MiCo: Microstructure-Consistent Flow Matching for Diffusion MRI Angular Super-Resolution

Diffusion-weighted MRI (dMRI) requires densely sampling $q$-space across many directions and shells, leading to long scan times that limit clinical use. Angular super-resolution (ASR) recovers a dense signal from a sparse acquisition, enabling substantially shorter scans. We propose a physics-guided flow matching framework for ASR built on a basic fact about dMRI: under the cumulant expansion, the diffusion signal naturally splits into a known linear function of the $q$-vectors plus a residual carrying higher-order angular detail. We exploit this twice. First, we initialize the flow from a closed-form dense DTI estimate, recasting ASR as residual flow matching from a physically meaningful starting point rather than uninformative noise. Second, we impose a microstructure-consistency loss, rooted in the physical principle that any two diffusion-weighted signals from the same voxel must be explained by a single underlying set of microstructural parameters. Together, these designs anchor predictions wherever the acquisition is informative while leaving the learned prior to model crossing-fiber and higher-order angular detail. On the Human Connectome Project (HCP-YA) and UK Biobank, our method matches or surpasses analytical baselines and recent deep learning methods on signal reconstruction, kurtosis scalar maps, and fiber-orientation reconstruction under aggressive subsampling.