Physics-Informed Joint Multi-TE Super-Resolution with Implicit Neural Representation for Robust Fetal T2 Mapping
This work addresses motion-corrupted fetal MRI acquisition for T2 mapping, which is incremental as it builds on existing slice-to-volume reconstruction techniques with novel integration of implicit neural representations and physics modeling.
The paper tackles the challenge of long scan times and motion sensitivity in fetal brain T2 mapping by developing a method that jointly reconstructs data across echo times using implicit neural representations and physics-informed regularization, achieving state-of-the-art performance on simulated and in vivo datasets and presenting the first in vivo fetal T2 mapping results at 0.55T.
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.