Automated reconstruction of dynamic 2D fetal cardiac MRI using deep learning in late third-trimester fetuses.

Authors

William Myers, Datta Singh Goolaub, Mike Seed, Christopher K Macgowan

Published in

European journal of radiology. Volume 205. Pages 113197. Aug 30, 2026. Epub Aug 30, 2026.

Abstract

Fetal cardiovascular magnetic resonance (CMR) suffers from motion corruption and cardiac gating challenges. Data-driven motion correction and gating remain dependent on manual region-of-interest (ROI) selection, limiting clinical utility through resource demands, processing time, and inter-observer variability. We developed an nnU-Net model to automatically select ROIs and integrated it into a cine reconstruction pipeline.
2327 real-time images acquired as multi-slice stacks from 23 late third-trimester pregnancies (34-36 weeks gestational age), comprising 21 healthy fetuses and 2 with congenital heart disease, were used to train and test the model. Images varied in orientation, temporal resolution, and signal-to-noise ratio. Manual and automatic ROI selections were compared using the Dice similarity coefficient, ROI sizes, and ROI centroids, and were separately input into a cine reconstruction pipeline with motion correction and metric-optimized gating. Cines were compared between methods using in-plane translational parameters, RR intervals, mutual information (MI), the blind Perception-derived Image Quality Evaluator (PIQE), and qualitative anatomical review.
The nnU-Net model achieved a mean Dice score of 0.83 ± 0.23 with 81% of scores exceeding 0.8. No significant differences were found between ROI sizes (p = 0.13), translational parameters (along x-dimension: p = 0.58 or y-dimension: p = 0.11), or MI values (p = 0.59). Automatic and manual cines showed broadly comparable image quality, with statistically lower PIQE scores for automatic cines (p < 0.001) but a small absolute difference (∼0.5 points) of uncertain clinical significance. Cines displayed qualitatively comparable anatomical fidelity.
Deep-learning-automated ROI selection for fetal CMR reconstruction is feasible and addresses a key barrier to clinical adoption of the modality.

PMID:
42700702
Bibliographic data and abstract were imported from PubMed on 06 Sep 2026.

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