used to calculate a photon phase space for several treatment plans, and the resulting phase space was applied to the Dose Planning Method (DPM) Monte Carlo dose calculation algorithm. Simple fields and intensity-modulated radiation therapy (IMRT) treatment plans for prostate and lung were calculated for benchmarking purposes and compared with the convolution-superposition dose calculation within RayStation. RESULTS For simple square fields in a water phantom, the calculated dose distribution agrees to within ±2% with that from the commercial treatment planning system, except in the buildup region, where the DPM code does not model the electron contamination. For IMRT plans of prostate and lung, agreements of ±2% and ±6%, respectively, are found, with slightly larger differences in the high dose gradients. CONCLUSIONS The phase space model presented allows convenient calculation of a phase space for application to Monte Carlo dose calculation, with straightforward translation of beam parameters from the RayStation beam model. This provides a basis on which to develop dose calculation in a real-time adaptive setting.
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Affiliation(s)
- James L. Bedford
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonUK
| | - Rahul Nilawar
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonUK
| | - Simeon Nill
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonUK
| | - Uwe Oelfke
- Joint Department of PhysicsThe Institute of Cancer Research and The Royal Marsden NHS Foundation TrustLondonUK
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Loirec CL, Hernandez N. Technical Note: Development of a generalized source model for flux estimation in nuclear reactors. ANN NUCL ENERGY 2022. [DOI: 10.1016/j.anucene.2021.108776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Cui Z, Sha S, Bai Y. A structural analytic method on the phase space data of Linac 4 MV photons based on the real world. Phys Med 2021; 82:109-113. [PMID: 33610005 DOI: 10.1016/j.ejmp.2021.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 02/07/2021] [Accepted: 02/08/2021] [Indexed: 10/22/2022] Open
Abstract
PURPOSE It was given that the characteristics of the fluence distribution and the energy spectrum structure of 4MV photons on the Phase Space (PhSp) plane and a method to analyzing the characteristics. METHODS After the PhSp file of 4 MV photons was acquired by the method of Monte Carlo (MC) calculation, the photons recorded by PhSp file were grouped based on the energy bin, and it was analyzed that the spatial distribution and energy spectrum structure of the photons. The photons in each energy group were continually grouped to sub-files according to momentum bin, and the primary and scattered photons could be separated according to the character of the fluence distribution of the photons in the sub-files. RESULTS The energy of 4 MV beam is a continuous spectrum. The energy constituent on a pixel at different distances from the center point is different, and the average energy on the center axis of the field is the highest; The photons with 0-1.0 MeV had 42.6% of all; that with energy more than 3.0 MeV had 11.7%; greater than 4 MeV, just 1.5%. The primary and scattered photons were easy collected according to the distribution characteristics of sub-groups. CONCLUSIONS The work to acquire and analyze the PhSp file of the 4 MV beam is significant. 4 MV, 6 MV, 8 MV, 10 MV and 15 MV energy beams basically cover the beams of radiotherapy, and a database of the energy beams could be built for the MC related research of other scholars.
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Affiliation(s)
- Zhenguo Cui
- Department of Radiation Physics, Harbin Medical University Cancer Hospital, 150081 Harbin, Heilongjiang Province, PR China
| | - Songlin Sha
- Department of Radiation Physics, Harbin Medical University Cancer Hospital, 150081 Harbin, Heilongjiang Province, PR China
| | - Yanling Bai
- Department of Radiation Physics, Harbin Medical University Cancer Hospital, 150081 Harbin, Heilongjiang Province, PR China.
