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Article Dans Une Revue Phys.Med.Biol. Année : 2021

Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept

Résumé

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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Dates et versions

hal-03150535 , version 1 (23-08-2021)

Identifiants

Citer

David Sarrut, A. Etxebeste, Nils Krah, Jean Michel Létang. Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept. Phys.Med.Biol., 2021, 66 (5), pp.055014. ⟨10.1088/1361-6560/abde9a⟩. ⟨hal-03150535⟩
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