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Article Abstract
Purpose: To develop a multiparametric free-breathing three-dimensional, whole-liver quantitative maps of water T, water T, fat fraction (FF) and R*.
Methods: A multi-echo 3D stack-of-spiral gradient-echo sequence with inversion recovery and T-prep magnetization preparations was implemented for multiparametric MRI. Fingerprinting and a neural network based on implicit neural representation (FINR) were developed to simultaneously reconstruct the motion deformation fields, the static images, perform water-fat separation, and generate T, T, R*, and FF maps. FINR performance was evaluated in 10 healthy subjects by comparison with quantitative maps generated using conventional breath-holding imaging.
Results: FINR consistently generated sharp images in all subjects free of motion artifacts. FINR showed minimal bias and narrow 95% limits of agreement for T, T, R*, and FF values in the liver compared with conventional imaging. FINR training took about 3 h per subject, and FINR inference took less than 1 min to produce static images and motion deformation fields.
Conclusions: FINR is a promising approach for 3D whole-liver T, T, R*, and FF mapping in a single free-breathing continuous scan.
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| http://dx.doi.org/10.1002/mrm.70063 | DOI Listing |
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