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

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 detec...

Full description

Saved in:
Bibliographic Details
Published in:Physics in medicine & biology 2021-03, Vol.66 (5), p.055014-055014
Main Authors: Sarrut, D, Etxebeste, A, Krah, N, Létang, JM
Format: Article
Language:English
Subjects:
Computer Simulation
generative adversarial network
Humans
Monte Carlo Method
Monte-Carlo simulation
Neoplasms - diagnostic imaging
Neural Networks, Computer
Phantoms, Imaging
phase space
Physics
SPECT
Tomography, Emission-Computed, Single-Photon - methods
Tomography, X-Ray Computed - methods
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0031-9155
1361-6560
DOI:10.1088/1361-6560/abde9a