Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA.
Med Phys. 2010 Nov;37(11):5593-603. doi: 10.1118/1.3490083.
Dose calculation is a key component in radiation treatment planning systems. Its performance and accuracy are crucial to the quality of treatment plans as emerging advanced radiation therapy technologies are exerting ever tighter constraints on dose calculation. A common practice is to choose either a deterministic method such as the convolution/superposition (CS) method for speed or a Monte Carlo (MC) method for accuracy. The goal of this work is to boost the performance of a hybrid Monte Carlo convolution/superposition (MCCS) method by devising a graphics processing unit (GPU) implementation so as to make the method practical for day-to-day usage.
Although the MCCS algorithm combines the merits of MC fluence generation and CS fluence transport, it is still not fast enough to be used as a day-to-day planning tool. To alleviate the speed issue of MC algorithms, the authors adopted MCCS as their target method and implemented a GPU-based version. In order to fully utilize the GPU computing power, the MCCS algorithm is modified to match the GPU hardware architecture. The performance of the authors' GPU-based implementation on an Nvidia GTX260 card is compared to a multithreaded software implementation on a quad-core system.
A speedup in the range of 6.7-11.4x is observed for the clinical cases used. The less than 2% statistical fluctuation also indicates that the accuracy of the authors' GPU-based implementation is in good agreement with the results from the quad-core CPU implementation.
This work shows that GPU is a feasible and cost-efficient solution compared to other alternatives such as using cluster machines or field-programmable gate arrays for satisfying the increasing demands on computation speed and accuracy of dose calculation. But there are also inherent limitations of using GPU for accelerating MC-type applications, which are also analyzed in detail in this article.
剂量计算是放射治疗计划系统的关键组成部分。随着新兴的先进放射治疗技术对剂量计算施加越来越严格的限制，其性能和准确性对于治疗计划的质量至关重要。一种常见的做法是选择卷积/叠加（CS）方法来提高速度，或者选择蒙特卡罗（MC）方法来提高准确性。本工作的目的是通过设计图形处理单元（GPU）实现来提高混合蒙特卡罗卷积/叠加（MCCS）方法的性能，以便使其在日常使用中更加实用。
尽管 MCCS 算法结合了 MC 通量生成和 CS 通量传输的优点，但它的速度仍然不够快，无法作为日常计划工具使用。为了缓解 MC 算法的速度问题，作者采用 MCCS 作为目标方法，并实现了基于 GPU 的版本。为了充分利用 GPU 计算能力，作者对 MCCS 算法进行了修改，以匹配 GPU 硬件架构。作者在 Nvidia GTX260 卡上的 GPU 实现的性能与四核系统上的多线程软件实现进行了比较。
观察到临床病例的速度提高了 6.7-11.4 倍。小于 2%的统计波动也表明，作者的 GPU 实现的准确性与四核 CPU 实现的结果非常吻合。
与使用集群机器或现场可编程门阵列等其他替代方案相比，GPU 是一种可行且具有成本效益的解决方案，可以满足对剂量计算的计算速度和准确性的日益增长的需求。但是，使用 GPU 加速 MC 类型的应用程序也存在固有的限制，本文也对此进行了详细分析。
