A GPU- BASED SIMULATED ANNEALING ALGORITHM FOR INTENSITY-MODULATED RADIATION THERAPY

Simulating Annealing Algorithm (SAA) has been proposed for optimization of the Intensity-Modulated Radiation Therapy (IMRT). Despite the advantage of the SAA to be a global optimizer, the SAA optimization of IMRT is an extensive computational task due to the large scale of the optimization variables, and therefore it requires significant computational resources. In this research we introduce a parallel graphics processing unit (GPU)-based SAA developed in MATLAB platform and compliant with the computational environment for radiotherapy research (CERR) for IMRT treatment planning in order elucidate the performance improvement of the SAA in IMRT optimization. First, we identify the “bottlenecks” of our code, and then we parallelize those on the GPU accordingly. Performance tests were conducted on four different GPU cards in comparison to a serial version of the algorithm executed on a CPU. A gradual increase of the speedup factor as a function of the number of beamlets was found for all four GPUs. A maximum speedup factor of 33.48 was achieved for a prostate case, and 30.51 for a lung cancer case when the K40m card and the maximum number of beams was utilized for each case. At the same time, the two optimized IMRT plans that were created (prostate and lung cancer plans) were met the IMRT optimization goals.