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 graphics processing unit (GPU) based on our previous work on CPU. We formulate the DAO problem as a large-scale convex programming problem, and use an exact method called column generation approach to deal with its extremely large dimensionality on GPU. Five 9-field prostate and five 5-field head-and-neck IMRT clinical cases with 5\times5 mm2 beamlet size and 2.5\times2.5\times2.5 mm3 voxel size were used to evaluate our algorithm on GPU. It takes only 0.7~2.5 seconds for our implementation to generate optimal treatment plans using 50 MLC apertures 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

GPU-based fast gamma index calcuation

The gamma-index dose comparison tool has been widely used to compare dose distributions in cancer radiotherapy. The accurate calculation of gamma-index requires an exhaustive search of the closest Euclidean distance in the high-resolution dose-distance space. This is a computational intensive task when dealing with 3D dose distributions. In this work, we combine a geometric method with a radial pre-sorting technique , and implement them on computer graphics processing units (GPUs). The developed GPU-based gamma-index computational tool is evaluated on eight pairs of IMRT dose distributions. The GPU implementation achieved 20x~30x speedup factor compared to CPU implementation and gamma-index calculations can be finished within a few seconds for all 3D testing cases. We further investigated the effect of various factors on both CPU and GPU computation time. The strategy of pre-sorting voxels based on their dose difference values speed up the GPU calculation by about 2-4 times. For n-dimensional dose distributions, gamma-index calculation time on CPU is proportional to the summation of gamma^n over all voxels, while that on GPU is effected by gamma^n distributions and is approximately proportional to the gamma^n summation over all voxels. We found increasing dose distributions resolution leads to quadratic increase of computation time on CPU, while less-than-quadratic increase on GPU. The values of dose difference (DD) and distance-to-agreement (DTA) criteria also have their impact on gamma-index calculation time.Comment: 13 pages, 2 figures, and 3 table