A time- and space-saving Monte Carlo simulation method using post-collimation generative adversarial network for dose calculation of an O-ring gantry Linac.

Autor: Shi M; Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA; Department of Radiation Oncology, University of California, Irvine, Orange, CA, USA. Electronic address: mengyis5@hs.uci.edu., Cui S; Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA; Department of Radiation Oncology, University of Washington, Seattle, WA, USA., Chuang C; Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA., Oderinde O; RefleXion Medical, Hayward, CA, USA., Kovalchuk N; Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA., Surucu M; Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA., Xing L; Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA., Han B; Department of Radiation Oncology, Stanford University, Palo Alto, CA, USA.
Jazyk: angličtina
Zdroj: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) [Phys Med] 2024 Mar; Vol. 119, pp. 103318. Date of Electronic Publication: 2024 Feb 21.
DOI: 10.1016/j.ejmp.2024.103318
Abstrakt: Purpose: This study explores the feasibility of employing Generative Adversarial Networks (GANs) to model the RefleXion X1 Linac. The aim is to investigate the accuracy of dose simulation and assess the potential computational benefits.
Methods: The X1 Linac is a new radiotherapy machine with a binary multi-leaf collimation (MLC) system, facilitating innovative biology-guided radiotherapy. A total of 34 GAN generators, each representing a desired MLC aperture, were developed. Each generator was trained using a phase space file generated underneath the corresponding aperture, enabling the generation of particles and serving as a beam source for Monte Carlo simulation. Dose distributions in water were simulated for each aperture using both the GAN and phase space sources. The agreement between dose distributions was evaluated. The computational time reduction from bypassing the collimation simulation and storage space savings were estimated.
Results: The percentage depth dose at 10 cm, penumbra, and full-width half maximum of the GAN simulation agree with the phase space simulation, with differences of 0.4 % ± 0.2 %, 0.32 ± 0.66 mm, and 0.26 ± 0.44 mm, respectively. The gamma passing rate (1 %/1mm) for the planar dose exceeded 90 % for all apertures. The estimated time-saving for simulating an plan using 5766 beamlets was 530 CPU hours. The storage usage was reduced by a factor of 102.
Conclusion: The utilization of the GAN in simulating the X1 Linac demonstrated remarkable accuracy and efficiency. The reductions in both computational time and storage requirements make this approach highly valuable for future dosimetry studies and beam modeling.
Competing Interests: Declaration of competing interest This study was partically supported by a grant from RefleXion Medical Inc.
(Copyright © 2024 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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