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Didi S, Bahhous K, Zerfaoui M, Aboulbanine Z, Ouhadda H, Halimi A. Experimental validation of a linac head Geant4 model under a grid computing environment. Biomed Phys Eng Express 2022; 8. [DOI: 10.1088/2057-1976/ac4dd2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/21/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Background and purpose: This work aims to present the strategy to simulate a clinical linear accelerator based on the geometry provided by the manufacturer and summarize the corresponding experimental validation. Simulations were performed with the Geant4 Monte Carlo code under a grid computing environment. The objective of this contribution is reproducing therapeutic dose distributions in a water phantom with an accuracy less than 2%. Materials and methods: A Geant4 Monte Carlo model of an Elekta Synergy linear accelerator has been established, the simulations were launched in a large grid computing platform. Dose distributions are calculated for a 6 MV photon beam with treatment fields ranging from 5 × 5 cm2 to 20 × 20 cm 2 at a source - surface distance of 100 cm. Results: A high degree of agreement is achieved between the simulation results and the measured data, with dose differences of about 1.03% and 1.96% for the percentage depth dose curves and lateral dose profiles, respectively. This agreement is evaluated by the gamma index comparisons. Over 98% of the points for all simulations meet the restrictive acceptability criteria of 2%/2 mm. Conclusion: We have demonstrated the possibility to establish an accurate linac head Monte Carlo model for dose distribution simulations and quality assurance. Percentage depth dose curves and beam quality indices are in perfect agreement with the measured data with an accuracy of better than 2%.
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Donahue WP, Newhauser WD, Li X, Chen F, Dey J. Computational feasibility of simulating changes in blood flow through whole-organ vascular networks from radiation injury. Biomed Phys Eng Express 2020; 6:055027. [PMID: 33444258 DOI: 10.1088/2057-1976/abaf5c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Vasculature is necessary to the healthy function of most tissues. In radiation therapy, injury of the vasculature can have both beneficial and detrimental effects, such as tumor starvation, cardiac fibrosis, and white-matter necrosis. These effects are caused by changes in blood flow due to the vascular injury. Previously, research has focused on simulating the radiation injury of vasculature in small volumes of tissue, ignoring the systemic effects of local damage on blood flow. Little is known about the computational feasibility of simulating the radiation injury to whole-organ vascular networks. The goal of this study was to test the computational feasibility of simulating the dose deposition to a whole-organ vascular network and the resulting change in blood flow. To do this, we developed an amorphous track-structure model to transport radiation and combined this with existing methods to model the vasculature and blood flow rates. We assessed the algorithm's computational scalability, execution time, and memory usage. The data demonstrated it is computationally feasible to calculate the radiation dose and resulting changes in blood flow from 2 million protons to a network comprising 8.5 billion blood vessels (approximately the number in the human brain) in 87 hours using a 128-node cluster. Furthermore, the algorithm demonstrated both strong and weak scalability, meaning that additional computational resources can reduce the execution time further. These results demonstrate, for the first time, that it is computationally feasible to calculate radiation dose deposition in whole-organ vascular networks. These findings provide key insights into the computational aspects of modeling whole-organ radiation damage. Modeling the effects radiation has on vasculature could prove useful in the study of radiation effects on tissues, organs, and organisms.
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Affiliation(s)
- William P Donahue
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, United States of America
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Chow JCL. Internet-based computer technology on radiotherapy. Rep Pract Oncol Radiother 2017; 22:455-462. [PMID: 28932174 DOI: 10.1016/j.rpor.2017.08.005] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Revised: 02/07/2017] [Accepted: 08/21/2017] [Indexed: 12/11/2022] Open
Abstract
Recent rapid development of Internet-based computer technologies has made possible many novel applications in radiation dose delivery. However, translational speed of applying these new technologies in radiotherapy could hardly catch up due to the complex commissioning process and quality assurance protocol. Implementing novel Internet-based technology in radiotherapy requires corresponding design of algorithm and infrastructure of the application, set up of related clinical policies, purchase and development of software and hardware, computer programming and debugging, and national to international collaboration. Although such implementation processes are time consuming, some recent computer advancements in the radiation dose delivery are still noticeable. In this review, we will present the background and concept of some recent Internet-based computer technologies such as cloud computing, big data processing and machine learning, followed by their potential applications in radiotherapy, such as treatment planning and dose delivery. We will also discuss the current progress of these applications and their impacts on radiotherapy. We will explore and evaluate the expected benefits and challenges in implementation as well.
