Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping

TL;DR

This paper introduces a physics-informed neural network approach for robust fetal T2 mapping in MRI, effectively handling motion artifacts and reducing scan time by leveraging implicit representations and multi-TE data sharing.

Contribution

It proposes a novel joint reconstruction method combining implicit neural representations with physics-based regularization for fetal T2 mapping, improving robustness and efficiency.

Findings

State-of-the-art performance on simulated and in vivo datasets

First in vivo fetal T2 mapping at 0.55T achieved

Potential to reduce number of stacks per TE in T2 mapping

Abstract

T2 mapping in fetal brain MRI has the potential to improve characterization of the developing brain, especially at mid-field (0.55T), where T2 decay is slower. However, this is challenging as fetal MRI acquisition relies on multiple motion-corrupted stacks of thick slices, requiring slice-to-volume reconstruction (SVR) to estimate a high-resolution (HR) 3D volume. Currently, T2 mapping involves repeated acquisitions of these stacks at each echo time (TE), leading to long scan times and high sensitivity to motion. We tackle this challenge with a method that jointly reconstructs data across TEs, addressing severe motion. Our approach combines implicit neural representations with a physics-informed regularization that models T2 decay, enabling information sharing across TEs while preserving anatomical and quantitative T2 fidelity. We demonstrate state-of-the-art performance on simulated fetal brain and in vivo adult datasets with fetal-like motion. We also present the first in vivo fetal T2 mapping results at 0.55T. Our study shows potential for reducing the number of stacks per TE in T2 mapping by leveraging anatomical redundancy.