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dc.contributor.authorBarajas, Carlos A.
dc.contributor.authorKroiz, Gerson C.
dc.contributor.authorGobbert, Matthias
dc.contributor.authorPolf, Jerimy C.
dcterms.creatorhttps://orcid.org/0000-0003-1745-2292en_US
dc.date.accessioned2022-05-02T14:13:39Z
dc.date.available2022-05-02T14:13:39Z
dc.date.issued2021
dc.description.abstractProton beam radiotherapy is a method of cancer treatment that uses proton beams to irradi ate cancerous tissue, while simultaneously sparing doses to healthy tissue. In order to optimize radiation doses 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. One promising method of real-time imaging is the use of a Compton camera, which can image prompt gamma rays that are emitted along the beam’s path through the patient. However, because of limita tions in the Compton camera’s ability to detect prompt gammas, the reconstructed images are often noisy and unusable for verifying proton treatment delivery. Machine learning is able to automatically learn patterns that exist in numerical data, making it a promising method to ana lyze Compton camera data for the purpose of reducing noise in the reconstructed images. First, we provide motivation for training deep neural networks over standard ensemble techniques. We then present the usage of supervised deep neural networks to detect and exploit these patterns so that we can remove and correct the various problems that exist within our data.en_US
dc.description.sponsorshipThis work is supported by the grant “CyberTraining: DSE: Cross-Training of Researchers in Com puting, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Re sources” from the National Science Foundation (grant no. OAC–1730250). The research reported in this publication was also supported 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 pro gram (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 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.urihttp://hpcf-files.umbc.edu/research/papers/BarajasKroiz_HPCF20211.pdfen_US
dc.format.extent41 pagesen_US
dc.genrereportsen_US
dc.identifierdoi:10.13016/m2cnsa-wysb
dc.identifier.urihttp://hdl.handle.net/11603/24662
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Mathematics Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofseriesHPCF Technical Report;HPCF–2021–1
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.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.titleDeep Learning Based Classification Methods of Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapyen_US
dc.typeTexten_US


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