使用按需云基础设施进行光子-组织相互作用的蒙特卡罗建模
Monte Carlo modeling photon-tissue interaction using on-demand cloud infrastructure
摘要：
目的：这项工作提出了一种蒙特卡罗（MC）方法，结合电离辐射物理与光学物理，在某种程度上，这是隐含的设计部署与最广泛的并行化和可移植性可能。研究方法：目前的工作更新了以前开发的GEANT 4架构的面向医学的模拟（GAMOS）的光传播插件。虚拟机（VM）和基于容器的实例验证使用以前发布的脚本，并使用并行模拟的执行时间的改进证明。提出了一种以编程方式部署多个容器以使用按需的基于云的基础设施来实现并行执行的方法。结果：使用多层组织模型以及光学和X射线源输入演示了基于容器的GAMOS部署。例如，该模型被分成154个模拟，这些模拟在4台服务器上的64个单独的容器上同时运行。结论：基于容器的模型提供了执行应用程序的并行模拟的能力，这些应用程序本身不是线程安全的或GPU优化的。在当前的演示中，与顺序执行相比，这最多减少了97%的时间。代码和示例可通过交互式在线界面获得，链接为：https://sites.dartmouth.edu/optmed/research-projects/monte-carlo-software/
英文摘要：
Purpose: This work advances a Monte Carlo (MC) method to combine ionizing radiation physics with optical physics, in a manner which was implicitly designed for deployment with the most widely accessible parallelization and portability possible. Methods: The current work updates a previously developed optical propagation plugin for GEANT4 architecture for medically oriented simulations (GAMOS). Both virtual-machine (VM) and container based instances were validated using previously published scripts, and improvements in execution time using parallel simulations are demonstrated. A method to programmatically deploy multiple containers to achieve parallel execution using an on-demand cloud-based infrastructure is presented. Results: A container-based GAMOS deployment is demonstrated using a multi-layer tissue model and both optical and X-ray source inputs. As an example, the model was split into 154 simulations which were run simultaneously on 64 separate containers across 4 servers. Conclusions: The container-based model provides the ability to execute parallel simulations of applications which are not inherently thread-safe or GPU-optimized. In the current demonstration, this reduced the time by at most 97% compared to sequential execution. The code and examples are available through an interactive online interface through links at: https://sites.dartmouth.edu/optmed/research-projects/monte-carlo-software/
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