Orientation-Aware Diffusion Super-Resolution for 3T-Like Fetal MRI from Routine 1.5T Scans.

Overview

1. Department of Computer Science, Emory University, Atlanta, GA, USA
2. Department of Biomedical Informatics, Emory University, Atlanta, GA, USA
3. Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA
4. Children’s Healthcare of Atlanta, Atlanta, GA, USA

Abstract

Fetal MRI plays a central role in assessing early brain development. While 3T scanners offer higher SNR and improved cortical detail, their increased sensitivity to motion, susceptibility artifacts, and B1 inhomogeneity limits wide adoption for routine fetal imaging. Consequently, most clinical examinations are performed at 1.5T, where greater motion tolerance comes at the cost of lower SNR, reduced gray-white matter contrast, and partial-volume blurring - factors that undermine downstream morphometric analysis. Bridging this quality gap without sacrificing motion robustness of 1.5T would enable 3T-like morphometric reliability in routine clinical acquisitions.

We propose an orientation-aware diffusion super-resolution framework that synthesizes 3T-like fetal brain contrast from routine 1.5T scans. The model combines a Swin-UNet backbone with gated FiLM-based orientation embeddings and a residual error-shifting diffusion mechanism. Training leverages the FaBiAN phantom to generate controllable high-/low-resolution pairs with monotonic intensity remapping, geometric perturbations, and simulated signal voids, thereby ensuring generalization to clinical data. Our model produces markedly sharper gyri and mitigates partial-volume effects in both synthesized and clinical data. When evaluated using Fetal-SynthSeg following NeSVoR reconstruction, the framework consistently improves tissue segmentation accuracy over state-of-the-art restoration baselines, yielding more reliable morphometric estimates for fetal brain analysis.

Reproduced under the paper's license (CC BY), from the paper cited above.

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• doi:10.7910/dvn/we9jvr, at the source; found in the references

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 7 authors, 4 keywords, 2 funders, 12 references.

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This paper

Zhong, X., Liu, R., Lin, G., Huang, C., Goldman-Yassen, A. E., Mehollin-Ray, A. R., & Wang, Y. (2026). Orientation-Aware Diffusion Super-Resolution for 3T-Like Fetal MRI from Routine 1.5T Scans. Proceedings of machine learning research, 315, 3827-3845.

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