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Shi M, Myronakis M, Jacobson M, Ferguson D, Williams C, Lehmann M, Baturin P, Huber P, Fueglistaller R, Lozano IV, Harris T, Morf D, Berbeco RI. GPU-accelerated Monte Carlo simulation of MV-CBCT. Phys Med Biol 2020; 65:235042. [PMID: 33263311 DOI: 10.1088/1361-6560/abaeba] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Monte Carlo simulation (MCS) is one of the most accurate computation methods for dose calculation and image formation in radiation therapy. However, the high computational complexity and long execution time of MCS limits its broad use. In this paper, we present a novel strategy to accelerate MCS using a graphic processing unit (GPU), and we demonstrate the application in mega-voltage (MV) cone-beam computed tomography (CBCT) simulation. A new framework that generates a series of MV projections from a single simulation run is designed specifically for MV-CBCT acquisition. A Geant4-based GPU code for photon simulation is incorporated into the framework for the simulation of photon transport through a phantom volume. The FastEPID method, which accelerates the simulation of MV images, is modified and integrated into the framework. The proposed GPU-based simulation strategy was tested for its accuracy and efficiency in a Catphan 604 phantom and an anthropomorphic pelvis phantom with beam energies at 2.5 MV, 6 MV, and 6 MV FFF. In all cases, the proposed GPU-based simulation demonstrated great simulation accuracy and excellent agreement with measurement and CPU-based simulation in terms of reconstructed image qualities. The MV-CBCT simulation was accelerated by factors of roughly 900-2300 using an NVIDIA Tesla V100 GPU card against a 2.5 GHz AMD Opteron™ Processor 6380.
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Affiliation(s)
- Mengying Shi
- Medical Physics Program, Department of Physics and Applied Physics, University of Massachusetts Lowell, Lowell, MA, United States of America. Brigham and Women's Hospital, Dana Farber Cancer Institute and Harvard Medical School, Boston, MA, United States of America
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7
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Shi M, Myronakis M, Jacobson M, Lehmann M, Ferguson D, Baturin P, Huber P, Fueglistaller R, Harris T, Lozano IV, Williams C, Morf D, Berbeco RI. A rapid, accurate image simulation strategy for mega-voltage cone-beam computed tomography. ACTA ACUST UNITED AC 2020; 65:135004. [DOI: 10.1088/1361-6560/ab868a] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Mirzapour M, Hadad K, Faghihi R, Hamilton RJ, Watchman CJ. Fast Monte-Carlo Photon Transport Employing GPU-Based Parallel Computation. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020. [DOI: 10.1109/trpms.2020.2972202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Adam DP, Liu T, Caracappa PF, Bednarz BP, Xu XG. New capabilities of the Monte Carlo dose engine ARCHER-RT: Clinical validation of the Varian TrueBeam machine for VMAT external beam radiotherapy. Med Phys 2020; 47:2537-2549. [PMID: 32175615 DOI: 10.1002/mp.14143] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 02/03/2020] [Accepted: 02/10/2020] [Indexed: 11/08/2022] Open
Abstract
PURPOSE The Monte Carlo radiation transport method is considered the most accurate approach for absorbed dose calculations in external beam radiation therapy. In this study, an efficient and accurate source model of the Varian TrueBeam 6X STx Linac is developed and integrated with a fast Monte Carlo photon-electron transport absorbed dose engine, ARCHER-RT, which is capable of being executed on CPUs, NVIDIA GPUs, and AMD GPUs. This capability of fast yet accurate radiation dose calculation is essential for clinical utility of this new technology. This paper describes the software and algorithmic developments made to the ARCHER-RT absorbed dose engine. METHODS AMD's Heterogeneous-Compute Interface for Portability (HIP) was implemented in ARCHER-RT to allow for device independent execution on NVIDIA and AMD GPUs. Architecture-specific atomic-add algorithms have been identified and both more accurate single-precision and double-precision computational absorbed dose calculation methods have been added to ARCHER-RT and validated through a test case to evaluate the accuracy and performance of the algorithms. The validity of the source model and the radiation transport physics were benchmarked against Monte Carlo simulations performed with EGSnrc. Secondary dose-check physics plans, and a clinical prostate treatment plan were calculated to demonstrate the applicability of the platform for