Monte Carlo (MC) simulation is a valuable tool for radiation therapy. Particularly for particle beam radiation therapy (PBRT), its remarkable value has been well recognized. Examples include, but not limited to, accurately calculating dose distributions that are highly sensitive to treatment geometry and anatomy, reducing range uncertainty, developing novel treatment verification techniques, capturing radiobiological effects from the microscopic level, and designing treatment facility. Hence, researchers are eager to have a fast, robust, and easy-to-use MC system in their studies. Yet, there are two main difficulties to use current available MC packages for PBRT, namely low computational efficiency and highly required user expertise. The conflicts between the great desire of using MC and the difficulties of using it have impeded research and clinical activities in PBRT to significantly. As part of the planning process for National Particle Therapy Research Center (NPTRC), we propose in this pilot project a highly accurate, efficient, yet user-friendly centralized MC simulation system using novel graphics-processing unit (GPU) and cloud-computing technologies. Different from conventional MC packages running on the user's end, our system remotely resides in a cloud inside NPTRC and provides MC simulation services to PBRT researchers though standard web browsers. While our long-term goal is to deliver novel MC simulations to facilitate the establishments of NPTRC and its future research activities, as well as to service the entire PBRT community, the goal of this pilot project is to initiate efforts toward the long-term goal by developing and validating a prototype system focusing on particle beam dose calculations to demonstrate feasibility and impacts. The deliverability of this project has been clearly demonstrated by mature technologies and our extensive preliminary studies. The strong research team, particularly the integration of Dr. Parodi for particle physics modeling, also ensures success. Our goal will be accomplished by pursuing two specific aims (SAs): (1) System developments: develop web interface, physics database, and core GPU-based MC simulation codes. (2) System validations: Comprehensively validate the computational accuracy of our system and test its efficiency. Perform end-to-end functionality test in a representative research scenario. This pilot project fits into the overall plan for the proposed NPTRC facility. (1) Being an integral component of NPTRC, it will play a critical role for the planning stage by offering virtual yet realistic simulations of different clinical, physical, and technical scenarios. In the long run, our system will greatly expand NPTRC's research capacity and hence significantly contribute to the establishments of its leading role in PBRT field. (2) Our system service PBRT field with high quality MC simulations. Continuous developments will add much more features to address needs from different research aspects. This is aligned with the NPTRC's mission of providing resources for researchers to investigate important problems in PBRT.
This pilot project will develop a highly accurate, efficient, yet user-friendly centralized MC simulation system using novel graphics-processing unit (GPU) and cloud-computing technologies. This system will play a critical role for the planning stage of National Particle Therapy Research Center (NPTRC) by offering virtual yet realistic simulations of different clinical, physical, and technical scenarios, and contribute to the establishments of its leading role in particle beam radiation therapy (PBRT) field. It will also provide a platform for researchers in the entire PBRT community to investigate important problems.
|Qin, Nan; Shen, Chenyang; Tsai, Min-Yu et al. (2018) Full Monte Carlo-Based Biologic Treatment Plan Optimization System for Intensity Modulated Carbon Ion Therapy on Graphics Processing Unit. Int J Radiat Oncol Biol Phys 100:235-243|
|Shen, Chenyang; Li, Bin; Chen, Liyuan et al. (2018) Material elemental decomposition in dual and multi-energy CT via a sparsity-dictionary approach for proton stopping power ratio calculation. Med Phys 45:1491-1503|
|Story, Michael D; Wang, Jing (2018) Developing Predictive or Prognostic Biomarkers for Charged Particle Radiotherapy. Int J Part Ther 5:94-102|
|Shusharina, Nadya; Liao, Zhongxing; Mohan, Radhe et al. (2018) Differences in lung injury after IMRT or proton therapy assessed by 18FDG PET imaging. Radiother Oncol 128:147-153|
|Li, B; Lee, H C; Duan, X et al. (2017) Comprehensive analysis of proton range uncertainties related to stopping-power-ratio estimation using dual-energy CT imaging. Phys Med Biol 62:7056-7074|
|Mohamad, Osama; Sishc, Brock J; Saha, Janapriya et al. (2017) Carbon Ion Radiotherapy: A Review of Clinical Experiences and Preclinical Research, with an Emphasis on DNA Damage/Repair. Cancers (Basel) 9:|
|Qin, Nan; Pinto, Marco; Tian, Zhen et al. (2017) Initial development of goCMC: a GPU-oriented fast cross-platform Monte Carlo engine for carbon ion therapy. Phys Med Biol 62:3682-3699|
|Li, Yongbao; Tian, Zhen; Song, Ting et al. (2017) A new approach to integrate GPU-based Monte Carlo simulation into inverse treatment plan optimization for proton therapy. Phys Med Biol 62:289-305|
|Tian, Zhen; Jiang, Steve B; Jia, Xun (2017) Accelerated Monte Carlo simulation on the chemical stage in water radiolysis using GPU. Phys Med Biol 62:3081-3096|
|Qin, Nan; Botas, Pablo; Giantsoudi, Drosoula et al. (2016) Recent developments and comprehensive evaluations of a GPU-based Monte Carlo package for proton therapy. Phys Med Biol 61:7347-7362|
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