A fast three-dimensional gamma evaluation using a GPU utilizing texture memory for on-the-fly interpolations
A fast three-dimensional gamma evaluation using a GPU utilizing texture memory for on-the-fly interpolations
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DOI：
10.1118/1.3595114
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发表时间：
2011-07-01
期刊：
影响因子：
3.8
通讯作者：
Verhaegen, Frank
中科院分区：
文献类型：
--
作者：
Persoon, Lucas C. G. G.;Podesta, Mark;Verhaegen, Frank
目的：一种广泛接受的量化剂量分布差异的方法是伽马（gamma）评价。目前，几乎所有的伽玛实现都使用中央处理器（CPU）。最近，图形处理单元（GPU）已经成为用于特定计算任务的强大平台。在这项研究中，我们描述了一个3D伽马评估使用GPU，以提高calculationtime.Methods：伽马评估算法的实现在NVIDIA Tesla C2050 GPU上使用计算统一设备架构（CUDA）。首先，模拟了几个立方体虚拟体模。这些幻影进行了测试，不同的剂量立方体的大小和设置，引入人工剂量差异。其次，为了显示在临床实践中的适用性，使用来自治疗计划系统的3D剂量分布作为参考和在治疗期间确定的输送剂量作为比较，对5个患者病例进行了评价。CPU和GPU之间的计算时间进行了比较，不同的线程块大小，包括使用纹理或全局memory.Results的选项：CPU的GPU的速度提高了66 +/- 12的虚拟幻影。对于患者病例，使用GPU获得了57 +/- 15的加速。在所有情况下，16 x 16的螺纹块尺寸表现最佳。纹理存储器的使用改善了总计算时间，特别是在应用插值时。CPU和GPU之间的差异gamma s可以忽略不计。结论：GPU及其功能，如纹理内存，大大减少了伽玛评估的计算时间，而不损失准确性。(C)2011年美国医学物理学家协会。[DOI：10.1118/1.3595114]
Purpose: A widely accepted method to quantify differences in dose distributions is the gamma (gamma) evaluation. Currently, almost all gamma implementations utilize the central processing unit (CPU). Recently, the graphics processing unit (GPU) has become a powerful platform for specific computing tasks. In this study, we describe the implementation of a 3D gamma evaluation using a GPU to improve calculation time.Methods: The gamma evaluation algorithm was implemented on an NVIDIA Tesla C2050 GPU using the compute unified device architecture (CUDA). First, several cubic virtual phantoms were simulated. These phantoms were tested with varying dose cube sizes and set-ups, introducing artificial dose differences. Second, to show applicability in clinical practice, five patient cases have been evaluated using the 3D dose distribution from a treatment planning system as the reference and the delivered dose determined during treatment as the comparison. A calculation time comparison between the CPU and GPU was made with varying thread-block sizes including the option of using texture or global memory.Results: A GPU over CPU speed-up of 66 +/- 12 was achieved for the virtual phantoms. For the patient cases, a speed-up of 57 +/- 15 using the GPU was obtained. A thread-block size of 16 x 16 performed best in all cases. The use of texture memory improved the total calculation time, especially when interpolation was applied. Differences between the CPU and GPU gamma s were negligible.Conclusions: The GPU and its features, such as texture memory, decreased the calculation time for gamma evaluations considerably without loss of accuracy. (C) 2011 American Association of Physicists in Medicine. [DOI:10.1118/1.3595114]