MArFE: Multi-Contrast MRI Arbitrary Scale Super-Resolution with Fourier Enhancement

High-resolution multi-contrast MRI is essential for capturingrich anatomical structures and supports more accurate disease diagnosisthrough the reference images from another contrast. It often suffers fromlimited spatial resolution due to acquisition constraints, and requestsexploring arbitrary-scale reconstruction beyond fixed scale factors. How-ever, related research exists two following issues: 1) Implicit neural rep-resentations (INR) as mainstream methods are prone to spectral bias,which limits their ability to recover high-frequency details; 2) Multi-contrast MRI is often used as the effective prior, but lacks the targetednetwork design to further merge INR positional information. To solvethese problems, we propose a Fourier-enhanced implicit framework forarbitrary-scale multi-contrast MRI super-resolution (MArFE). First, theFourier reparameterization module (FRM) is introduced to character-ize frequency features through fixed Fourier basis decomposition. Then,Fourier neural operators can alleviate frequency degradation by the non-local Galerkin-type linear attention (NLGLA) based on kernel integralmechanisms. Additionally, we design a hybrid loss that jointly supervisesboth spatial and frequency domains to improve high-frequency texturereconstruction. Extensive experiments on two public multi-contrast MRIdatasets demonstrate that MArFE outperforms existing methods undervarious scale factors with superior visual performances and quantitativemetrics. These results highlight the clinical potential of MArFE in en-hancing MRI quality across multi-contrast sequences. The project codecan be found at https://github.com/zhiwen-shi/MArFE.