clinical use. Absorbed dose difference maps and gamma analyses were conducted to establish the accuracy and consistency between the two Monte Carlo models. Timing studies were conducted on a CPU, an NVIDIA GPU, and an AMD GPU to evaluate the computational speed of ARCHER-RT. RESULTS Percent depth doses were computed for different field sizes ranging from 1.5 cm2 × 1.5 cm2 to 22 cm2 × 40cm2 and the two codes agreed for all points outside high gradient regions within 3%. Axial profiles computed for a 10 cm2 × 10 cm2 field for multiple depths agreed for all points outside high gradient regions within 2%. The test case investigating the impact of native single-precision compared to double-precision showed differences in voxels as large as 71.47% and the implementation of KAS single-precision reduced the difference to less than 0.01%. The 3%/3mm gamma pass rates for an MPPG5a multileaf collimator (MLC) test case and a clinical VMAT prostate plan were 94.2% and 98.4% respectively. Timing studies demonstrated the calculation of a VMAT plan was completed in 50.3, 187.9, and 216.8 s on an NVIDIA GPU, AMD GPU, and Intel CPU, respectively. CONCLUSION ARCHER-RT is capable of patient-specific VMAT external beam photon absorbed dose calculations and its potential has been demonstrated by benchmarking against a well validated EGSnrc model of a Varian TrueBeam. Additionally, the implementation of AMD's HIP has shown the flexibility of the ARCHER-RT platform for device independent calculations. This work demonstrates the significant addition of functionality added to ARCHER-RT framework which has marked utility for both research and clinical applications and demonstrates further that Monte Carlo-based absorbed dose engines like ARCHER-RT have the potential for widespread clinical implementation.
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Affiliation(s)
- David P Adam
- Medical Physics, University of Wisconsin Madison, 1111 Highland Avenue, Madison, WI, 53705, USA
| | - Tianyu Liu
- Nuclear Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180, USA
| | | | - Bryan P Bednarz
- Medical Physics, University of Wisconsin Madison, 1111 Highland Avenue, Madison, WI, 53705, USA
| | - Xie George Xu
- Nuclear Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180, USA
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Townson R, Egglestone H, Zavgorodni S. A fast jaw-tracking model for VMAT and IMRT Monte Carlo simulations. J Appl Clin Med Phys 2018; 19:26-34. [PMID: 29745009 PMCID: PMC6036353 DOI: 10.1002/acm2.12343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2017] [Revised: 01/25/2018] [Accepted: 02/13/2018] [Indexed: 12/17/2022] Open
Abstract
Modern radiotherapy techniques involve routine use of volumetric arc therapy (VMAT) and intensity modulated radiotherapy (IMRT) with jaw‐tracking – dynamic motion of the secondary collimators (jaws) in tandem with multi‐leaf collimators (MLCs). These modalities require accurate dose calculations for the purposes of treatment planning and dose verification. Monte Carlo (MC) methods for radiotherapy dose calculation are widely accepted as capable of achieving high accuracy. This paper presents an efficiency‐enhancement method for secondary collimator modeling, presented in the context of a tool for MC‐based dose second checks. The model constitutes an accuracy trade‐off in the source model for the sake of efficiency enhancement, but maintains the advantages of MC transport in patient heterogeneities. The secondary collimator model is called Flat‐Absorbing‐Jaw‐Tracking (FAJT). Transmission through and scatter from the secondary collimators is neglected, and jaws are modeled as perfectly absorbing planes. To couple the motion of secondary collimators with MLCs for jaw‐tracking, the FAJT model was built into the VCU‐MLC model. Gamma‐index analysis of the dose distributions from FAJT against the full BEAMnrc MC simulations showed over 99% pass rate for a range of open fields, two clinical IMRT, and one VMAT treatment plan, for 2%/2 mm criteria above 10%. Using FAJT, the simulation speed of the secondary collimators for open fields increased by a factor of 237, 1489, and 1395 for 4 × 4, 10 × 10, and 30 × 30 cm2, respectively. In general, clinically oriented simulation times are reduced from “hours” to “minutes” on identical hardware. Results for nine representative clinical cases (seven with jaw‐tracking) are presented. The average 2%/2 mm γ‐test success rate above the 80% isodose was 96.8% when tested against the EPIDose electronic portal image‐based dose reconstruction method and 97.3% against the Eclipse analytical anisotropic algorithm.
