Navigator motion-resolved MR fingerprinting using implicit neural representation: Feasibility for free-breathing three-dimensional whole-liver multiparametric mapping.
Academic Article
Overview
abstract
PURPOSE: To develop a multiparametric free-breathing three-dimensional, whole-liver quantitative maps of water T1, water T2, fat fraction (FF) and R2*. METHODS: A multi-echo 3D stack-of-spiral gradient-echo sequence with inversion recovery and T2-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 T1, T2, R2*, 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 T1, T2, R2*, 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 T1, T2, R2*, and FF mapping in a single free-breathing continuous scan.
