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    Deep Learning Based Classification Methods of Compton Camera Based Prompt Gamma Imaging for Proton Radiotherapy

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    BarajasKroiz_HPCF20211.pdf (2.540Mb)
    Links to Files
    http://hpcf-files.umbc.edu/research/papers/BarajasKroiz_HPCF20211.pdf
    Permanent Link
    http://hdl.handle.net/11603/24662
    Collections
    • UMBC Faculty Collection
    • UMBC Mathematics and Statistics Department
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    Author/Creator
    Barajas, Carlos A.
    Kroiz, Gerson C.
    Gobbert, Matthias
    Polf, Jerimy C.
    Author/Creator ORCID
    https://orcid.org/0000-0003-1745-2292
    Date
    2021
    Type of Work
    41 pages
    Text
    reports
    Rights
    This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
    Subjects
    UMBC High Performance Computing Facility (HPCF)
    Abstract
    Proton beam radiotherapy is a method of cancer treatment that uses proton beams to irradi ate cancerous tissue, while simultaneously sparing doses to healthy tissue. In order to optimize radiation doses to the tumor and ensure that healthy tissue is spared, many researchers have suggested verifying the treatment delivery through the use of real-time imaging. One promising method of real-time imaging is the use of a Compton camera, which can image prompt gamma rays that are emitted along the beam’s path through the patient. However, because of limita tions in the Compton camera’s ability to detect prompt gammas, the reconstructed images are often noisy and unusable for verifying proton treatment delivery. Machine learning is able to automatically learn patterns that exist in numerical data, making it a promising method to ana lyze Compton camera data for the purpose of reducing noise in the reconstructed images. First, we provide motivation for training deep neural networks over standard ensemble techniques. We then present the usage of supervised deep neural networks to detect and exploit these patterns so that we can remove and correct the various problems that exist within our data.


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    www.umbc.edu/scholarworks

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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3021


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.