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Affiliation(s)
- Reid Townson
- Measurement Science and Standards, National Research Council Canada, Ottawa, ON, Canada
| | - Hilary Egglestone
- Department of Physics and Astronomy, University of Victoria, Victoria, BC, Canada.,Department of Medical Physics, BC Cancer Agency, Vancouver Island Centre, Victoria, BC, Canada
| | - Sergei Zavgorodni
- Department of Medical Physics, BC Cancer Agency, Vancouver Island Centre, Victoria, BC, Canada
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GATE Monte Carlo simulation of dose distribution using MapReduce in a cloud computing environment. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2017; 40:777-783. [PMID: 28861861 DOI: 10.1007/s13246-017-0580-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 08/18/2017] [Indexed: 10/19/2022]
Abstract
The GATE Monte Carlo simulation platform has good application prospects of treatment planning and quality assurance. However, accurate dose calculation using GATE is time consuming. The purpose of this study is to implement a novel cloud computing method for accurate GATE Monte Carlo simulation of dose distribution using MapReduce. An Amazon Machine Image installed with Hadoop and GATE is created to set up Hadoop clusters on Amazon Elastic Compute Cloud (EC2). Macros, the input files for GATE, are split into a number of self-contained sub-macros. Through Hadoop Streaming, the sub-macros are executed by GATE in Map tasks and the sub-results are aggregated into final outputs in Reduce tasks. As an evaluation, GATE simulations were performed in a cubical water phantom for X-ray photons of 6 and 18 MeV. The parallel simulation on the cloud computing platform is as accurate as the single-threaded simulation on a local server and the simulation correctness is not affected by the failure of some worker nodes. The cloud-based simulation time is approximately inversely proportional to the number of worker nodes. For the simulation of 10 million photons on a cluster with 64 worker nodes, time decreases of 41× and 32× were achieved compared to the single worker node case and the single-threaded case, respectively. The test of Hadoop's fault tolerance showed that the simulation correctness was not affected by the failure of some worker nodes. The results verify that the proposed method provides a feasible cloud computing solution for GATE.
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Tian Z, Zhang M, Hrycushko B, Albuquerque K, Jiang SB, Jia X. Monte Carlo dose calculations for high-dose-rate brachytherapy using GPU-accelerated processing. Brachytherapy 2017; 15:387-398. [PMID: 27216118 DOI: 10.1016/j.brachy.2016.01.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2015] [Revised: 01/26/2016] [Accepted: 01/27/2016] [Indexed: 11/24/2022]
Abstract
PURPOSE Current 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 MATERIALS gBMC-simulated photon transport in voxelized geometry with physics in (192)Ir 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. RESULTS Radial 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. CONCLUSIONS The accuracy and efficiency of a new GPU-based MC dose calculation package, gBMC, for HDR brachytherapy make it attractive for clinical applications.
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Affiliation(s)
- Z Tian
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX.
| | - M Zhang
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ
| | - B Hrycushko
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX
| | - K Albuquerque
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX
| | - S B Jiang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX
| | - X Jia
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX.
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Ziegenhein P, Kozin IN, Kamerling CP, Oelfke U. Towards real-time photon Monte Carlo dose calculation in the cloud. Phys Med Biol 2017; 62:4375-4389. [PMID: 28141583 DOI: 10.1088/1361-6560/aa5d4e] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Near real-time application of Monte Carlo (MC) dose calculation in clinic and research is hindered by the long computational runtimes of established software. Currently, fast MC software solutions are available utilising accelerators such as graphical processing units (GPUs) or clusters based on central processing units (CPUs). Both platforms are expensive in terms of purchase costs and maintenance and, in case of the GPU, provide only limited scalability. In this work we propose a cloud-based MC solution, which offers high scalability of accurate photon dose calculations. The MC simulations run on a private virtual supercomputer that is formed in the cloud. Computational resources can be provisioned dynamically at low cost without upfront investment in expensive hardware. A client-server software solution has been developed which controls the simulations and transports data to and from the cloud efficiently and securely. The client application integrates seamlessly into a treatment planning system. It runs the MC simulation workflow automatically and securely exchanges simulation data with the server side application that controls the virtual supercomputer. Advanced encryption standards were used to add an additional security layer, which encrypts and decrypts patient data on-the-fly at the processor register level. We could show that our cloud-based MC framework enables near real-time dose computation. It delivers excellent linear scaling for high-resolution datasets with absolute runtimes of 1.1 seconds to 10.9 seconds for simulating a clinical prostate and liver case up to 1% statistical uncertainty. The computation runtimes include the transportation of data to and from the cloud as well as process scheduling and synchronisation overhead. Cloud-based MC simulations offer a fast, affordable and easily accessible alternative for near real-time accurate dose calculations to currently used GPU or cluster solutions.
