The Power of GPUs in Machine Learning to Improve Proton Beam Therapy for Cancer Treatment
| dc.contributor.author | Navarathna, Nithya | |
| dc.contributor.author | Gobbert, Matthias | |
| dc.date.accessioned | 2022-11-28T16:25:43Z | |
| dc.date.available | 2022-11-28T16:25:43Z | |
| dc.description.abstract | Proton beam therapy utilizes proton beams to treat cancerous tumors while avoiding unnecessary radiation exposure to surrounding healthy tissues. Real-time imaging of the proton beams while they travel through a patient’s body can make this form of radiotherapy more precise and safer for the patient. The use of a Compton camera is one proposed method for real-time imaging of the prompt gamma rays that are emitted by the proton beams. Unfortunately, some of the Compton camera data is flawed, and the image reconstruction algorithm yields noisy and insufficiently detailed images to evaluate the proton delivery for the patient. Machine learning can be a powerful tool to clean up the Compton camera images. Previous work used a deep residual fully connected neural network, but the use of recurrent neural networks (RNNs) has been proposed, since they use recurrence relationships to make potentially better predictions. In this work, RNN architectures using two different recurrent layers are tested, the LSTM and the GRU. Although the deep residual fully connected neural network achieves over 75% testing accuracy and our models achieve only over 73% testing accuracy, the simplicity of our RNN models containing only 6 hidden layers as opposed to 512 is a significant advantage.This will also cause the time to load the model from the disk to be significantly faster, potentially enabling the use of Compton camera image reconstruction in real-time during patient treatment. A graphics processing unit (GPU), known to perform complex math calculations to display high-quality graphics, could enable the use of this approach in a clinical setting since they are small in size and affordable. | en_US |
| dc.description.sponsorship | This work would not have been possible without my teammates (Joseph Clark, Anaise Gaillard, and Justin Koe) in the Big Data REU Site 2022 at UMBC (bigdatareu.umbc.edu). Additionally, I would like to thank our team’s research assistant Daniel J. Kelly and collaborators Dr. Carlos A. Barajas (Department of Mathematics and Statistics, UMBC) and Dr. Jerimy C. Polf (Department of Radiation Oncology, University of Maryland School of Medicine). This work is supported by the grant “REU Site: Online Interdisciplinary Big Data Analytics in Science and Engineering” from the National Science Foundation (grant no. OAC–2050943). 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, OAC–1726023, and CNS—1920079) 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. | en_US |
| dc.description.uri | http://hpcf-files.umbc.edu/research/papers/BigDataREU2022Team2_UMBCReview.pdf | en_US |
| dc.format.extent | 10 pages | en_US |
| dc.genre | journal articles | |
| dc.genre | preprints | |
| dc.identifier | doi:10.13016/m2arbi-ab3v | |
| dc.identifier.uri | http://hdl.handle.net/11603/26363 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Biological Sciences Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Mathematics and Statistics Department | |
| 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.subject | UMBC High Performance Computing Facility (HPCF) | |
| dc.title | The Power of GPUs in Machine Learning to Improve Proton Beam Therapy for Cancer Treatment | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0003-1745-2292 | en_US |