加速辐射剂量计算:一个多fpga解决方案

Bo Zhou, X. Hu, D. Chen, Cedric X. Yu
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引用次数: 2

摘要

在过去的几十年里,放射治疗在各个方面都取得了显著的进展。然而,其中一些有前途的技术,如图像引导的在线重新规划和弧线治疗,严重依赖于快速剂量计算的可用性。本文基于一种流行的剂量计算算法——坍缩锥卷积/叠加(CCCS)算法,设计了一种多fpga加速器来加速辐射剂量的计算。我们的性能驱动设计策略产生了一个完全流水线的架构,其中包括资源经济的光线追踪引擎和高性能的能量沉积管道。基于一组临床治疗计划案例的评估证实,我们的FPGA设计几乎充分利用了可用的外部存储带宽,并且在使用较少资源的情况下实现了接近CCCS算法的最佳性能。与现有旨在加速相同算法的FPGA设计相比,该设计通过提供更好的内存带宽利用率(81.7% vs . 43%的可用外部内存带宽)、更高的工作频率(90MHz vs . 70MHz)和更少的逻辑资源使用(25K vs . 55K逻辑单元),实现了1.9倍的加速。此外,它比四核系统上的商业多线程软件的速度提高了20倍,比密切相关的结果提高了15倍的性能。在准确性方面,测量到的小于1%的统计波动表明我们的解决方案在实际医疗场景中是实用的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accelerating radiation dose calculation: A multi-FPGA solution
Remarkable progress has been made in the past few decades in various aspects of radiation therapy (RT). However, some of these promising technologies, such as image-guided online replanning and arc therapy, rely heavily on the availability of fast dose calculation. In this article, based on a popular dose calculation algorithm, the Collapsed-Cone Convolution/Superposition (CCCS) algorithm, we present a multi-FPGA accelerator to speed up radiation dose calculation. Our performance-driven design strategy yields a fully pipelined architecture, which includes a resource-economic raytracing engine and high-performance energy deposition pipeline. An evaluation based on a set of clinical treatment planning cases confirms that our FPGA design almost fully utilizes the available external memory bandwidth and achieves close to the best possible performance for the CCCS algorithm while using less resource. Compared with an existing FPGA design which aimed to accelerate the identical algorithm, the proposed design achieved 1.9X speedup by providing better memory bandwidth utilization (81.7% v.s. 43% of the available external memory bandwidth), higher working frequency (90MHz v.s. 70MHz) and less logic resource usage (25K v.s. 55K logic cells). Furthermore, it obtains a speedup of 20X over a commercial multithreaded software on a quad-core system and 15X performance improvement over closely related results. In terms of accuracy, the measured less than 1% statistical fluctuation indicates that our solution is practical in real medical scenarios.
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