This article is a correction to:
Artificial Intelligence for Monte Carlo Simulation in Medical Physics
David Sarrut
Ane Etxebeste
Enrique Muñoz2
Nils Krah
Jean Michel Létang- 1University of Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France
- 2University of Lyon, Université Claude Bernard Lyon 1, CNRS/IN2P3, IP2I Lyon, Villeurbanne, France
A Corrigendum on
Artificial Intelligence for Monte Carlo Simulation in Medical Physics
by Sarrut, D., Etxebeste, A., Muñoz, E., Krah, N., and Létang, J. M. (2021). Front. Phys. 9:738112. doi: 10.3389/fphy.2021.738112
In the original article, there was an error in Literature Review, AI-Based Dose Computation, paragraph three. The sentence “For example, Roser et al. [117] use CNN, in particular a U-Net, to compute first-order dose exposure of patients (i.e., without considering scattered radiation) due to image-guided x-ray procedures.” should have read “For example, Roser et al. [117] use a U-Net fed with first order fluence maps computed by fast ray-casting in order to estimate the total dose exposure including scattered radiation during image-guided x-ray procedures.”
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Keywords: AI, Monte Carlo simulation, medical physics, GaN, deep learning
Citation: Sarrut D, Etxebeste A, Muñoz E, Krah N and Létang JM (2021) Corrigendum: Artificial Intelligence for Monte Carlo Simulation in Medical Physics. Front. Phys. 9:808444. doi: 10.3389/fphy.2021.808444
Received: 03 November 2021; Accepted: 15 November 2021;
Published: 02 December 2021.
Edited and reviewed by:
Susanna Guatelli, University of Wollongong, AustraliaCopyright © 2021 Sarrut, Etxebeste, Muñoz, Krah and Létang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: David Sarrut, david.sarrut@creatis.insa-lyon.fr