Spatial–Frequency-Guided Progressive Flow Matching for Accurate 3T-to-7T MRI Synthesis

Synthesizing 7T-like images from 3T MRI enhances anatomical clarity and expands the utility of existing 3T datasets. However, since fine tissue details and high-frequency noise occupy similar spectral bands, unconstrained high-frequency enhancement tends to amplify noise and introduce artifacts, whereas excessive smoothing obscures delicate boundaries. Consequently, leveraging source image structures to constrain local detail enhancement remains a major challenge in cross-field synthesis. To address this, we propose a spatio-frequency-guided progressive conditional flow matching framework. By integrating spatial and frequency-domain representations, our approach employs a multi-band generation strategy to schedule updates across distinct frequency bands. This conditions local detail refinement directly on the source structure, enabling adaptive, sequential enhancement. Experiments on paired datasets demonstrate that our method achieves superior synthesize performance in just 5 inference steps, significantly improving pixel-level reconstruction quality and reducing high-frequency error relative to the standard flow-matching baseline.