GRID-ENABLED TREATMENT PLANNING FOR PROTON THERAPY USING MONTE CARLO SIMULATIONS.

Grid computing is an emerging technology that enables computational tasks to be accomplished in a collaborative approach by using a distributed network of computers. The grid approach is especially important for computationally intensive problems that are not tractable with a single computer or even with a small cluster of computers, e.g., radiation transport calculations for cancer therapy. The objective of this work was to extend a Monte Carlo (MC) transport code used for proton radiotherapy to utilize grid computing techniques and demonstrate its promise in reducing runtime from days to minutes. As proof of concept we created the Medical Grid between Texas Tech University and Rice University. Preliminary computational experiments were carried out in the GEANT4 simulation environment for transport of 25 ×10 200 MeV protons in a prostate cancer treatment plan. The simulation speedup was approximately linear; deviations were attributed to the spectrum of parallel runtimes and communication overhead due to Medical Grid computing. The results indicate that ~3 × 10 to 5 × 10 proton events with processor core would result in 65 to 83% efficiency. Extrapolation of our results indicates that about 10 processor cores of the class used here would reduce the MC simulation runtime from 18.3 days to ~1 h.