Use of Deep Learning to Classify Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy
| dc.contributor.author | Basalyga, Jonathan N. | |
| dc.contributor.author | Kroiz, Gerson C. | |
| dc.contributor.author | Barajas, Carlos A. | |
| dc.contributor.author | Gobbert, Matthias K. | |
| dc.contributor.author | Maggi, Paul | |
| dc.contributor.author | Polf, Jerimy | |
| dc.date.accessioned | 2020-07-28T17:41:39Z | |
| dc.date.available | 2020-07-28T17:41:39Z | |
| dc.description | UMBC High Performance Computing Facility | en_US |
| dc.description.abstract | Real-time imaging has potential to greatly increase the effectiveness of proton beam therapy for cancer treatment. One promising method of real-time imaging is the use of a Compton camera to detect prompt gamma rays, which are emitted along the path of the beam, in order to reconstruct their origin. However, because of limitations in the Compton camera’s ability to detect prompt gammas, the data are often ambiguous, making reconstructions based on them unusable for practical purposes. Deep learning’s ability to detect subtleties in data that traditional models do not use make it one possible candidate for the improvement of classification of Compton camera data. We show that a suitably designed neural network can reduce false detections and misorderings of interactions, thereby improving reconstruction quality. | 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 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 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 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 | http://hpcf-files.umbc.edu/research/papers/CT2020Team4.pdf | en_US |
| dc.format.extent | 14 pages | en_US |
| dc.genre | technical reports | en_US |
| dc.identifier | doi:10.13016/m2lond-ascp | |
| dc.identifier.citation | Jonathan N. Basalyga et al., Use of Deep Learning to Classify Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy, http://hpcf-files.umbc.edu/research/papers/CT2020Team4.pdf | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/19255 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | UMBC | 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.relation.ispartofseries | HPCF–2020–14; | |
| 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. | |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | |
| dc.title | Use of Deep Learning to Classify Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy | en_US |
| dc.type | Text | en_US |