Navigator motion‐resolved MR fingerprinting using implicit neural representation: Feasibility for free‐breathing three‐dimensional whole‐liver multiparametric mapping

作者：Chao Li, Jiahao Li, Jinwei Zhang, Eddy Solomon, Alexey Dimov, Pascal Spincemaille, Thanh D. Nguyen, Martin R. Prince, Yi Wang · 发表于：Magnetic Resonance in Medicine · 年份：2025 · DOI：10.1002/mrm.70063 · 被引用次数：1 · 研究领域：Advanced MRI Techniques and Applications、MRI in cancer diagnosis、Advanced Neuroimaging Techniques and Applications

Abstract Purpose To develop a multiparametric free‐breathing three‐dimensional, whole‐liver quantitative maps of water T 1 , water T 2 , fat fraction (FF) and R 2 *. Methods A multi‐echo 3D stack‐of‐spiral gradient‐echo sequence with inversion recovery and T 2 ‐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 1 , T 2 , R 2 *, 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 1 , T 2 , R 2 *, 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 1 , T 2 , R 2 *, and FF mapping in a single free‐breathing continuous scan.