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Affiliation(s)
- Peter Ziegenhein
- Joint Department of Physics at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, SM2 5NG, United Kingdom
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Qin N, Pinto M, Tian Z, Dedes G, Pompos A, Jiang SB, Parodi K, Jia X. Initial development of goCMC: a GPU-oriented fast cross-platform Monte Carlo engine for carbon ion therapy. Phys Med Biol 2017; 62:3682-3699. [PMID: 28140352 PMCID: PMC5730973 DOI: 10.1088/1361-6560/aa5d43] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Monte Carlo (MC) simulation is considered as the most accurate method for calculation of absorbed dose and fundamental physics quantities related to biological effects in carbon ion therapy. To improve its computational efficiency, we have developed a GPU-oriented fast MC package named goCMC, for carbon therapy. goCMC simulates particle transport in voxelized geometry with kinetic energy up to 450 MeV u-1. Class II condensed history simulation scheme with a continuous slowing down approximation was employed. Energy straggling and multiple scattering were modeled. δ-electrons were terminated with their energy locally deposited. Four types of nuclear interactions were implemented in goCMC, i.e. carbon-hydrogen, carbon-carbon, carbon-oxygen and carbon-calcium inelastic collisions. Total cross section data from Geant4 were used. Secondary particles produced in these interactions were sampled according to particle yield with energy and directional distribution data derived from Geant4 simulation results. Secondary charged particles were transported following the condensed history scheme, whereas secondary neutral particles were ignored. goCMC was developed under OpenCL framework and is executable on different platforms, e.g. GPU and multi-core CPU. We have validated goCMC with Geant4 in cases with different beam energy and phantoms including four homogeneous phantoms, one heterogeneous half-slab phantom, and one patient case. For each case [Formula: see text] carbon ions were simulated, such that in the region with dose greater than 10% of maximum dose, the mean relative statistical uncertainty was less than 1%. Good agreements for dose distributions and range estimations between goCMC and Geant4 were observed. 3D gamma passing rates with 1%/1 mm criterion were over 90% within 10% isodose line except in two extreme cases, and those with 2%/1 mm criterion were all over 96%. Efficiency and code portability were tested with different GPUs and CPUs. Depending on the beam energy and voxel size, the computation time to simulate [Formula: see text] carbons was 9.9-125 s, 2.5-50 s and 60-612 s on an AMD Radeon GPU card, an NVidia GeForce GTX 1080 GPU card and an Intel Xeon E5-2640 CPU, respectively. The combined accuracy, efficiency and portability make goCMC attractive for research and clinical applications in carbon ion therapy.
