Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy

Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy

We studied the application of a deep, fully connected Neural Network (NN) to process prompt gamma (PG) data measured by a Compton camera (CC) during the delivery of clinical proton radiotherapy beams. The network identifies 1) recorded “bad” PG events arising from background noise during the measure...

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Bibliographic Details
Main Authors: Jerimy C. Polf, Carlos A. Barajas, Stephen W. Peterson, Dennis S. Mackin, Sam Beddar, Lei Ren, Matthias K. Gobbert
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-04-01
Series:Frontiers in Physics
Subjects:
prompt gamma
compton camera
proton radiotherapy
range verification
in vivo imaging
proton pencil beam
Online Access:https://www.frontiersin.org/articles/10.3389/fphy.2022.838273/full

Internet

https://www.frontiersin.org/articles/10.3389/fphy.2022.838273/full

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