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dc.contributor.authorChambers, Micah Christopheren_US
dc.date.accessioned2014-03-14T20:45:46Z
dc.date.available2014-03-14T20:45:46Z
dc.date.issued2010-09-14en_US
dc.identifier.otheretd-09212010-215625en_US
dc.identifier.urihttp://hdl.handle.net/10919/35143
dc.description.abstractTraditional methods of analyzing functional Magnetic Resonance Images use a linear combination of just a few static regressors. This work demonstrates an alternative approach using a physiologically inspired nonlinear model. By using a particle filter to optimize the model parameters, the computation time is kept below a minute per voxel without requiring a linearization of the noise in the state variables. The activation results show regions similar to those found in Statistical Parametric Mapping; however, there are some notable regions not detected by that technique. Though the parameters selected by the particle filter based approach are more than sufficient to predict the Blood-Oxygen-Level-Dependent signal response, more model constraints are needed to uniquely identify a single set of parameters. This illposed nature explains the large discrepancies found in other research that attempted to characterize the model parameters. For this reason the final distribution of parameters is more medically relevant than a single estimate. Because the output of the particle filter is a full posterior probability, the reliance on the mean to estimate parameters is unnecessary. This work presents not just a viable alternative to the traditional method of detecting activation, but an extensible technique of estimating the joint probability distribution function of the Blood-Oxygen-Level-Dependent Signal parameters.en_US
dc.publisherVirginia Techen_US
dc.relation.haspartChambers_MicahC_T_2010.pdfen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectBOLD Responseen_US
dc.subjectFMRIen_US
dc.subjectNonlinear Systemsen_US
dc.subjectParticle Filteren_US
dc.subjectBayesian Statisticsen_US
dc.subjectSystem Identificationen_US
dc.titleFull Brain Blood-Oxygen-Level-Dependent Signal Parameter Estimation Using Particle Filtersen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.committeechairWyatt, Christopher L.en_US
dc.contributor.committeememberBaumann, William T.en_US
dc.contributor.committeememberBeex, A. A. Louisen_US
dc.contributor.committeememberStilwell, Daniel J.en_US
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-09212010-215625/en_US
dc.date.sdate2010-09-21en_US
dc.date.rdate2011-01-05
dc.date.adate2011-01-05en_US


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