Streaming architectures for computation in medical radiation physics

放射治疗在历史上与更快、更强大的计算机的发展密切共生;如果没有今天的微处理器，先进的输送技术，如调强放射治疗（IMRT）将是不可能的。 对更好的辐射输送技术的追求一直持续到今天，并要求越来越强大的机器来处理复杂的治疗计划算法所需的计算。为了跟上现代算法的处理要求，治疗计划系统（TPS）有时必须求助于计算机集群，以在合理的时间内提供临床输出。 这种架构虽然高效，但需要大量的时间和专业知识投资，并可能因经济和后勤原因而阻碍部署更好的TPS。 因此，在放射治疗和一般的医学物理学中，似乎非常需要一种经济且功能强大但独立的计算平台。 我们在这里建议开发这样一个平台，使用专用于大规模并行计算的创新硬件材料，如图形处理单元（GPU）。GPU和类似的流处理器，如细胞宽带引擎（CBE）已经成功地用于许多科学领域的通用计算，实现了高达两个数量级的速度提高因素，超过传统的中央处理器（CPU）的实现。我们建议在GPU等流处理器上实现医学物理算法，以显着加速图像处理，优化和剂量计算等任务。反过来，这将允许在治疗计划系统中集成更复杂、更准确和更个性化的算法。这种算法在流处理器上的发展不仅有利于医学辐射物理领域，而且有利于任何需要高性能计算的学科。

Radiation therapy historically has evolved in close symbiosis with the development of faster and more powerful computers; advanced delivery techniques such as intensity-modulated radiation therapy (IMRT) would not be possible without today's microprocessors. The quest for better radiation delivery techniques continues to this day, and commands increasingly more powerful machines to handle the calculations required by complex treatment planning algorithms. In order to keep up with the processing requirements of modern algorithms, treatment planning systems (TPS) must sometimes resort to clusters of computers to provide clinical output in reasonable time. This architecture, although efficient, requires substantial investments in time and expertise, and can hinder the deployment of better TPS for economical and logistical reasons. It therefore appears that an economical and powerful, yet standalone calculation platform is highly desirable in radiation therapy, and in medical physics in general. We propose here to develop such a platform, using innovative hardware material dedicated to massively parallel calculations such as Graphics Processing Units (GPUs). GPUs and similar stream processors such as the Cell Broadband Engine (CBE) have already been used successfully for general-purpose calculations in many scientific fields, achieving speed improvement factors of up to two orders of magnitude over traditional implementations on Central Processing Units (CPUs). We propose to implement medical physics algorithms on stream processors such as GPUs in order to significantly accelerate tasks such as image processing, optimization and dose calculations. This, in turn, will allow the integration of more complex, more accurate and more personalized algorithms in treatment planning systems. The development of such algorithms on stream processors will benefit not only the field of medical radiation physics, but any discipline where high-performance computing is desirable.