Navigator motion-resolved MR fingerprinting using implicit neural representation: Feasibility for free-breathing three-dimensional whole-liver multiparametric mapping.
Authors
Affiliations (3)
Affiliations (3)
- Radiology, Weill Cornell Medicine, New York, New York, USA.
- Applied and Engineering Physics, Cornell University, Ithaca, New York, USA.
- Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
Abstract
To develop a multiparametric free-breathing three-dimensional, whole-liver quantitative maps of water T<sub>1</sub>, water T<sub>2</sub>, fat fraction (FF) and R<sub>2</sub>*. A multi-echo 3D stack-of-spiral gradient-echo sequence with inversion recovery and T<sub>2</sub>-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<sub>1</sub>, T<sub>2</sub>, R<sub>2</sub>*, and FF maps. FINR performance was evaluated in 10 healthy subjects by comparison with quantitative maps generated using conventional breath-holding imaging. FINR consistently generated sharp images in all subjects free of motion artifacts. FINR showed minimal bias and narrow 95% limits of agreement for T<sub>1</sub>, T<sub>2</sub>, R<sub>2</sub>*, 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. FINR is a promising approach for 3D whole-liver T<sub>1</sub>, T<sub>2</sub>, R<sub>2</sub>*, and FF mapping in a single free-breathing continuous scan.