Program Information
Implementation of a Parallel Simulating Annealing Algorithm for Intensity Modulated Radiation Therapy Optimization
P Galanakou*, T Leventouri , G Kalantzis , Florida Alantic University, Boca Rotan, Florida
Presentations
SU-I-GPD-J-99 (Sunday, July 30, 2017) 3:00 PM - 6:00 PM Room: Exhibit Hall
Purpose: To elucidate the performance improvement of the simulating annealing algorithm (SAA) by parallelizing it on graphics processing unit (GPU) in highly dimensional optimization tasks, such as the Intensity Modulated Radiation Therapy (IMRT). To investigate the potential of its applicability for IMRT optimization in prostate and lung cancer cases.
Methods: A MATLAB® based implementation of treatment planning for radiation therapy was accomplished by using the computational environment for radiotherapy research (CERR. The planning target volume (PTV) was defined as a quadratic error function, while dose-volume constraints (DVCs) were applied for the dose that the Organs at risk (OARs) would receive separately. The optimization algorithm is implemented to determine the optimal intensities that deliver the prescribed dose in the PTV, while satisfying the dose-volume constraints for the OARs. For the parallelization of SAA on the GPU, the Parallel Computing Toolbox in MATLAB® version 2016a was employed and the code was launched on four different GPUs: namely K40m, GTX750, GTX770, and C2050, which are developed by NVIDIA. The performance comparison between the different GPUs was established on the speedup factors between the serial and parallelized SAA for different beamlet sizes.
Results: In prostate and lung cancer cases, a gradual increase of the speedup factor as a function of the number of beamlets was found for all four GPUs. Particularly, a maximum speedup factor of ~33 for 0.2x0.2 cm² beamlet size was achieved when the K40m card was utilized.
Conclusion: This work denotes that the parallel code on GPU outperforms the serial code in CPU, and has been efficiently applied for IMRT treatment planning for prostate and lung cancer cases.
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