Deep Residual Fully Connected Neural Network Classification of Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy

dc.contributor.authorBarajas, Carlos A.
dc.contributor.authorGobbert, Matthias
dc.contributor.authorPolf, Jerimy C.
dc.date.accessioned2022-01-26T16:00:56Z
dc.date.available2022-01-26T16:00:56Z
dc.date.issued2023-02-16
dc.description.abstractProton beam radiotherapy is a method of cancer treatment that uses proton beams to irradiate cancerous tissue, while minimizing doses to healthy tissue. In order to guarantee that the prescribed radiation dose is delivered to the tumor and ensure that healthy tissue is spared, many researchers have suggested verifying the treatment delivery through the use of real-time imaging using methods which can image prompt gamma rays that are emitted along the beam’s path through the patient such as Compton cameras (CC). However, because of limitations of the CC, their images are noisy and unusable for verifying proton treatment delivery. We provide a detailed description of a deep residual fully connected neural network that is capable of classifying and improving measured CC data with an increase in the fraction of usable data by up to 72% and allows for improved image reconstruction across the full range of clinical treatment delivery conditions.en_US
dc.description.sponsorshipThis work is supported by the grant “CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Resources” from the National Science Foundation (grant no. OAC–1730250). The research reported in this publication was also sup ported by the National Institutes of Health National Cancer Institute under award number R01CA187416. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). The facility is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS–1228778, and OAC–1726023) and the SCREMS program (grant no. DMS–0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See http://hpcf.umbc.edu for more information on HPCF and the projects using its resources. Co-author Carlos Barajas additionally acknowledges support as HPCF RAen_US
dc.description.urihttps://www.frontiersin.org/articles/10.3389/fphy.2023.903929/fullen_US
dc.format.extent11 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2jlah-v0fa
dc.identifier.citationBarajas CA, Polf JC and Gobbert MK (2023) Deep residual fully connected neural network classification of Compton camera based prompt gamma imaging for proton radiotherapy. Front. Phys. 11:903929. doi: 10.3389/fphy.2023.903929
dc.identifier.urihttp://hdl.handle.net/11603/24088
dc.identifier.urihttps://doi.org/10.3389/fphy.2023.903929
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.titleDeep Residual Fully Connected Neural Network Classification of Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapyen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0003-1745-2292en_US

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