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

Xinliu Zhong, Ruiying Liu, Guohao Lin, Chuan Huang, Adam Ezra Goldman-Yassen, Amy Robben Mehollin-Ray, Yun Wang

Keywords: MRI, Deep Learning, Diffusion Models, Image Enhancement, Fetal Neuroimaging

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 $B_1$ 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 the 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 enabling robustness across axial, coronal, and sagittal motion profiles. 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.

Primary Subject Area: Application: Neuroimaging

Secondary Subject Area: Image Synthesis

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Submission Number: 359