Subject-Specific Low-Field MRI Synthesis via a Neural Operator

Ultra-low-field (ULF) MRI offers portable and accessible neuroimaging but suffers from reduced signal-to-noise ratio and limited spatial resolution compared to high-field (HF) systems. Acquiring paired ULF-HF data for supervised enhancement is often difficult, particularly in resource-limited settings. We introduce ULF-Synth, a framework that combines: (i) acquisition-based synthesis of realistic ULF images from HF volumes to create large-scale paired training data, (ii) a spatial-frequency domain objective that prioritizes recovery of high-frequency anatomical detail. This formulation is architecture-agnostic, consistently improving structural similarity and perceptual fidelity across encoder-decoder, adversarial, and diffusion-based translation models. When trained exclusively on synthetic data, the resulting models generalize effectively to real 64mT ULF acquisitions, improving downstream multiclass brain segmentation and achieving higher radiologist preference and diagnostic acceptability in a blinded reader study. These findings demonstrate that synthetic paired supervision provides a practical and scalable pathway for enhancing ULF MRI without requiring real paired acquisitions. Code, Models and Dataset: https://github.com/toufiqmusah/ULF-Synth

Low-field to high-field MRI synthesis has emerged as a promising strategy to improve image quality when access to high-field scanners is limited. However, in ultra-low-field settings, the degradation of anatomical detail is spatially heterogeneous: structurally ambiguous regions are more susceptible to unstable high-frequency generation, which may produce anatomically inconsistent textures and boundaries. This issue is particularly problematic when synthesized images are used for downstream quantitative analysis. We therefore study how to make diffusion-based LF-to-HF synthesis more spatially reliable, rather than only sharper on average. To this end, we propose a reliability-guided diffusion framework (ReDiff) with two complementary inference-time mechanisms. First, a reliability-guided sampling strategy attenuates unstable reverse-diffusion updates in regions with weak low-field support. Second, an uncertainty-aware candidate selection scheme aggregates multiple stochastic reconstructions according to spatial consensus and predictive uncertainty. Beyond aggregate image quality, we test whether the uncertainty is itself a usable reliability signal. Experiments on paired 64mT$\rightarrow$3T MRI datasets show that ReDiff attains the lowest LPIPS across three contrasts and two datasets while remaining competitive on PSNR and SSIM, and downstream segmentation analysis indicates better preservation of anatomical structure.

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.