GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy
GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy
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DOI：
10.1088/0031-9155/55/15/008
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发表时间：
2010-08-07
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;Jia, Xun;Jiang, Steve B.
在线自适应放射治疗（ART）具有很大的希望，可以通过基于当前患者解剖结构的实时治疗适应性来显著降低正常组织毒性和/或改善肿瘤控制。然而，在线ART临床实现的主要技术障碍，即无法实现治疗重新规划的实时效率，尚未得到解决。为了克服这一挑战，本文介绍了我们的工作上实现的调强放射治疗（IMRT）直接孔径优化（DAO）算法的图形处理单元（GPU）的基础上，我们以前的工作CPU。我们将DAO问题表述为一个大规模凸规划问题，并使用称为列生成方法的精确方法在GPU上处理其极大的维度。测试了5个9场前列腺和5个5场头颈部IMRT临床病例，其中5 x 5 mm（2）射束尺寸和2.5 x 2.5 x 2.5 mm（3）体素尺寸，以评估我们在GPU上的算法。在NVIDIA Tesla C1060 GPU卡上生成高质量的治疗计划仅需0.7-3.8秒。因此，我们的工作解决了一个主要问题，在开发超快速（重新）规划技术的在线艺术。
Online adaptive radiation therapy (ART) has great promise to significantly reduce normal tissue toxicity and/or improve tumor control through real-time treatment adaptations based on the current patient anatomy. However, the major technical obstacle for clinical realization of online ART, namely the inability to achieve real-time efficiency in treatment re-planning, has yet to be solved. To overcome this challenge, this paper presents our work on the implementation of an intensity-modulated radiation therapy (IMRT) direct aperture optimization (DAO) algorithm on the graphics processing unit (GPU) based on our previous work on the CPU. We formulate the DAO problem as a large-scale convex programming problem, and use an exact method called the column generation approach to deal with its extremely large dimensionality on the GPU. Five 9-field prostate and five 5-field head-and-neck IMRT clinical cases with 5 x 5 mm(2) beamlet size and 2.5 x 2.5 x 2.5 mm(3) voxel size were tested to evaluate our algorithm on the GPU. It takes only 0.7-3.8 s for our implementation to generate high-quality treatment plans on an NVIDIA Tesla C1060 GPU card. Our work has therefore solved a major problem in developing ultra-fast (re-)planning technologies for online ART.