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Affiliation(s)
- James C L Chow
- Department of Radiation Oncology, University of Toronto and Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2M9, Canada
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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: 14] [Impact Index Per Article: 2.0] [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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Newhauser WD, de Gonzalez AB, Schulte R, Lee C. A Review of Radiotherapy-Induced Late Effects Research after Advanced Technology Treatments. Front Oncol 2016; 6:13. [PMID: 26904500 PMCID: PMC4748041 DOI: 10.3389/fonc.2016.00013] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2015] [Accepted: 01/12/2016] [Indexed: 01/01/2023] Open
Abstract
The number of incident cancers and long-term cancer survivors is expected to increase substantially for at least a decade. Advanced technology radiotherapies, e.g., using beams of protons and photons, offer dosimetric advantages that theoretically yield better outcomes. In general, evidence from controlled clinical trials and epidemiology studies are lacking. To conduct these studies, new research methods and infrastructure will be needed. In the paper, we review several key research methods of relevance to late effects after advanced technology proton-beam and photon-beam radiotherapies. In particular, we focus on the determination of exposures to therapeutic and stray radiation and related uncertainties, with discussion of recent advances in exposure calculation methods, uncertainties, in silico studies, computing infrastructure, electronic medical records, and risk visualization. We identify six key areas of methodology and infrastructure that will be needed to conduct future outcome studies of radiation late effects.
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Affiliation(s)
- Wayne D. Newhauser
- Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA, USA
- Department of Physics, Mary Bird Perkins Cancer Center, Baton Rouge, LA, USA
| | | | - Reinhard Schulte
- Department of Basic Sciences, Loma Linda University Medical Center, Loma Linda, CA, USA
| | - Choonsik Lee
- Radiation Epidemiology Branch, National Institutes of Health, Rockville, MD, USA
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Abstract
The physics of proton therapy has advanced considerably since it was proposed in 1946. Today analytical equations and numerical simulation methods are available to predict and characterize many aspects of proton therapy. This article reviews the basic aspects of the physics of proton therapy, including proton interaction mechanisms, proton transport calculations, the determination of dose from therapeutic and stray radiations, and shielding design. The article discusses underlying processes as well as selected practical experimental and theoretical methods. We conclude by briefly speculating on possible future areas of research of relevance to the physics of proton therapy.
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Affiliation(s)
- Wayne D Newhauser
- Medical Physics Program, Department of Physics and Astronomy, Louisiana State University, 202 Nicholson Hall, Baton Rouge, LA, 70803, USA
- Mary Bird Perkins Cancer Center, 4950 Essen Lane, Baton Rouge, LA, 70809, USA
| | - Rui Zhang
- Medical Physics Program, Department of Physics and Astronomy, Louisiana State University, 202 Nicholson Hall, Baton Rouge, LA, 70803, USA
- Mary Bird Perkins Cancer Center, 4950 Essen Lane, Baton Rouge, LA, 70809, USA
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Yepes PP, Mirkovic D, Taddei PJ. A GPU implementation of a track-repeating algorithm for proton radiotherapy dose calculations. Phys Med Biol 2010; 55:7107-20. [PMID: 21076192 DOI: 10.1088/0031-9155/55/23/s11] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
An essential component in proton radiotherapy is the algorithm to calculate the radiation dose to be delivered to the patient. The most common dose algorithms are fast but they are approximate analytical approaches. However their level of accuracy is not always satisfactory, especially for heterogeneous anatomical areas, like the thorax. Monte Carlo techniques provide superior accuracy; however, they often require large computation resources, which render them impractical for routine clinical use. Track-repeating algorithms, for example the fast dose calculator, have shown promise for achieving the accuracy of Monte Carlo simulations for proton radiotherapy dose calculations in a fraction of the computation time. We report on the implementation of the fast dose calculator for proton radiotherapy on a card equipped with graphics processor units (GPUs) rather than on a central processing unit architecture. This implementation reproduces the full Monte Carlo and CPU-based track-repeating dose calculations within 2%, while achieving a statistical uncertainty of 2% in less than 1 min utilizing one single GPU card, which should allow real-time accurate dose calculations.
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Affiliation(s)
- Pablo P Yepes
- Department of Physics and Astronomy, Rice University, Houston, TX 77005, USA.
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