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Motion Robust Magnetic Susceptibility and Field Inhomogeneity Estimation Using Regularized Image Restoration Techniques for fMRI

dc.contributor.authorYeo, Desmond Teck Bengen_US
dc.contributor.authorFessler, Jeffrey A.en_US
dc.contributor.authorKim, Boklyeen_US
dc.date.accessioned2011-08-18T18:21:10Z
dc.date.available2011-08-18T18:21:10Z
dc.date.issued2008-09-06en_US
dc.identifier.citationYeo, D. T. B.; Fessler, J. A.; Kim, B. (2008). "Motion Robust Magnetic Susceptibility and Field Inhomogeneity Estimation Using Regularized Image Restoration Techniques for fMRI." Lecture Notes in Computer Science 5241: 991-998. <http://hdl.handle.net/2027.42/85944>en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/85944
dc.description.abstractIn functional MRI, head motion may cause dynamic nonlinear field-inhomogeneity changes, especially with large out-of-plane rotations. This may lead to dynamic geometric distortion or blurring in the time series, which may reduce activation detection accuracy. The use of image registration to estimate dynamic field inhomogeneity maps from a static field map is not sufficient in the presence of such rotations. This paper introduces a retrospective approach to estimate magnetic susceptibility induced field maps of an object in motion, given a static susceptibility induced field map and the associated object motion parameters. It estimates a susceptibility map from a static field map using regularized image restoration techniques, and applies rigid body motion to the former. The dynamic field map is then computed using susceptibility voxel convolution. The method addresses field map changes due to out-of-plane rotations during time series acquisition and does not involve real time field map acquisitions.en_US
dc.publisherSpringeren_US
dc.titleMotion Robust Magnetic Susceptibility and Field Inhomogeneity Estimation Using Regularized Image Restoration Techniques for fMRIen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelBiomedical Engineeringen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Radiology.Department of Electrical Engineering and Computer Science.en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/85944/1/Fessler233.pdf
dc.identifier.doi10.1007/978-3-540-85988-8_118en_US
dc.identifier.sourceLecture Notes in Computer Scienceen_US
dc.owningcollnameElectrical Engineering and Computer Science, Department of (EECS)


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