GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy

被引:71
作者
Men, Chunhua [1 ]
Jia, Xun [1 ]
Jiang, Steve B. [1 ]
机构
[1] Univ Calif San Diego, Dept Radiat Oncol, La Jolla, CA 92037 USA
关键词
INTENSITY-MODULATED RADIOTHERAPY; DEFORMABLE IMAGE REGISTRATION; BEAM COMPUTED-TOMOGRAPHY; FIDUCIAL MARKERS; DOSE CALCULATION; PROSTATE-CANCER; RE-OPTIMIZATION; CERVICAL-CANCER; IMRT; TUMOR;
D O I
10.1088/0031-9155/55/15/008
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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.
引用
收藏
页码:4309 / 4319
页数:11
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