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    Modeling Stochastic Processes in Gamma-Ray Imaging Detectors and Evaluation of a Multi-Anode PMT Scintillation Camera for Use with Maximum-Likelihood Estimation Methods

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    Author
    Hunter, William Coulis Jason
    Issue Date
    2007
    Keywords
    Maximum-Likelihood
    Gamma-Ray Detector
    Stochastic
    Modeling
    Imaging
    Advisor
    Barrett, Harrison H.
    Committee Chair
    Barrett, Harrison H.
    Barrett, Bruce R.
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Maximum-likelihood estimation or other probabilistic estimation methods are underused in many areas of applied gamma-ray imaging, particularly in biomedicine. In this work, we show how to use our understanding of stochastic processes in a scintillation camera and their effect on signal formation to better estimate gamma-ray interaction parameters such as interaction position or energy.To apply statistical estimation methods, we need an accurate description of the signal statistics as a function of the parameters to be estimated. First, we develop a probability model of the signals conditioned on the parameters to be estimated by carefully examining the signal generation process. Subsequently, the likelihood model is calibrated by measuring signal statistics for an ensemble of events as a function of the estimate parameters.In this work, we investigate the application of ML-estimation methods for three topics. First, we design, build, and evaluate a scintillation camera based on a multi-anode PMT readout for use with ML-estimation techniques. Next, we develop methods for calibrating the response statistics of a thick-detector gamma camera as a function of interaction depth. Finally, we demonstrate the use of ML estimation with a modified clinical Anger camera.
    Type
    text
    Electronic Dissertation
    Degree Name
    PhD
    Degree Level
    doctoral
    Degree Program
    Physics
    Graduate College
    Degree Grantor
    University of Arizona
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