Using Deep Learning to Enhance Compton Camera Based Prompt Gamma Image Reconstruction Data for Proton Radiotherapy

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http://hpcf-files.umbc.edu/research/papers/Barajas_PAMM2021.pdf

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 https://doi.org/10.1002/pamm.202100236
 http://hdl.handle.net/11603/24663

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UMBC Mathematics and Statistics Department
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https://orcid.org/0000-0003-1745-2294

Date

2021-12-14

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journal articles
postprints
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Citation of Original Publication

Barajas, C.A., Kroiz, G.C., Gobbert, M.K. and Polf, J.C. (2021), Using Deep Learning to Enhance Compton Camera Based Prompt Gamma Image Reconstruction Data for Proton Radiotherapy. Proc. Appl. Math. Mech., 21: e202100236. https://doi.org/10.1002/pamm.202100236

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This is the peer reviewed version of the following article: Barajas, C.A., Kroiz, G.C., Gobbert, M.K. and Polf, J.C. (2021), Using Deep Learning to Enhance Compton Camera Based Prompt Gamma Image Reconstruction Data for Proton Radiotherapy. Proc. Appl. Math. Mech., 21: e202100236. https://doi.org/10.1002/pamm.202100236, which has been published in final form at https://doi.org/10.1002/pamm.202100236. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Access to this item will begin on 12/14/2022

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UMBC High Performance Computing Facility (HPCF)

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 images reconstructed with modern reconstruction algorithms are often noisy and unusable for verifying proton treatment delivery due to limitations with the camera. This paper demonstrates the ability of deep learning for removing false prompt gamma couplings and correcting the improperly ordered gamma interactions within the data for the case of Triples and Doubles-to-Triple events.