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Full Brain Blood-Oxygen-Level-Dependent Signal Parameter Estimation Using Particle Filters

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dc.contributor.authorChambers, Micah Christopheren
dc.contributor.committeechairWyatt, Christopher L.en
dc.contributor.committeememberBaumann, William T.en
dc.contributor.committeememberBeex, A. A. Louisen
dc.contributor.committeememberStilwell, Daniel J.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2014-03-14T20:45:46Zen
dc.date.adate2011-01-05en
dc.date.available2014-03-14T20:45:46Zen
dc.date.issued2010-09-14en
dc.date.rdate2011-01-05en
dc.date.sdate2010-09-21en
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
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-09212010-215625en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-09212010-215625/en
dc.identifier.urihttp://hdl.handle.net/10919/35143en
dc.publisherVirginia Techen
dc.relation.haspartChambers_MicahC_T_2010.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectBOLD Responseen
dc.subjectFMRIen
dc.subjectNonlinear Systemsen
dc.subjectParticle Filteren
dc.subjectBayesian Statisticsen
dc.subjectSystem Identificationen
dc.titleFull Brain Blood-Oxygen-Level-Dependent Signal Parameter Estimation Using Particle Filtersen
dc.typeThesisen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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