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Affiliation(s)
- Nan Qin
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America
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Tian Z, Li Y, Hassan-Rezaeian N, Jiang SB, Jia X. Moving GPU-OpenCL-based Monte Carlo dose calculation toward clinical use: Automatic beam commissioning and source sampling for treatment plan dose calculation. J Appl Clin Med Phys 2017; 18:69-84. [PMID: 28300376 PMCID: PMC5689963 DOI: 10.1002/acm2.12049] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Revised: 11/17/2016] [Accepted: 12/20/2016] [Indexed: 11/24/2022] Open
Abstract
We have previously developed a GPU‐based Monte Carlo (MC) dose engine on the OpenCL platform, named goMC, with a built‐in analytical linear accelerator (linac) beam model. In this paper, we report our recent improvement on goMC to move it toward clinical use. First, we have adapted a previously developed automatic beam commissioning approach to our beam model. The commissioning was conducted through an optimization process, minimizing the discrepancies between calculated dose and measurement. We successfully commissioned six beam models built for Varian TrueBeam linac photon beams, including four beams of different energies (6 MV, 10 MV, 15 MV, and 18 MV) and two flattening‐filter‐free (FFF) beams of 6 MV and 10 MV. Second, to facilitate the use of goMC for treatment plan dose calculations, we have developed an efficient source particle sampling strategy. It uses the pre‐generated fluence maps (FMs) to bias the sampling of the control point for source particles already sampled from our beam model. It could effectively reduce the number of source particles required to reach a statistical uncertainty level in the calculated dose, as compared to the conventional FM weighting method. For a head‐and‐neck patient treated with volumetric modulated arc therapy (VMAT), a reduction factor of ~2.8 was achieved, accelerating dose calculation from 150.9 s to 51.5 s. The overall accuracy of goMC was investigated on a VMAT prostate patient case treated with 10 MV FFF beam. 3D gamma index test was conducted to evaluate the discrepancy between our calculated dose and the dose calculated in Varian Eclipse treatment planning system. The passing rate was 99.82% for 2%/2 mm criterion and 95.71% for 1%/1 mm criterion. Our studies have demonstrated the effectiveness and feasibility of our auto‐commissioning approach and new source sampling strategy for fast and accurate MC dose calculations for treatment plans.
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Affiliation(s)
- Zhen Tian
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Yongbao Li
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.,School of Astronautics, Beihang University, Beijing, 100191, China
| | - Nima Hassan-Rezaeian
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Steve B Jiang
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Xun Jia
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
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Lin H, Liu T, Su L, Bednarz B, Caracappa P, Xu XG. Modeling of Radiotherapy Linac Source Terms Using ARCHER Monte Carlo Code: Performance Comparison for GPU and MIC Parallel Computing Devices. EPJ WEB OF CONFERENCES 2017. [DOI: 10.1051/epjconf/201715304010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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Tian Z, Li Y, Folkerts M, Shi F, Jiang SB, Jia X. An analytic linear accelerator source model for GPU-based Monte Carlo dose calculations. Phys Med Biol 2015; 60:7941-67. [PMID: 26418216 DOI: 10.1088/0031-9155/60/20/7941] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
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.
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Affiliation(s)
- Zhen Tian
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Tian Z, Shi F, Folkerts M, Qin N, Jiang SB, Jia X. A GPU OpenCL based cross-platform Monte Carlo dose calculation engine (goMC). Phys Med Biol 2015; 60:7419-35. [DOI: 10.1088/0031-9155/60/19/7419] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Li Y, Tian Z, Shi F, Song T, Wu Z, Liu Y, Jiang S, Jia X. A new Monte Carlo-based treatment plan optimization approach for intensity modulated radiation therapy. Phys Med Biol 2015; 60:2903-19. [DOI: 10.1088/0031-9155/60/7/2903] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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Su L, Yang Y, Bednarz B, Sterpin E, Du X, Liu T, Ji W, Xu XG. ARCHERRT - a GPU-based and photon-electron coupled Monte Carlo dose computing engine for radiation therapy: software development and application to helical tomotherapy. Med Phys 2015; 41:071709. [PMID: 24989378 DOI: 10.1118/1.4884229] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
