{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["65(24)"],"submitter":["Lalonde A"],"funding":["Natural Sciences and Engineering Research Council of Canada","National Cancer Institute","Fonds de Recherche du Québec - Nature et Technologies"],"pubmed_abstract":["Adaptive proton therapy (APT) is a promising approach for the treatment of head and neck cancers. One crucial element of APT is daily volumetric imaging of the patient in the treatment position. Such data can be acquired with cone-beam computed tomography (CBCT), although scatter artifacts make uncorrected CBCT images unsuitable for proton therapy dose calculation. The purpose of this work is to evaluate the performance of a U-shape deep convolutive neural network (U-Net) to perform projection-based scatter correction and enable fast and accurate dose calculation on CBCT images in the context of head and neck APT. CBCT projections are simulated for a cohort of 48 head and neck patients using a GPU accelerated Monte Carlo (MC) code . A U-Net is trained to reproduce MC projection-based scatt"],"journal":["Physics in medicine and biology"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8920050"],"repository":["biostudies-literature"],"pubmed_title":["Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy."],"pmcid":["PMC8920050"],"funding_grant_id":["267388","PDF-532784 - 2019","R01CA229178"],"pubmed_authors":["Lalonde A","Paganetti H","Winey B","Sharp GC","Verburg J"],"additional_accession":[]},"is_claimable":false,"name":"Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy.","description":"Adaptive proton therapy (APT) is a promising approach for the treatment of head and neck cancers. One crucial element of APT is daily volumetric imaging of the patient in the treatment position. Such data can be acquired with cone-beam computed tomography (CBCT), although scatter artifacts make uncorrected CBCT images unsuitable for proton therapy dose calculation. The purpose of this work is to evaluate the performance of a U-shape deep convolutive neural network (U-Net) to perform projection-based scatter correction and enable fast and accurate dose calculation on CBCT images in the context of head and neck APT. CBCT projections are simulated for a cohort of 48 head and neck patients using a GPU accelerated Monte Carlo (MC) code . A U-Net is trained to reproduce MC projection-based scatt","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Dec","modification":"2025-04-05T11:07:29.862Z","creation":"2025-02-18T23:37:05.832Z"},"accession":"S-EPMC8920050","cross_references":{"pubmed":["32580174"],"doi":["10.1088/1361-6560/ab9fcb"]}}