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dc.contributor.advisorLawrence P. Panych.en_US
dc.contributor.authorYoo, Seung-Schik, 1970-en_US
dc.contributor.otherHarvard University--MIT Division of Health Sciences and Technology.en_US
dc.date.accessioned2012-05-21T17:27:20Z
dc.date.available2012-05-21T17:27:20Z
dc.date.copyright2000en_US
dc.date.issued2000en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/70893
dc.descriptionThesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 2000.en_US
dc.descriptionSome research performed with the Harvard-M.I.T. Division of Health Sciences and Technology.en_US
dc.descriptionIncludes bibliographical references (leaves 132-140).en_US
dc.description.abstractFunctional MRI (fMRI) detects the signal associated with neuronal activation, and has been widely used to map brain functions. Locations of neuronal activation are localized and distributed throughout the brain, however, conventional encoding methods based on k-space acquisition have limited spatial selectivity. To improve it, we propose an adaptive fMRI method using non-Fourier, spatially selective RF encoding. This method follows a strategy of zooming into the locations of activation by progressively eliminating the regions that do not show any apparent activation. In this thesis, the conceptual design and implementation of adaptive fMRI are pursued under the hypothesis that the method may provide a more efficient means to localize functional activities with increased spatial or temporal resolution. The difference between functional detection and mapping is defined, and the multi- resolution approach for functional detection is examined using theoretical models simulating variations in both in-plane and through-plane resolution. We justify the multi-resolution approach experimentally using BOLD CNR as a quantitative measure and compare results to those obtained using theoretical models. We conclude that there is an optimal spatial resolution to obtain maximum detection; when the resolution matches the size of the functional activation. We demonstrated on a conventional 1.5-Tesla system that RF encoding provides a simple means for monitoring irregularly distributed slices throughout the brain without encoding the whole volume. We also show the potential for increased signal-to-noise ratio with Hadamard encoding as well as reduction of the in-flow effect with unique design of excitation pulses.en_US
dc.description.abstract(cont.) RF encoding was further applied in the implementation of real-time adaptive fMRI method, where we can zoom into the user-defined regions interactively. In order to do so, real-time pulse prescription and data processing capabilities were combined with RF encoding. Our specific implementation consisted of five scan stages tailored to identify the volume of interest, and to increase temporal resolution (from 7.2 to 3.2 seconds) and spatial resolution (from 10 mm to 2.5-mm slice thickness). We successfully demonstrated the principle of the multi- resolution adaptive fMRI method in volunteers performing simple sensorimotor paradigms for simultaneous activation of primary motor as well as cerebellar areas.en_US
dc.description.statementofresponsibilityby Seung-Schik Yoo.en_US
dc.format.extent140 leavesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectNuclear Engineering.en_US
dc.subjectHarvard University--MIT Division of Health Sciences and Technology.en_US
dc.titleAdaptive functional magnetic resonance imagingen_US
dc.title.alternativeAdaptive fMRIen_US
dc.title.alternativeAdaptive functional MRIen_US
dc.typeThesisen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Nuclear Engineering.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technology.en_US
dc.identifier.oclc48759378en_US


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