Deep Residual Fully Connected Neural Network Classification of Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy
dc.contributor.author | Barajas, Carlos A. | |
dc.contributor.author | Gobbert, Matthias | |
dc.contributor.author | Polf, Jerimy C. | |
dc.date.accessioned | 2022-01-26T16:00:56Z | |
dc.date.available | 2022-01-26T16:00:56Z | |
dc.date.issued | 2023-02-16 | |
dc.description.abstract | Proton 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.sponsorship | This 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 RA | en_US |
dc.description.uri | https://www.frontiersin.org/articles/10.3389/fphy.2023.903929/full | en_US |
dc.format.extent | 11 pages | en_US |
dc.genre | journal articles | en_US |
dc.identifier | doi:10.13016/m2jlah-v0fa | |
dc.identifier.citation | Barajas 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.uri | http://hdl.handle.net/11603/24088 | |
dc.identifier.uri | https://doi.org/10.3389/fphy.2023.903929 | |
dc.language.iso | en_US | en_US |
dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
dc.relation.ispartof | UMBC Mathematics Department Collection | |
dc.relation.ispartof | UMBC Faculty Collection | |
dc.relation.ispartof | UMBC Student Collection | |
dc.rights | This 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.rights | Attribution 4.0 International (CC BY 4.0) | |
dc.rights | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | UMBC High Performance Computing Facility (HPCF) | en_US |
dc.title | Deep Residual Fully Connected Neural Network Classification of Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy | en_US |
dc.type | Text | en_US |
dcterms.creator | https://orcid.org/0000-0003-1745-2292 | en_US |
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