Deep Learning for Classification of Compton Camera Data in the Reconstruction of Proton Beams in Cancer Treatment
| dc.contributor.author | Basalyga, Jonathan N. | |
| 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 | 2020-07-28T18:36:44Z | |
| dc.date.available | 2020-07-28T18:36:44Z | |
| dc.date.issued | 2020-06-12 | |
| dc.description.abstract | Real-time imaging has potential to greatly increase the effectiveness of proton beam therapy in cancer treatment. One promising method of real-time imaging is the use of a Compton camera to detect prompt gamma rays, which are emitted by 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 in part by the U.S. National Science Foundation under the CyberTraining (OAC–1730250) and MRI (OAC–1726023) programs. The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). Co-author Carlos A. Barajas was supported as HPCF RA as well as as CyberTraining RA. | en_US |
| dc.description.uri | http://hpcf-files.umbc.edu/research/papers/S21_Basalyga_v1.pdf | en_US |
| dc.format.extent | 2 pages | en_US |
| dc.genre | conference papers and proceedings preprints | en_US |
| dc.identifier | doi:10.13016/m2xyaw-kkim | |
| dc.identifier.citation | Jonathan N. Basalyga et al., Deep Learning for Classification of Compton Camera Data in the Reconstruction of Proton Beams in Cancer Treatment, Proceedings in Applied Mathematics and Mechanics (2020), http://hpcf-files.umbc.edu/research/papers/S21_Basalyga_v1.pdf | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/19260 | |
| 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.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 | Deep Learning for Classification of Compton Camera Data in the Reconstruction of Proton Beams in Cancer Treatment | en_US |
| dc.type | Text | en_US |