Exploring Deep Learning to Improve Compton Camera Based Prompt Gamma Image Reconstruction for Proton Radiotherapy
| dc.contributor.author | Kroiz, Gerson C. | |
| dc.contributor.author | Barajas, Carlos A. | |
| dc.contributor.author | Gobbert, Matthias K. | |
| dc.contributor.author | Polf, Jerimy C. | |
| dc.date.accessioned | 2021-05-21T16:52:08Z | |
| dc.date.available | 2021-05-21T16:52:08Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Proton beam radiotherapy is a cancer treatment method that uses proton beams to irradiate cancerous tissue while simultaneously sparing doses to healthy tissue. In order to optimize radiational doses to the tumor and ensure that healthy tissue is spared, many researchers have suggested verifying the treatment delivery through real-time imaging. One promising method of real-time imaging is through a Compton camera, which can image prompt gamma rays emitted along the beam’s path through the patient. However, the reconstructed images are often noisy and unusable for verifying proton treatment delivery due to limitations with the camera. We present the usage of deep learning to remove and correct the various problems that exist within our data. | en_US |
| dc.description.sponsorship | This work is 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 https://hpcf.umbc.edu for more information on HPCF and the projects using its resources. | en_US |
| dc.description.uri | http://hpcf-files.umbc.edu/research/papers/Kroiz_ICDATA2021.pdf | en_US |
| dc.format.extent | 4 pages | en_US |
| dc.genre | conference papers and proceedings | en_US |
| dc.genre | postprints | |
| dc.identifier | doi:10.13016/m2qgs1-itid | |
| dc.identifier.citation | Gerson C. Kroiz, Carlos A. Barajas, Matthias K. Gobbert, and Jerimy C. Polf. Exploring Deep Learning to Improve Compton Camera Based Prompt Gamma Image Reconstruction for Proton Radiotherapy. In: The 17th International Conference on Data Science (ICDATA’21), accepted (2021). (HPCF machines used: taki.). | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/21594 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Springer | |
| 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) | en_US |
| dc.title | Exploring Deep Learning to Improve Compton Camera Based Prompt Gamma Image Reconstruction for Proton Radiotherapy | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0003-1745-2292 |