Galanakou, Panagiota

Intensity modulated proton beam scanning therapy allows for highly conformal dose distribution and better sparing of organ-at-risk compared to conventional photon radiotherapy, thanks to the characteristic dose deposition at depth, the Bragg Peak (BP), of protons as a function of depth and energy. However, proton range uncertainties lead to extended clinical margins, at the expense of treatment quality. Prompt Gamma (PG) rays emitted during non- elastic interactions of proton with the matter have been proposed for in-vivo proton range tracking. Nevertheless, poor PG statistics downgrade the potential of the clinical implementation of the proposed techniques. We study the insertion of the nonradioactive elements 19F, 17O, 127I in a tumor area to enhance the PG production of 4.44 MeV (P1) and 6.15 MeV (P2) PG rays emitted during proton irradiation, both correlated with the distal fall-off of the BP. We developed a novel Monte Carlo (MC) model using the TOPAS MC package. With this model, we simulated incident proton beams with energies of 75 MeV, 100 MeV and 200 MeV in co-centric cylindrical phantoms. The outer cylinder (scorer) was filled with water and the inner cylinder (simulating a tumor region inside water-equivalent body) was filled with water containing 0.1%–20% weight fractions of each of the tested elements.

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.