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

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    Author
    Polf, Jerimy C.
    Barajas, Carlos A.
    Peterson, Stephen W.
    Mackin, Dennis S.
    Beddar, Sam
    Ren, Lei
    Gobbert, Matthias K.
    Date
    2022-04-11
    Journal
    Frontiers in Physics
    Publisher
    Frontiers Media S.A.
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.3389/fphy.2022.838273
    Abstract
    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 measurement, and 2) the correct ordering of PG interactions in the CC to help improve the fidelity of “good” data used for image reconstruction. PG emission from a tissue-equivalent target during irradiation with a 150 MeV proton beam delivered at clinical dose rates was measured with a prototype CC. Images were reconstructed from both the raw measured data and the measured data that was further processed with a neural network (NN) trained to identify “good” and “bad” PG events and predict the ordering of individual interactions within the good PG events. We determine if NN processing of the CC data could improve the reconstructed PG images to a level in which they could provide clinically useful information about the in vivo range and range shifts of the proton beams delivered at full clinical dose rates. Results showed that a deep, fully connected NN improved the achievable contrast to noise ratio (CNR) in our images by more than a factor of 8x. This allowed the path, range, and lateral width of the clinical proton beam within a tissue equivalent target to easily be identified from the PG images, even at the highest dose rates of a 150 MeV proton beam used for clinical treatments. On average, shifts in the beam range as small as 3 mm could be identified. However, when limited by the amount of PG data measured with our prototype CC during the delivery of a single proton pencil beam (∼1 × 109 protons), the uncertainty in the reconstructed PG images limited the identification of range shift to ∼5 mm. Substantial improvements in CC images were obtained during clinical beam delivery through NN pre-processing of the measured PG data. We believe this shows the potential of NNs to help improve and push CC-based PG imaging toward eventual clinical application for proton RT treatment delivery verification. Copyright © 2022 Polf, Barajas, Peterson, Mackin, Beddar, Ren and Gobbert.
    Sponsors
    National Science Foundation
    Keyword
    compton camera
    in vivo imaging
    prompt gamma
    proton pencil beam
    proton radiotherapy
    range verification
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/18742
    ae974a485f413a2113503eed53cd6c53
    10.3389/fphy.2022.838273
    Scopus Count
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