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dc.contributor.advisorMitra, Debasis
dc.contributor.authorChang, Haoran
dc.date.accessioned2016-12-15T15:37:49Z
dc.date.available2016-12-15T15:37:49Z
dc.date.issued2016-12
dc.identifier.urihttp://hdl.handle.net/11141/1109
dc.descriptionThesis (M.S.) - Florida Institute of Technology, 2016en_US
dc.description.abstractNon-negative matrix factorization (NMF) is a useful method for those non-negative multivariate data. In nuclear imaging, NMF, which is also called factor analysis, is used to analyze the 4D image of positron emission tomography and single positron emission computed tomography. Normally, we use factor analysis of dynamic structure (FADS) for the dynamic reconstruction [1]. However, this reconstruction algorithm is extremely dependent on the initial data, which means the result may be unstable when the initial data is not good enough. There are several ways for the initializing, such as splined initialized (SIFADS) [2] and clustering initialized (CIFA) [3][4]. In this thesis, we have attempted different ways to improve uninitialized FADS. The dataset we used is the real human data.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.rightsCC BY 4.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/legalcodeen_US
dc.titleStudies with Dynamic Nuclear Imaging Image Reconstruction Algorithmen_US
dc.typeThesisen_US
dc.date.updated2016-12-07T21:13:28Z
thesis.degree.nameMaster of Science in Computer Scienceen_US
thesis.degree.levelMastersen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.departmentComputer Sciencesen_US
thesis.degree.grantorFlorida Institute of Technologyen_US
dc.type.materialtext


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