Deep Learning for Classification of Compton Camera Data in the Reconstruction of Proton Beams in Cancer Treatment
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Basalyga, Jonathan N., Carlos A. Barajas, Matthias K. Gobbert, Paul Maggi, and Jerimy Polf. “Deep Learning for Classification of Compton Camera Data in the Reconstruction of Proton Beams in Cancer Treatment.” PAMM 20, no. 1 (2021): e202000070. https://doi.org/10.1002/pamm.202000070.
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This is the pre-peer reviewed version of the following article: Basalyga, Jonathan N., Carlos A. Barajas, Matthias K. Gobbert, Paul Maggi, and Jerimy Polf. “Deep Learning for Classification of Compton Camera Data in the Reconstruction of Proton Beams in Cancer Treatment.” PAMM 20, no. 1 (2021): e202000070. https://doi.org/10.1002/pamm.202000070., which has been published in final form at https://doi.org/10.1002/pamm.202000070. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
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.