PURPOSE Using the graphical processing units (GPU) hardware technology, an extremely fast Monte Carlo (MC) code ARCHERRT is developed for radiation dose calculations in radiation therapy. This paper describes the detailed software development and testing for three clinical TomoTherapy® cases: the prostate, lung, and head & neck. METHODS To obtain clinically relevant dose distributions, phase space files (PSFs) created from optimized radiation therapy treatment plan fluence maps were used as the input to ARCHERRT. Patient-specific phantoms were constructed from patient CT images. Batch simulations were employed to facilitate the time-consuming task of loading large PSFs, and to improve the estimation of statistical uncertainty. Furthermore, two different Woodcock tracking algorithms were implemented and their relative performance was compared. The dose curves of an Elekta accelerator PSF incident on a homogeneous water phantom were benchmarked against DOSXYZnrc. For each of the treatment cases, dose volume histograms and isodose maps were produced from ARCHERRT and the general-purpose code, GEANT4. The gamma index analysis was performed to evaluate the similarity of voxel doses obtained from these two codes. The hardware accelerators used in this study are one NVIDIA K20 GPU, one NVIDIA K40 GPU, and six NVIDIA M2090 GPUs. In addition, to make a fairer comparison of the CPU and GPU performance, a multithreaded CPU code was developed using OpenMP and tested on an Intel E5-2620 CPU. RESULTS For the water phantom, the depth dose curve and dose profiles from ARCHERRT agree well with DOSXYZnrc. For clinical cases, results from ARCHERRT are compared with those from GEANT4 and good agreement is observed. Gamma index test is performed for voxels whose dose is greater than 10% of maximum dose. For 2%/2mm criteria, the passing rates for the prostate, lung case, and head & neck cases are 99.7%, 98.5%, and 97.2%, respectively. Due to specific architecture of GPU, modified Woodcock tracking algorithm performed inferior to the original one. ARCHERRT achieves a fast speed for PSF-based dose calculations. With a single M2090 card, the simulations cost about 60, 50, 80 s for three cases, respectively, with the 1% statistical error in the PTV. Using the latest K40 card, the simulations are 1.7-1.8 times faster. More impressively, six M2090 cards could finish the simulations in 8.9-13.4 s. For comparison, the same simulations on Intel E5-2620 (12 hyperthreading) cost about 500-800 s. CONCLUSIONS ARCHERRT was developed successfully to perform fast and accurate MC dose calculation for radiotherapy using PSFs and patient CT phantoms.
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Affiliation(s)
- Lin Su
- Nuclear Engineering Program, Rensselaer Polytechnic Institute, Troy, New York 12180
| | - Youming Yang
- Medical Physics, University of Wisconsin, Madison, Wisconsin 53706
| | - Bryan Bednarz
- Medical Physics, University of Wisconsin, Madison, Wisconsin 53706
| | - Edmond Sterpin
- Molecular Imaging, Radiotherapy and Oncology, Université catholique de Louvain, Brussels, Belgium 1348
| | - Xining Du
- Nuclear Engineering Program, Rensselaer Polytechnic Institute, Troy, New York 12180
| | - Tianyu Liu
- Nuclear Engineering Program, Rensselaer Polytechnic Institute, Troy, New York 12180
| | - Wei Ji
- Nuclear Engineering Program, Rensselaer Polytechnic Institute, Troy, New York 12180
| | - X George Xu
- Nuclear Engineering Program, Rensselaer Polytechnic Institute, Troy, New York 12180
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Townson RW, Zavgorodni S. A hybrid phase-space and histogram source model for GPU-based Monte Carlo radiotherapy dose calculation. Phys Med Biol 2014; 59:7919-35. [PMID: 25426972 DOI: 10.1088/0031-9155/59/24/7919] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
In GPU-based Monte Carlo simulations for radiotherapy dose calculation, source modelling from a phase-space source can be an efficiency bottleneck. Previously, this has been addressed using phase-space-let (PSL) sources, which provided significant efficiency enhancement. We propose that additional speed-up can be achieved through the use of a hybrid primary photon point source model combined with a secondary PSL source. A novel phase-space derived and histogram-based implementation of this model has been integrated into gDPM v3.0. Additionally, a simple method for approximately deriving target photon source characteristics from a phase-space that does not contain inheritable particle history variables (LATCH) has been demonstrated to succeed in selecting over 99% of the true target photons with only ~0.3% contamination (for a Varian 21EX 18 MV machine). The hybrid source model was tested using an array of open fields for various Varian 21EX and TrueBeam energies, and all cases achieved greater than 97% chi-test agreement (the mean was 99%) above the 2% isodose with 1% / 1 mm criteria. The root mean square deviations (RMSDs) were less than 1%, with a mean of 0.5%, and the source generation time was 4-5 times faster. A seven-field intensity modulated radiation therapy patient treatment achieved 95% chi-test agreement above the 10% isodose with 1% / 1 mm criteria, 99.8% for 2% / 2 mm, a RMSD of 0.8%, and source generation speed-up factor of 2.5.
