Abstract
Low-field MRI offers a cost-effective and portable alternative to conventional high-field systems, but its clinical use is limited by reduced image quality. Recent advancements in deep learning have aimed to enhance low-field scans, including approaches based on GANs, stochastic quality transfer, and denoising diffusion models. In this paper, we introduce a bridged denoising diffusion model that explicitly aligns the latent spaces of low- and high-field MR images through a learned bridging model. At a predefined timestep in the diffusion process, a bridge network translates the noisy low-field representation into the high-field domain, enabling the downstream diffusion model, trained solely on high-field data, to generate high-field-like images. The proposed method is trained on paired 3T and 0.64 mT scans across multiple contrasts, based on the “Enhancing Ultra-Low-Field MRI with Paired High-Field MRI Comparisons for Brain Imaging” challenge. On the hidden validation set, our method achieved an SSIM of 0.779 and a PSNR of 22.85 dB, outperforming alternative configurations in ablation experiments. We show that our model can significantly enhance low-field images.
Barış İmre
PhD Candidate
Marius Staring
Professor
