GPU-accelerated Monte Carlo simulation of MV-CBCT. (30th November 2020)

• Record Type: Journal Article
• Authors: Shi, Mengying; Myronakis, Marios; Jacobson, Matthew; Ferguson, Dianne; Williams, Christopher; Lehmann, Mathias; Baturin, Paul; Huber, Pascal; Fueglistaller, Rony; Lozano, Ingrid Valencia; Harris, Thomas; Morf, Daniel; Berbeco, Ross I
• Is Part Of: Physics in medicine & biology. Volume 65:Number 23(2020:Dec.)
• Publication Date: 2020-11-30
• Subjects: GPU -- Monte Carlo simulation -- MV-CBCT -- fast simulation; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/abaeba
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Monte Carlo simulation (MCS) is one of the most accurate computation methods for dose calculation and image formation in radiation therapy. However, the high computational complexity and long execution time of MCS limits its broad use. In this paper, we present a novel strategy to accelerate MCS using a graphic processing unit (GPU), and we demonstrate the application in mega-voltage (MV) cone-beam computed tomography (CBCT) simulation. A new framework that generates a series of MV projections from a single simulation run is designed specifically for MV-CBCT acquisition. A Geant4-based GPU code for photon simulation is incorporated into the framework for the simulation of photon transport through a phantom volume. The FastEPID method, which accelerates the simulation of MV images, is modified and integrated into the framework. The proposed GPU-based simulation strategy was tested for its accuracy and efficiency in a Catphan 604 phantom and an anthropomorphic pelvis phantom with beam energies at 2.5 MV, 6 MV, and 6 MV FFF. In all cases, the proposed GPU-based simulation demonstrated great simulation accuracy and excellent agreement with measurement and CPU-based simulation in terms of reconstructed image qualities. The MV-CBCT simulation was accelerated by factors of roughly 900–2300 using an NVIDIA Tesla V100 GPU card against a 2.5 GHz AMD Opteron™ Processor 6380.