High-performance computing with graphics hardware for medical physics applications

用于医学物理应用的具有图形硬件的高性能计算

• 批准号: 355493-2013
• 负责人: Després, Philippe
• 金额: $ 1.97万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=633293
• 关键词: performance; computing; graphics; hardware; medical

A vast array of numerical methods are used to solve problems in Medical Physics, ranging from purely analytical approaches to Monte Carlo techniques. These numerical methods are essential for instance in dose calculation in radiation oncology and in all image processing tasks, including tomographic image reconstruction in radiology and nuclear medicine. The accuracy of numerical solutions typically grows with the complexity of the model used to describe the physics of the problem. Monte Carlo techniques for example provide the most accurate solutions to dose calculation problems because they are based on fundamental physics processes. However, the computational burden and long execution times associated with physics-rich models are typically not compatible with clinical applications, which often require near real-time execution. In order to deploy physics-rich, more accurate algorithms in clinical environments, my research group has developed high-performance computing approaches based on Graphics Processing Units (GPUs). We have developed a recognized expertise in adapting Medical Physics problems to these massively parallel devices, using innovative strategies to significantly accelerate advanced dose calculation and tomographic reconstruction algorithms. Building upon these previous achievements made mostly in Radiation Oncology, we plan to continue this research program to further develop innovative applications in Radiology and Nuclear Medicine. The overall objective is to integrate advanced physics models into Medical Physics applications in order to reach better, more accurate solutions in acceptable timeframes.This research program will ultimately benefit patients and stakeholders in the healthcare sector, as well as industrial partners who already are collaborating with us. The GPU-based high-performance strategies we will develop, and which are essential to all aspects of this research program, are very likely to be useful in a large array of natural sciences and engineering disciplines where massive calculations are required.

大量的数值方法被用来解决医学物理学中的问题，从纯粹的分析方法到蒙特卡罗技术。这些数值方法是必不可少的，例如在放射肿瘤学中的剂量计算和所有图像处理任务中，包括放射学和核医学中的断层图像重建。数值解的准确性通常随着用于描述问题物理特性的模型的复杂性而增长。例如，蒙特卡罗技术为剂量计算问题提供了最准确的解决方案，因为它们基于基本的物理过程。然而，与物理丰富的模型相关联的计算负担和长执行时间通常与临床应用不兼容，临床应用通常需要近实时执行。为了在临床环境中部署物理丰富，更准确的算法，我的研究小组开发了基于图形处理单元（GPU）的高性能计算方法。我们在将医学物理学问题应用于这些大规模并行设备方面拥有公认的专业知识，使用创新策略显著加速高级剂量计算和断层重建算法。基于这些以前的成就，主要是在放射肿瘤学，我们计划继续这项研究计划，以进一步开发放射学和核医学的创新应用。总体目标是将先进的物理模型集成到医学物理应用中，以便在可接受的时间范围内获得更好，更准确的解决方案。该研究计划最终将使医疗保健领域的患者和利益相关者以及已经与我们合作的行业合作伙伴受益。我们将开发的基于GPU的高性能策略对本研究计划的各个方面都至关重要，很可能在需要大量计算的大量自然科学和工程学科中有用。