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dc.contributor.authorOtazo, Ricardo
dc.date.accessioned2008-02-07T18:35:04Z
dc.date.available2008-02-07T18:35:04Z
dc.date.issued2008-02-07T18:35:04Z
dc.date.submittedDecember 2007
dc.identifier.urihttp://hdl.handle.net/1928/3610
dc.description.abstractParallel magnetic resonance imaging offers a framework for acceleration of conventional MRI encoding using an array of receiver coils with spatially-varying sensitivities. Novel encoding and reconstruction techniques for parallel MRI are investigated in this dissertation. The main goal is to improve the actual reconstruction methods and to develop new approaches for massively parallel MRI systems that take advantage of the higher information content provided by the large number of small receivers. A generalized forward model and inverse reconstruction with regularization for parallel MRI with arbitrary k-space sub-sampling is developed. Regularization methods using the singular value decomposition of the encoding matrix and pre-conditioning of the forward model are proposed to desensitize the solution from data noise and model errors. Variable density k-space sub-sampling is presented to improve the reconstruction with the common uniform sub-sampling. A novel method for massively parallel MRI systems named Superresolution Sensitivity Encoding (SURE-SENSE) is proposed where acceleration is performed by acquiring the low spatial resolution representation of the object being imaged and the stronger sensitivity variation from small receiver coils is used to perform intra-pixel reconstruction. SURE-SENSE compares favorably the performance of standard SENSE reconstruction for low spatial resolution imaging such as spectroscopic imaging. The methods developed in this dissertation are applied to Proton Echo Planar Spectroscopic Imaging (PEPSI) for metabolic imaging in human brain with high spatial and spectral resolution in clinically feasible acquisition times. The contributions presented in this dissertation are expected to provide methods that substantially enhance the utility of parallel MRI for clinical research and to offer a framework for fast MRSI of human brain with high spatial and spectral resolution.en_US
dc.description.sponsorshipNational Institutes of Health Grants R01 HD040712, R01 NS037462, R01 EB000790-04, P41 RR14075, R01 EB002618-01, the Mental Illness and Neuroscience Discovery Institute (MIND), and the Ibero-American Science and Technology Education Consortium (ISTEC).en
dc.format.extent4537334 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.subjectmagnetic resonance imagingen_US
dc.subjectparallel imagingen_US
dc.subjectinverse reconstructionen_US
dc.subjectsuperresolutionen_US
dc.subject.lcshBrain--Magnetic resonance imaging
dc.subject.lcshMagnetic resonance imaging.
dc.subject.lcshSpectroscopic imaging.
dc.subject.lcshMagnetic resonance imaging.
dc.titleAdvanced parallel magnetic resonance imaging methods with applications to MR spectroscopic imagingen_US
dc.typeDissertationen_US
dc.description.degreeDoctor of Electrical Engineeringen
dc.description.levelDoctoralen
dc.description.departmentUniversity of New Mexico. Dept. of Electrical and Computer Engineeringen
dc.description.advisorPosse, Stefan
dc.description.advisorJordan, Ramiro
dc.description.committee-memberPattichis, Marios
dc.description.committee-memberCaprihan, Arvind


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