MRIQT: Physics-Aware Diffusion Model for Image Quality Transfer in Neonatal Ultra-Low-Field MRI

• URL: http://arxiv.org/abs/2511.13232v1
• Date: Mon, 17 Nov 2025 10:51:11 GMT
• Title: MRIQT: Physics-Aware Diffusion Model for Image Quality Transfer in Neonatal Ultra-Low-Field MRI
• Authors: Malek Al Abed, Sebiha Demir, Anne Groteklaes, Elodie Germani, Shahrooz Faghihroohi, Hemmen Sabir, Shadi Albarqouni,
• Abstract summary: Portable ultra-low-field MRI (uLF-MRI) offers neuroimaging accessible for neonatal care but suffers from low signal-to-noise ratio and poor diagnostic quality.<n>We propose MRIQT, a 3D conditional diffusion framework for image quality transfer (IQT) from uLF to HF MRI.
• Score: 2.180751253048667
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Portable ultra-low-field MRI (uLF-MRI, 0.064 T) offers accessible neuroimaging for neonatal care but suffers from low signal-to-noise ratio and poor diagnostic quality compared to high-field (HF) MRI. We propose MRIQT, a 3D conditional diffusion framework for image quality transfer (IQT) from uLF to HF MRI. MRIQT combines realistic K-space degradation for physics-consistent uLF simulation, v-prediction with classifier-free guidance for stable image-to-image generation, and an SNR-weighted 3D perceptual loss for anatomical fidelity. The model denoises from a noised uLF input conditioned on the same scan, leveraging volumetric attention-UNet architecture for structure-preserving translation. Trained on a neonatal cohort with diverse pathologies, MRIQT surpasses recent GAN and CNN baselines in PSNR 15.3% with 1.78% over the state of the art, while physicians rated 85% of its outputs as good quality with clear pathology present. MRIQT enables high-fidelity, diffusion-based enhancement of portable ultra-low-field (uLF) MRI for deliable neonatal brain assessment.

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