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

TL;DR

This paper proposes DACT(Diffusion-Based Adaptive Contrast Transport), a novel zero-shot framework that restores HF-quality images without paired supervision, and introduces 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.

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.

Figures (4)

• Figure 1: Overview of the DACT. (a) Soft histograms of input LF image $\boldsymbol{y}$ and HF estimate $\boldsymbol{z}$ are generated via kernel density estimation (KDE). The Sinkhorn algorithm computes an optimal transport plan for contrast alignment, while a learnable weight map $\boldsymbol{\alpha}$ adaptively balances contrast transformation and structural detail preservation. The known spatial degradation $\mathcal{H}$ is then applied to obtain the reconstructed LF estimate $\hat{\boldsymbol{y}}$. (b) A high-field prior $\hat{\boldsymbol{x}}_0$ generated by a pretrained diffusion model is incorporated to jointly optimize the target HF image and the forward degradation process.
• Figure 2: Qualitative comparisons of DACT with baselines on two representative synthetic LF data. The bottom row shows the downstream segmentation masks for GM, WM, and CSF based on the T2w results, with the average Dice scores.
• Figure 3: Qualitative reconstruction (top) and tissue segmentation (bottom) on real LF data. DACT yields sharp cortical contrast and highly accurate segmentation.
• Figure 4: Convergence analysis. (Left) All methods successfully minimize fidelity error. (Right) Validation error against the ground truth reveals baseline overfitting, whereas DACT stably converges to the authentic contrast transformation.