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

Persistent Link:
http://hdl.handle.net/10150/196121
Title:
Modeling Stochastic Processes in Gamma-Ray Imaging Detectors and Evaluation of a Multi-Anode PMT Scintillation Camera for Use with Maximum-Likelihood Estimation Methods
Author:
Hunter, William Coulis Jason
Issue Date:
2007
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
Keywords:
Maximum-Likelihood; Gamma-Ray Detector; Stochastic; Modeling; Imaging
Degree Name:
PhD
Degree Level:
doctoral
Degree Program:
Physics; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Barrett, Harrison H.
Committee Chair:
Barrett, Harrison H.; Barrett, Bruce R.

Full metadata record

DC FieldValue Language
dc.language.isoENen_US
dc.titleModeling Stochastic Processes in Gamma-Ray Imaging Detectors and Evaluation of a Multi-Anode PMT Scintillation Camera for Use with Maximum-Likelihood Estimation Methodsen_US
dc.creatorHunter, William Coulis Jasonen_US
dc.contributor.authorHunter, William Coulis Jasonen_US
dc.date.issued2007en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © 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.en_US
dc.description.abstractMaximum-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.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectMaximum-Likelihooden_US
dc.subjectGamma-Ray Detectoren_US
dc.subjectStochasticen_US
dc.subjectModelingen_US
dc.subjectImagingen_US
thesis.degree.namePhDen_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplinePhysicsen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorBarrett, Harrison H.en_US
dc.contributor.chairBarrett, Harrison H.en_US
dc.contributor.chairBarrett, Bruce R.en_US
dc.contributor.committeememberFurenlid, Lars R.en_US
dc.contributor.committeememberKupinski, Matthew A.en_US
dc.contributor.committeememberVisscher, Koenen_US
dc.identifier.proquest2176en_US
dc.identifier.oclc659747271en_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.