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Affiliation(s)
- Reid W Townson
- Department of Physics and Astronomy, University of Victoria, Victoria, British Columbia, Canada. Department of Medical Physics, BC Cancer Agency, Vancouver Island Centre, Victoria, British Columbia, Canada
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Kearney V, Chen S, Gu X, Chiu T, Liu H, Jiang L, Wang J, Yordy J, Nedzi L, Mao W. Automated landmark-guided deformable image registration. Phys Med Biol 2014; 60:101-16. [DOI: 10.1088/0031-9155/60/1/101] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Tian Z, Graves YJ, Jia X, Jiang SB. Automatic commissioning of a GPU-based Monte Carlo radiation dose calculation code for photon radiotherapy. Phys Med Biol 2014; 59:6467-86. [PMID: 25295381 DOI: 10.1088/0031-9155/59/21/6467] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Monte Carlo (MC) simulation is commonly considered as the most accurate method for radiation dose calculations. Commissioning of a beam model in the MC code against a clinical linear accelerator beam is of crucial importance for its clinical implementation. In this paper, we propose an automatic commissioning method for our GPU-based MC dose engine, gDPM. gDPM utilizes a beam model based on a concept of phase-space-let (PSL). A PSL contains a group of particles that are of the same type and close in space and energy. A set of generic PSLs was generated by splitting a reference phase-space file. Each PSL was associated with a weighting factor, and in dose calculations the particle carried a weight corresponding to the PSL where it was from. Dose for each PSL in water was pre-computed, and hence the dose in water for a whole beam under a given set of PSL weighting factors was the weighted sum of the PSL doses. At the commissioning stage, an optimization problem was solved to adjust the PSL weights in order to minimize the difference between the calculated dose and measured one. Symmetry and smoothness regularizations were utilized to uniquely determine the solution. An augmented Lagrangian method was employed to solve the optimization problem. To validate our method, a phase-space file of a Varian TrueBeam 6 MV beam was used to generate the PSLs for 6 MV beams. In a simulation study, we commissioned a Siemens 6 MV beam on which a set of field-dependent phase-space files was available. The dose data of this desired beam for different open fields and a small off-axis open field were obtained by calculating doses using these phase-space files. The 3D γ-index test passing rate within the regions with dose above 10% of dmax dose for those open fields tested was improved averagely from 70.56 to 99.36% for 2%/2 mm criteria and from 32.22 to 89.65% for 1%/1 mm criteria. We also tested our commissioning method on a six-field head-and-neck cancer IMRT plan. The passing rate of the γ-index test within the 10% isodose line of the prescription dose was improved from 92.73 to 99.70% and from 82.16 to 96.73% for 2%/2 mm and 1%/1 mm criteria, respectively. Real clinical data measured from Varian, Siemens, and Elekta linear accelerators were also used to validate our commissioning method and a similar level of accuracy was achieved.
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Affiliation(s)
- Zhen Tian
- Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Abstract
Recent developments in radiotherapy therapy demand high computation powers to solve challenging problems in a timely fashion in a clinical environment. The graphics processing unit (GPU), as an emerging high-performance computing platform, has been introduced to radiotherapy. It is particularly attractive due to its high computational power, small size, and low cost for facility deployment and maintenance. Over the past few years, GPU-based high-performance computing in radiotherapy has experienced rapid developments. A tremendous amount of study has been conducted, in which large acceleration factors compared with the conventional CPU platform have been observed. In this paper, we will first give a brief introduction to the GPU hardware structure and programming model. We will then review the current applications of GPU in major imaging-related and therapy-related problems encountered in radiotherapy. A comparison of GPU with other platforms will also be presented.
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Affiliation(s)
- Xun Jia
- Deparment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Peter Ziegenhein
- German Cancer Research Center (DKFZ), Department of Medical Physics in Radiation Oncology, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Steve B. Jiang
- Deparment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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