Deep Learning for Classification of Compton Camera Data in the Reconstruction of Proton Beams in Cancer Treatment
Loading...
Permanent Link
Author/Creator ORCID
Date
2020-06-12
Type of Work
Department
Program
Citation of Original Publication
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
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.
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