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Model‐based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin‐echo MRI
Author(s) -
Sumpf Tilman J.,
Uecker Martin,
Boretius Susann,
Frahm Jens
Publication year - 2011
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.22634
Subject(s) - undersampling , computer science , algorithm , cartesian coordinate system , nonlinear system , compressed sensing , iterative reconstruction , spin echo , real time mri , artificial intelligence , magnetic resonance imaging , mathematics , physics , radiology , medicine , geometry , quantum mechanics
Purpose: To develop a model‐based reconstruction technique for T2 mapping based on multi‐echo spin‐echo MRI sequences with highly undersampled Cartesian data encoding. Materials and Methods: The proposed technique relies on a nonlinear inverse reconstruction algorithm which directly estimates a T2 and spin‐density map from a train of undersampled spin echoes. The method is applicable to acquisitions with single receiver coils but benefits from multi‐element coil arrays. The algorithm is validated for trains of 16 spin echoes with a spacing of 10 to 12 ms using numerical simulations as well as human brain MRI at 3 Tesla (T). Results: When compared with a standard T2 fitting procedure using fully sampled T2‐weighted images, and depending on the available signal‐to‐noise ratio and number of coil elements, model‐based nonlinear inverse reconstructions for both simulated and in vivo MRI data yield accurate T2 estimates for undersampling factors of 5 to 10. Conclusion: This work describes a promising strategy for T2‐weighted MRI that simultaneously offers accurate T2 relaxation times and properly T2‐weighted images at arbitrary echo times. For a standard spin‐echo MRI sequence with Cartesian encoding, the method allows for a much higher degree of undersampling than obtainable by conventional parallel imaging. J. Magn. Reson. Imaging 2011;. © 2011 Wiley‐Liss, Inc.