MRIQT: Image-quality transfer from ultra-low-field to high-field neonatal MRI
This project addresses the limited diagnostic usability of portable ultra-low-field MRI (0.064 T) in neonatal neuroimaging, where reduced signal-to-noise ratio constrains clinical interpretation despite bedside accessibility.
Abstract
This project addresses the limited diagnostic usability of portable ultra-low-field MRI (0.064 T) in neonatal neuroimaging, where reduced signal-to-noise ratio constrains clinical interpretation despite bedside accessibility. Together with the clinical partners, we curated a unique in-house cohort comprising 50 paired ultra-low-field and high-field 3D neonatal brain MRI scans, complemented by 230 additional unpaired scans across nine pathologies. Building on this paired–unpaired data structure, we developed MRIQT, a 3D conditional diffusion framework for structure-preserving image quality transfer from ultra-low-field to high-field MRI. The method integrates physics-consistent k-space degradation, classifier-free guided diffusion, and an SNR-weighted volumetric perceptual loss.
MRIQT outperformed CNN- and GAN-based baselines, achieving up to 15.3% improvement in PSNR, while expert clinicians rated 85% of generated images as diagnostically useful with preserved pathology.
Perspective
The project provides a methodological foundation for automated MRI quality enhancement and modality-agnostic quality control. Building on the current results, the work is prepared for translational follow-up and prospective validation, with a planned submission to the Go-Bio Initial funding program.
