Zero-shot Low-Field MRI Enhancement via Diffusion-Based Adaptive Contrast Transport

TL;DR

This paper introduces DACT, a zero-shot diffusion-based framework that enhances low-field MRI images to high-field quality by modeling contrast transformations without paired data, improving detail and tissue contrast fidelity.

Contribution

DACT is the first zero-shot method combining diffusion priors with adaptive contrast transport to recover high-quality MRI images without paired training data.

Findings

Achieves state-of-the-art reconstruction quality on simulated and real datasets.

Effectively models contrast transformation without paired supervision.

Preserves anatomical fidelity and tissue contrast in enhanced images.

Abstract

Low-field (LF) magnetic resonance imaging (MRI) democratizes access to diagnostic imaging but is fundamentally limited by low signal-to-noise ratio and significant tissue contrast distortion due to field-dependent relaxation dynamics. Reconstructing high-field (HF) quality images from LF data is a blind inverse problem, severely challenged by the scarcity of paired training data and the unknown, non-linear contrast transformation operator. Existing zero-shot methods, which assume simplified linear degradation, often fail to recover authentic tissue contrast. In this paper, we propose DACT(Diffusion-Based Adaptive Contrast Transport), a novel zero-shot framework that restores HF-quality images without paired supervision. DACT synergizes a pre-trained HF diffusion prior to ensure anatomical fidelity with a physically-informed adaptive forward model. Specifically, we introduce a differentiable Sinkhorn optimal transport module that explicitly models and corrects the intensity distribution shift between LF and HF domains during the reverse diffusion process. This allows the framework to dynamically learn the intractable contrast mapping while preserving topological consistency. Extensive experiments on simulated and real clinical LF datasets demonstrate that DACT achieves state-of-the-art performance, yielding reconstructions with superior structural detail and correct tissue contrast.