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dc.contributor.advisorKupinski, Matthewen_US
dc.contributor.authorTrumbull, Tara
dc.creatorTrumbull, Taraen_US
dc.date.accessioned2011-10-10T22:34:23Z
dc.date.available2011-10-10T22:34:23Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/10150/144580
dc.description.abstractWe have developed a simulation of the AdaptiSPECT small-animal Single Photon Emission Computed Tomography (SPECT) imaging system. The simulation system is entitled SimAdaptiSPECT and is written in C, NVIDIA CUDA, and Matlab. Using this simulation, we have accomplished an analysis of the Scanning Linear Estimation (SLE) technique for estimating tumor parameters, and calculated sensitivity information for AdaptiSPECT configurations.SimAdaptiSPECT takes, as input, simulated mouse phantoms (generated by MOBY) contained in binary files and AdaptiSPECT configuration geometry contained in ASCII text files. SimAdaptiSPECT utilizes GPU parallel processing to simulate AdaptiSPECT images. SimAdaptiSPECT also utilizes GPU parallel processing to perform 3-D image reconstruction from 2-D AdaptiSPECT camera images (real or simulated), using a novel variant of the Ordered Subsets Expectation Maximization (OSEM) algorithm. Methods for generating the inputs, such as a population of randomly varying numerical mouse phantoms with randomly varying hepatic lesions, are also discussed.
dc.language.isoenen_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.subjectAdaptive Imagingen_US
dc.subjectGPUen_US
dc.subjectSimulationen_US
dc.subjectSLEen_US
dc.subjectSmall-animal imagingen_US
dc.subjectSPECTen_US
dc.titleSimulation and Analysis of an Adaptive SPECT Imaging System for Tumor Estimationen_US
dc.typeElectronic Thesisen_US
dc.typetexten_US
dc.identifier.oclc752261298
thesis.degree.grantorUniversity of Arizonaen_US
thesis.degree.levelmastersen_US
dc.contributor.committeememberClarkson, Ericen_US
dc.contributor.committeememberFurenlid, Larsen_US
dc.contributor.committeememberBarrett, Harrison Hen_US
dc.identifier.proquest11372
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineOptical Sciencesen_US
thesis.degree.nameM.S.en_US
refterms.dateFOA2018-08-22T06:41:57Z
html.description.abstractWe have developed a simulation of the AdaptiSPECT small-animal Single Photon Emission Computed Tomography (SPECT) imaging system. The simulation system is entitled SimAdaptiSPECT and is written in C, NVIDIA CUDA, and Matlab. Using this simulation, we have accomplished an analysis of the Scanning Linear Estimation (SLE) technique for estimating tumor parameters, and calculated sensitivity information for AdaptiSPECT configurations.SimAdaptiSPECT takes, as input, simulated mouse phantoms (generated by MOBY) contained in binary files and AdaptiSPECT configuration geometry contained in ASCII text files. SimAdaptiSPECT utilizes GPU parallel processing to simulate AdaptiSPECT images. SimAdaptiSPECT also utilizes GPU parallel processing to perform 3-D image reconstruction from 2-D AdaptiSPECT camera images (real or simulated), using a novel variant of the Ordered Subsets Expectation Maximization (OSEM) algorithm. Methods for generating the inputs, such as a population of randomly varying numerical mouse phantoms with randomly varying hepatic lesions, are also discussed.


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