GPU-based ultrafast IMRT plan optimization
GPU-based ultrafast IMRT plan optimization
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DOI：
10.1088/0031-9155/54/21/008
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发表时间：
2009-11-07
影响因子：
3.5
通讯作者：
Jiang, Steve B.
中科院分区：
文献类型：
--
作者：
Men, Chunhua;Gu, Xuejun;Jiang, Steve B.
车载体积成像在癌症放疗中的广泛应用，刺激了开发在线自适应放疗技术的研究工作，以处理患者几何形状的内部变化。这些努力面临着实时执行治疗计划的重大技术挑战。为了克服这一挑战，我们正在加州大学圣地亚哥分校（UCSD）开发一个超级计算机在线重新规划环境（SCORE）。作为SCORE项目的一部分，本文介绍了我们在图形处理单元（gpu）上实现调强放射治疗（IMRT）优化算法的工作。采用基于惩罚的二次优化模型，采用梯度投影法和Armijo线搜索规则求解。我们的优化算法已经在CUDA中用于并行GPU计算，以及在C中用于串行CPU计算进行比较。我们用一个不同光束和体素大小的前列腺IMRT病例来评估我们的实施。在NVIDIA Tesla C1060 GPU卡上，与英特尔至强2.27 GHz CPU的结果相比，我们已经实现了20-40的加速因子，而不会失去精度。对于特定的9场前列腺IMRT病例，具有5 x 5 mm(2)束束尺寸和2.5 x 2.5 x 2.5 mm(3)体素尺寸，我们的GPU实现只需2.8 s即可生成最佳IMRT计划。因此，我们的工作解决了开发适应性放疗在线重新规划技术的一个主要问题。
The widespread adoption of on-board volumetric imaging in cancer radiotherapy has stimulated research efforts to develop online adaptive radiotherapy techniques to handle the inter-fraction variation of the patient's geometry. Such efforts face major technical challenges to perform treatment planning in real time. To overcome this challenge, we are developing a supercomputing online re-planning environment (SCORE) at the University of California, San Diego (UCSD). As part of the SCORE project, this paper presents our work on the implementation of an intensity-modulated radiation therapy (IMRT) optimization algorithm on graphics processing units (GPUs). We adopt a penalty-based quadratic optimization model, which is solved by using a gradient projection method with Armijo's line search rule. Our optimization algorithm has been implemented in CUDA for parallel GPU computing as well as in C for serial CPU computing for comparison purpose. A prostate IMRT case with various beamlet and voxel sizes was used to evaluate our implementation. On an NVIDIA Tesla C1060 GPU card, we have achieved speedup factors of 20-40 without losing accuracy, compared to the results from an Intel Xeon 2.27 GHz CPU. For a specific nine-field prostate IMRT case with 5 x 5 mm(2) beamlet size and 2.5 x 2.5 x 2.5 mm(3) voxel size, our GPU implementation takes only 2.8 s to generate an optimal IMRT plan. Our work has therefore solved a major problem in developing online re-planning technologies for adaptive radiotherapy.