期刊
PHYSICS IN MEDICINE AND BIOLOGY
卷 66, 期 5, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1361-6560/abde9a
关键词
Monte-Carlo simulation; generative adversarial network; SPECT; phase space
资金
- SIRIC LYriCAN Grant [INCa-INSERM-DGOS-12563]
- LABEX PRIMES of Universite de Lyon, within the program Investissements d'Avenir [ANR-11-LABX-0063, ANR- 11-IDEX-0007]
- POPEYE ERA PerMed 2019 project [ANR-19-PERM-0007-04]
- Agence Nationale de la Recherche (ANR) [ANR-19-PERM-0007] Funding Source: Agence Nationale de la Recherche (ANR)
A method using generative adversarial networks to model particle distribution in emission tomography imaging devices during Monte Carlo simulation is proposed, allowing for efficient particle path generation and reduced computation time, beneficial for imaging system design.
A method is proposed to model by a generative adversarial network the distribution of particles exiting a patient during Monte Carlo simulation of emission tomography imaging devices. The resulting compact neural network is then able to generate particles exiting the patient, going towards the detectors, avoiding costly particle tracking within the patient. As a proof of concept, the method is evaluated for single photon emission computed tomography (SPECT) imaging and combined with another neural network modeling the detector response function (ARF-nn). A complete rotating SPECT acquisition can be simulated with reduced computation time compared to conventional Monte Carlo simulation. It also allows the user to perform simulations with several imaging systems or parameters, which is useful for imaging system design.
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