GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy.
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.
Men
Chunhua
C
Department of Radiation Oncology, University of California San Diego, La Jolla, CA 92037, USA.
Jia
Xun
X
Jiang
Steve B
SB
D000465:Algorithms; D003196:Computer Graphics; D006258:Head and Neck Neoplasms / Q000532:radiotherapy; D006801:Humans; D008297:Male; D009862:Online Systems; D011471:Prostatic Neoplasms / Q000532:radiotherapy; D011786:Quality Control; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D050397:Radiotherapy, Intensity-Modulated; D013997:Time Factors