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dc.contributor.advisorCiprian Catana.en_US
dc.contributor.authorChen, Kevin Tze-Hsiangen_US
dc.contributor.otherHarvard--MIT Program in Health Sciences and Technology.en_US
dc.date.accessioned2017-09-15T14:21:27Z
dc.date.available2017-09-15T14:21:27Z
dc.date.copyright2017en_US
dc.date.issued2017en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/111254
dc.descriptionThesis: Ph. D. in Medical Engineering and Medical Physics, Harvard-MIT Program in Health Sciences and Technology, 2017.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 147-155).en_US
dc.description.abstractRecent advances have allowed the hardware integration of positron emission tomography (PET) and magnetic resonance imaging (MRI). The spatiotemporally correlated data acquisition opened up opportunities for numerous applications. Furthermore, the MRI data can be utilized to improve the PET scanner performance. While PET has many advantages, including the fact that it could provide a quantitative means to assess in vivo biological processes, its accuracy is confounded by several factors. For example, attenuation correction is required to account for the interactions of the annihilation photons in the subject; motion correction is needed to minimize image degradation due to subject movements; partial volume effects correction is required due to the relatively limited spatial resolution. Although many applications could benefit from these methodological improvements, in this thesis we focused on dementia. MRI and PET are widely used and provide complementary information in the assessment of these patients. Equally important, dementia is a great test situation for these methodological developments because the confounding factors mentioned above are especially pronounced in this patient population. In this work, we developed a unified protocol to address these limitations, an approach we termed MR-assisted PET data optimization. Specifically, we first developed methods to derive head attenuation maps from the morphological MR images. Next, we used temporally-correlated MR data for PET motion compensation and spatially-correlated MR data for anatomy-aided reconstruction. Finally, we demonstrated that after applying these tools to data acquired in dementia patients the PET data quantification was positively impacted and the image quality improved substantially..en_US
dc.description.statementofresponsibilityby Kevin Tze-Hsiang Chen.en_US
dc.format.extent155 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectHarvard--MIT Program in Health Sciences and Technology.en_US
dc.titleMR-assisted PET data optimization for simultaneous dual-modality imaging in dementiaen_US
dc.title.alternativeMagnetic resonance-assisted positron emission tomography data optimization for simultaneous dual-modality imaging in dementiaen_US
dc.typeThesisen_US
dc.description.degreePh. D. in Medical Engineering and Medical Physicsen_US
dc.contributor.departmentHarvard--MIT Program in Health Sciences and Technology.en_US
dc.identifier.oclc1003290751en_US


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