Towards Trustworthy Selective Generation: Reliability-Guided Diffusion for Ultra-Low-Field to High-Field MRI Synthesis

Abstract

Low-field to high-field MRI synthesis has emerged as a cost-effective strategy to enhance image quality under hardware and acquisition constraints, particularly in scenarios where access to high-field scanners is limited or impractical. Despite recent progress in diffusion models, diffusion-based approaches often struggle to balance fine-detail recovery and structural fidelity. In particular, the uncontrolled generation of high-resolution details in structurally ambiguous regions may introduce anatomically inconsistent patterns, such as spurious edges or artificial texture variations. These artifacts can bias downstream quantitative analysis. For example, they may cause inaccurate tissue boundary delineation or erroneous volumetric estimation, ultimately reducing clinical trust in synthesized images. These limitations highlight the need for generative models that are not only visually accurate but also spatially reliable and anatomically consistent. To address this issue, we propose a reliability-aware diffusion framework (ReDiff) that improves synthesis robustness at both the sampling and post-generation stages. Specifically, we introduce a reliability-guided sampling strategy to suppress unreliable responses during the denoising process. We further develop an uncertainty-aware multi-candidate selection scheme to enhance the reliability of the final prediction. Experiments on multi-center MRI datasets demonstrate improved structural fidelity and reduced artifacts compared with state-of-the-art methods.

Figures (4)

• Figure 1: Motivation and challenges of our Reliability-Guided Diffusion. (A) Low-field MRI (e.g., 64 mT) is cost-effective and accessible but typically produces lower-quality images. LF-to-HF synthesis seeks to recover high-field (e.g., 3T) image fidelity under hardware and acquisition constraints. (B) Despite recent progress, diffusion-based methods may introduce unreliable high-resolution artifacts, such as artificial textures and spurious edges. These errors can degrade downstream tasks, leading to inaccurate segmentation, biased volume estimation, and reduced clinical trust.
• Figure 2: Reliability-Guided Diffusion (ReDiff) framework for LF-to-HF MRI synthesis. Given a low-field (LF) image $x$, a conditional diffusion U-Net predicts the noise term $\epsilon_\theta(y_t,x,t)$ to iteratively recover the high-field (HF) image. (a) In the proposed reliability-guided sampling (RGS), a spatial reliability map $\mathbf{R}(x)$ modulates the reverse diffusion update to suppress unsupported high-frequency amplification in ill-posed regions. (b) To further enhance robustness, uncertainty-aware candidate selection (UCS) generates multiple candidates and performs reliability-weighted aggregation based on spatial uncertainty. Together, the two-stage design improves structural consistency while reducing hallucinated details in LF-to-HF synthesis.
• Figure 3: Qualitative comparison of multi-contrast low-field to high-field MRI synthesis. For each case, the top row shows full images and the bottom row shows zoomed views of the red boxed regions. ReDiff produces sharper anatomical structures and fewer artifacts, with PSNR and SSIM reported on each result.
• Figure 4: Qualitative and quantitative downstream validation. (a) 3D structural visualization of SynthSeg-derived segmentations for representative cases. (b) Regional Dice distributions for the ventricle, thalamus, and hippocampus in both hemispheres. (c) Volumetric correlation with respect to the 3T reference.