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dc.contributor.advisorJi, Jim X
dc.creatorLusher II, John David
dc.date.accessioned2019-01-18T03:14:58Z
dc.date.available2019-01-18T03:14:58Z
dc.date.created2018-08
dc.date.issued2018-05-24
dc.date.submittedAugust 2018
dc.identifier.urihttps://hdl.handle.net/1969.1/173710
dc.description.abstractBrain connectivity networks help physicians better understand the neurological effects of certain diseases and make improved treatment options for patients. Voxel-to-Voxel Correlation Analysis (VVCA) of functional magnetic resonance imaging (fMRI) data has been used to create the individual brain connectivity networks. However, an outstanding issue is the long processing time to generate full brain connectivity maps. With close to a million individual voxels, with each having hundreds of samples, in a typical fMRI dataset, the number of calculations involved in a voxel-byvoxel CCA becomes very high. With the emergence of the dynamic time-varying functional connectivity analysis, the population-based studies, and the studies relying on real-time neurological feedbacks, the need for rapid processing methods becomes even more critical. This research dissertation describes a new method which produces high-resolution brain connectivity maps rapidly. This new method accelerates the correlation processing by using an architecture that includes clustered FPGAs and an efficient memory pipeline, which is termed the High-Performance Correlation and Mapping Engine (HPCME). The method has been tested with various datasets from the Human Connectome Project. The results show that HPCME with four FPGAs can improve the VVCA processing speed by a factor of 40 or more over that of a PC workstation with a multicore CPU.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectBrain Functional Connectivityen
dc.subjectfMRIen
dc.subjectSeed-based Correlation Analysisen
dc.subjectFPGA-based Parallel Computingen
dc.subjectHuman Connectome Projecten
dc.titleHigh-Performance Correlation and Mapping Engine for Rapid Generating Brain Connectivity Networks from Big fMRI Dataen
dc.typeThesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical Engineeringen
thesis.degree.grantorTexas A & M Universityen
thesis.degree.nameDoctor of Philosophyen
thesis.degree.levelDoctoralen
dc.contributor.committeeMemberOrr, Joseph M
dc.contributor.committeeMemberLi, Peng
dc.contributor.committeeMemberRighetti, Raffaella
dc.contributor.committeeMemberWang, Suojin
dc.type.materialtexten
dc.date.updated2019-01-18T03:14:59Z
local.etdauthor.orcid0000-0003-4640-5188


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