GPU-accelerated Monte Carlo simulation of MV-CBCT
| Autor: | Daniel Morf, M. Jacobson, Mengying Shi, Thomas C. Harris, Ingrid Valencia Lozano, Christopher S. Williams, Marios Myronakis, Ross Berbeco, Paul Baturin, Pascal Huber, Mathias Lehmann, Dianne Ferguson, Rony Fueglistaller |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Image formation
Cone beam computed tomography Computer science medicine.medical_treatment Computation Monte Carlo method Computed tomography Imaging phantom 030218 nuclear medicine & medical imaging Computational science Computer graphics 03 medical and health sciences 0302 clinical medicine medicine Computer Graphics Radiology Nuclear Medicine and imaging Computer Simulation Photons Radiological and Ultrasound Technology medicine.diagnostic_test Phantoms Imaging Volume (computing) Cone-Beam Computed Tomography Radiation therapy 030220 oncology & carcinogenesis Monte Carlo Method |
| Zdroj: | Physics in medicine and biology. 65(23) |
| ISSN: | 1361-6560 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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