Implicit neural representation for free-breathing MR fingerprinting (INR-MRF): co-registered 3D whole-liver water T1, water T2, proton density fat fraction, and R2* mapping

TL;DR

This paper introduces INR-MRF, a neural network-based MRI technique that enables simultaneous, free-breathing 3D mapping of multiple liver tissue parameters with high accuracy, eliminating the need for breath-holding or separate water-fat separation procedures.

Contribution

The study presents a novel implicit neural representation approach for co-registered 3D liver mapping of T1, T2, R2*, and PDFF during free-breathing, improving efficiency and accuracy over traditional methods.

Findings

Minimal bias in liver parameter measurements compared to conventional scans

High agreement with reference quantitative maps

Successful 3D whole-liver mapping in free-breathing conditions

Abstract

Purpose: To develop an MRI technique for free-breathing 3D whole-liver quantification of water T1, water T2, proton density fat fraction (PDFF), R2*. Methods: An Eight-echo spoiled gradient echo pulse sequence with spiral readout was developed by interleaving inversion recovery and T2 magnetization preparation. We propose a neural network based on a 4D and a 3D implicit neural representation (INR) which simultaneously learns the motion deformation fields and the static reference frame MRI subspace images respectively. Water and fat singular images were separated during network training, with no need of performing retrospective water-fat separation. T1, T2, R2* and proton density fat fraction (PDFF) produced by the proposed method were validated in vivo on 10 healthy subjects, using quantitative maps generated from conventional scans as reference. Results: Our results showed minimal bias and narrow 95% limits of agreement on T1, T2, R2* and PDFF values in the liver compared to conventional breath-holding scans. Conclusions: INR-MRF enabled co-registered 3D whole liver T1, T2, R2* and PDFF mapping in a single free-breathing scan.