Using Deep Learning to Enhance Compton Camera Based Prompt Gamma Image Reconstruction Data for Proton Radiotherapy
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2021-12-14
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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
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Access to this item will begin on 12/14/2022
Access to this item will begin on 12/14/2022
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.