Artificial Intelligence for Monte Carlo Simulation in Medical Physics

Monte Carlo simulation of particle tracking in matter is the reference simulation method in the field of medical physics. It is heavily used in various applications such as 1) patient dose distribution estimation in different therapy modalities (radiotherapy, protontherapy or ion therapy) or for rad...

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Autores principales:	David Sarrut, Ane Etxebeste, Enrique Muñoz, Nils Krah, Jean Michel Létang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	AI Monte Carlo simulation medical physics GAN deep learning Physics QC1-999
Acceso en línea:	https://doaj.org/article/fea60fb5bd4b45908550a6494a4e8767
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spelling	oai:doaj.org-article:fea60fb5bd4b45908550a6494a4e87672021-12-02T15:07:31ZArtificial Intelligence for Monte Carlo Simulation in Medical Physics2296-424X10.3389/fphy.2021.738112https://doaj.org/article/fea60fb5bd4b45908550a6494a4e87672021-10-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphy.2021.738112/fullhttps://doaj.org/toc/2296-424XMonte Carlo simulation of particle tracking in matter is the reference simulation method in the field of medical physics. It is heavily used in various applications such as 1) patient dose distribution estimation in different therapy modalities (radiotherapy, protontherapy or ion therapy) or for radio-protection investigations of ionizing radiation-based imaging systems (CT, nuclear imaging), 2) development of numerous imaging detectors, in X-ray imaging (conventional CT, dual-energy, multi-spectral, phase contrast … ), nuclear imaging (PET, SPECT, Compton Camera) or even advanced specific imaging methods such as proton/ion imaging, or prompt-gamma emission distribution estimation in hadrontherapy monitoring. Monte Carlo simulation is a key tool both in academic research labs as well as industrial research and development services. Because of the very nature of the Monte Carlo method, involving iterative and stochastic estimation of numerous probability density functions, the computation time is high. Despite the continuous and significant progress on computer hardware and the (relative) easiness of using code parallelisms, the computation time is still an issue for highly demanding and complex simulations. Hence, since decades, Variance Reduction Techniques have been proposed to accelerate the processes in a specific configuration. In this article, we review the recent use of Artificial Intelligence methods for Monte Carlo simulation in medical physics and their main associated challenges. In the first section, the main principles of some neural networks architectures such as Convolutional Neural Networks or Generative Adversarial Network are briefly described together with a literature review of their applications in the domain of medical physics Monte Carlo simulations. In particular, we will focus on dose estimation with convolutional neural networks, dose denoising from low statistics Monte Carlo simulations, detector modelling and event selection with neural networks, generative networks for source and phase space modelling. The expected interests of those approaches are discussed. In the second section, we focus on the current challenges that still arise in this promising field.David SarrutAne EtxebesteEnrique MuñozNils KrahNils KrahJean Michel LétangFrontiers Media S.A.articleAIMonte Carlo simulationmedical physicsGANdeep learningPhysicsQC1-999ENFrontiers in Physics, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	AI Monte Carlo simulation medical physics GAN deep learning Physics QC1-999
spellingShingle	AI Monte Carlo simulation medical physics GAN deep learning Physics QC1-999 David Sarrut Ane Etxebeste Enrique Muñoz Nils Krah Nils Krah Jean Michel Létang Artificial Intelligence for Monte Carlo Simulation in Medical Physics
description	Monte Carlo simulation of particle tracking in matter is the reference simulation method in the field of medical physics. It is heavily used in various applications such as 1) patient dose distribution estimation in different therapy modalities (radiotherapy, protontherapy or ion therapy) or for radio-protection investigations of ionizing radiation-based imaging systems (CT, nuclear imaging), 2) development of numerous imaging detectors, in X-ray imaging (conventional CT, dual-energy, multi-spectral, phase contrast … ), nuclear imaging (PET, SPECT, Compton Camera) or even advanced specific imaging methods such as proton/ion imaging, or prompt-gamma emission distribution estimation in hadrontherapy monitoring. Monte Carlo simulation is a key tool both in academic research labs as well as industrial research and development services. Because of the very nature of the Monte Carlo method, involving iterative and stochastic estimation of numerous probability density functions, the computation time is high. Despite the continuous and significant progress on computer hardware and the (relative) easiness of using code parallelisms, the computation time is still an issue for highly demanding and complex simulations. Hence, since decades, Variance Reduction Techniques have been proposed to accelerate the processes in a specific configuration. In this article, we review the recent use of Artificial Intelligence methods for Monte Carlo simulation in medical physics and their main associated challenges. In the first section, the main principles of some neural networks architectures such as Convolutional Neural Networks or Generative Adversarial Network are briefly described together with a literature review of their applications in the domain of medical physics Monte Carlo simulations. In particular, we will focus on dose estimation with convolutional neural networks, dose denoising from low statistics Monte Carlo simulations, detector modelling and event selection with neural networks, generative networks for source and phase space modelling. The expected interests of those approaches are discussed. In the second section, we focus on the current challenges that still arise in this promising field.
format	article
author	David Sarrut Ane Etxebeste Enrique Muñoz Nils Krah Nils Krah Jean Michel Létang
author_facet	David Sarrut Ane Etxebeste Enrique Muñoz Nils Krah Nils Krah Jean Michel Létang
author_sort	David Sarrut
title	Artificial Intelligence for Monte Carlo Simulation in Medical Physics
title_short	Artificial Intelligence for Monte Carlo Simulation in Medical Physics
title_full	Artificial Intelligence for Monte Carlo Simulation in Medical Physics
title_fullStr	Artificial Intelligence for Monte Carlo Simulation in Medical Physics
title_full_unstemmed	Artificial Intelligence for Monte Carlo Simulation in Medical Physics
title_sort	artificial intelligence for monte carlo simulation in medical physics
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/fea60fb5bd4b45908550a6494a4e8767
work_keys_str_mv	AT davidsarrut artificialintelligenceformontecarlosimulationinmedicalphysics AT aneetxebeste artificialintelligenceformontecarlosimulationinmedicalphysics AT enriquemunoz artificialintelligenceformontecarlosimulationinmedicalphysics AT nilskrah artificialintelligenceformontecarlosimulationinmedicalphysics AT nilskrah artificialintelligenceformontecarlosimulationinmedicalphysics AT jeanmichelletang artificialintelligenceformontecarlosimulationinmedicalphysics
_version_	1718388491738415104

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

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