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Iterative Image Reconstruction in MRI with Separate Magnitude and Phase Regularization

dc.contributor.authorFessler, Jeffrey A.en_US
dc.contributor.authorNoll, Douglas C.en_US
dc.date.accessioned2011-08-18T18:20:44Z
dc.date.available2011-08-18T18:20:44Z
dc.date.issued2004-04-15en_US
dc.identifier.citationFessler, J.A.; Noll, D.C. (2004). "Iterative Image Reconstruction in MRI with Separate Magnitude and Phase Regularization." IEEE International Symposium on Biomedical Imaging: Nano to Macro 1: 209-212. <http://hdl.handle.net/2027.42/85802>en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/85802
dc.description.abstractIterative methods for image reconstruction in MRI are useful in several applications, including reconstruction from non-Cartesian k-space samples, compensation for magnetic field inhomogeneities, and imaging with multiple receive coils. Existing iterative MR image reconstruction methods are either unregularized, and therefore sensitive to noise, or have used regularization methods that smooth the complex valued image. These existing methods regularize the real and imaginary components of the image equally. In many MRI applications, including T2*-weighted imaging as used in fMRI BOLD imaging, one expects most of the signal information of interest to be contained in the magnitude of the voxel value, whereas the phase values are expected to vary smoothly spatially. This paper proposes separate regularization of the magnitude and phase components, preserving the spatial resolution of the magnitude component while strongly regularizing the phase component. This leads to a non-convex regularized least-squares cost function. We describe a new iterative algorithm that monotonically decreases this cost function. The resulting images have reduced noise relative to conventional regularization methods.en_US
dc.publisherIEEEen_US
dc.titleIterative Image Reconstruction in MRI with Separate Magnitude and Phase Regularizationen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelBiomedical Engineeringen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumBME Dept.en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/85802/1/Fessler194.pdf
dc.identifier.doi10.1109/ISBI.2004.1398511en_US
dc.identifier.sourceIEEE International Symposium on Biomedical Imaging: Nano to Macroen_US
dc.owningcollnameElectrical Engineering and Computer Science, Department of (EECS)


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