Acceleration of BNCT dose map calculations via convolutional neural networks

IF 1.6 3区工程技术 Q3 CHEMISTRY, INORGANIC & NUCLEAR

Applied Radiation and Isotopes Pub Date : 2025-02-20 DOI:10.1016/j.apradiso.2025.111718

G. Marzik , M.E. Capoulat , A.J. Kreiner , D.M. Minsky

{"title":"Acceleration of BNCT dose map calculations via convolutional neural networks","authors":"G. Marzik , M.E. Capoulat , A.J. Kreiner , D.M. Minsky","doi":"10.1016/j.apradiso.2025.111718","DOIUrl":null,"url":null,"abstract":"<div><div>A carefully made treatment plan is of paramount importance in order to achieve satisfactory results in treatments based on Boron Neutron Capture Therapy. Different source configurations and positions have to be analyzed, and based on the different dose maps that can be computed, an optimal treatment should be chosen. Nowadays the dose maps are computed using slow and computationally intensive Monte Carlo simulations, which hinder the formulation of an optimized treatment plan. This work proposes a machine learning algorithm based on a convolutional neural network that accelerates the convergence of Monte Carlo neutron transport simulations, drastically reducing computation time without loss of accuracy. A dataset of Monte Carlo simulation was made and used for the training of the proposed model. 97% of the voxels of the set of testing simulations had errors lower than 5% when processed by the neural network, and inference times were reduced by three orders of magnitude. In the future, this tool could allow a real optimization of treatment plans.</div></div>","PeriodicalId":8096,"journal":{"name":"Applied Radiation and Isotopes","volume":"220 ","pages":"Article 111718"},"PeriodicalIF":1.6000,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Radiation and Isotopes","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0969804325000636","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, INORGANIC & NUCLEAR","Score":null,"Total":0}

引用次数: 0

Abstract

A carefully made treatment plan is of paramount importance in order to achieve satisfactory results in treatments based on Boron Neutron Capture Therapy. Different source configurations and positions have to be analyzed, and based on the different dose maps that can be computed, an optimal treatment should be chosen. Nowadays the dose maps are computed using slow and computationally intensive Monte Carlo simulations, which hinder the formulation of an optimized treatment plan. This work proposes a machine learning algorithm based on a convolutional neural network that accelerates the convergence of Monte Carlo neutron transport simulations, drastically reducing computation time without loss of accuracy. A dataset of Monte Carlo simulation was made and used for the training of the proposed model. 97% of the voxels of the set of testing simulations had errors lower than 5% when processed by the neural network, and inference times were reduced by three orders of magnitude. In the future, this tool could allow a real optimization of treatment plans.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

Applied Radiation and Isotopes 工程技术-核科学技术

CiteScore

3.00

自引率

12.50%

发文量

406

审稿时长

13.5 months

期刊介绍： Applied Radiation and Isotopes provides a high quality medium for the publication of substantial, original and scientific and technological papers on the development and peaceful application of nuclear, radiation and radionuclide techniques in chemistry, physics, biochemistry, biology, medicine, security, engineering and in the earth, planetary and environmental sciences, all including dosimetry. Nuclear techniques are defined in the broadest sense and both experimental and theoretical papers are welcome. They include the development and use of α- and β-particles, X-rays and γ-rays, neutrons and other nuclear particles and radiations from all sources, including radionuclides, synchrotron sources, cyclotrons and reactors and from the natural environment. The journal aims to publish papers with significance to an international audience, containing substantial novelty and scientific impact. The Editors reserve the rights to reject, with or without external review, papers that do not meet these criteria. Papers dealing with radiation processing, i.e., where radiation is used to bring about a biological, chemical or physical change in a material, should be directed to our sister journal Radiation Physics and Chemistry.