PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping

2026-07-22

AI Summary

Current slice-to-volume reconstruction methods support only a single echo time (TE), hindering multi-echo quantitative T2 mapping and limiting precise assessment of fetal brain development. This work proposes the first implicit neural representation framework tailored for multi-echo MRI, employing a fully connected network to model a continuous mapping from spatial coordinates to multi-TE signal intensities. The approach enforces physical consistency across TEs by incorporating Bloch equation regularization and estimates slice-specific degradation through a self-supervised strategy. Evaluated on 39 fetal cases, the method achieves a 47% improvement in reconstruction sharpness, 30% higher anatomical accuracy, and 14% enhanced cross-TE structural consistency. It enables, for the first time at ultra-low field strength (0.55T), isotropic 0.8mm T2 mapping while reducing scan time from 15 minutes to 5–10 minutes.

Abstract

Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal brain volumes from motion-corrupted 2D MRI slice stacks acquired in multiple orientations. Existing SVR methods are optimized and validated only for clinical-range echo times (TEs), limiting their use at non-clinical TEs and making them incompatible with quantitative T2 mapping, a protocol- and center-independent biomarker of fetal brain maturation requiring HR reconstructions across multiple TEs. We present PRIME-SVR, the first implicit neural representation (INR) framework for joint HR reconstruction from multi-echo MRI. A single fully connected network models a continuous function from spatial coordinates to signal intensities across TEs, while a second network estimates slice-specific acquisition degradations. Cross-TE coherence is enforced via a Bloch equation-derived regularization penalizing deviations from expected T2 decay, with adaptive weighting that strengthens coupling for degraded stacks. The method is fully self-supervised. We validate PRIME-SVR on 39 in vivo fetal acquisitions (13 subjects x 3 TEs) from two centers, two vendors, and two field strengths (1.5 T and 0.55 T). Compared to state-of-the-art SVR, PRIME-SVR improves reconstruction sharpness by 47%, anatomical accuracy by 30%, and cross-TE structural consistency by 14%. It enables reconstruction at late TEs previously inaccessible to SVR, yielding the first 0.8 mm isotropic T2 maps at 0.55 T and the first T2 maps derived from INR-based SVR. PRIME-SVR also accelerates quantitative imaging by reducing the data needed for multi-TE reconstruction, cutting acquisition from 15 to 10 minutes while keeping T2 accuracy within 1.7% in white and deep gray matter, or to 5 minutes with a mean T2 error of 2.3% for high-quality acquisitions.

Problem

• slice-to-volume reconstruction
• fetal MRI
• T2 mapping
• multi-echo
• quantitative imaging

Innovation

• implicit neural representation
• slice-to-volume reconstruction
• multi-echo MRI
• T2 mapping
• self-supervised learning