WCCAN: Windowed Cross-contrast Attention Network for Multi-contrast Brain MR Image Super-resolution

Purpose: Most existing multi-contrast MRI super-resolution (MCMSR) methods rely on spatial-domain fusion and global attention, overlooking explicit high-frequency (HF) priors while incurring high computational costs. This work addresses these limitations through a general reference-guided MCMSR framework designed for low computational cost, applicable to any contrast pairing rather than a fixed clinical protocol. Methods: We introduce a wavelet-guided HF prior modeling block for directional-based decomposition and bounded nonlinear enhancement, enabling precise extraction and controlled amplification of anatomical details from both reference and target contrasts. We further introduce a triple cross-contrast fusion module, based on a windowed cross-contrast attention module, to efficiently transfer high-frequency information between contrasts and reduce computational complexity compared to global attention schemes. Additionally, to reduce feature differences across contrasts, a consistent feature fusion module with selective spatial adaptive modulation is incorporated. Results: Extensive experiments on IXI and M4Raw datasets demonstrate that our proposed windowed cross-contrast attention network (WCCAN) framework consistently outperforms state-of-the-art single and multi-contrast MRI SR methods in terms of quantitative accuracy and visual fidelity. In addition, the WCCAN model achieves lower computational complexity and faster inference time compared to the other MCMSR methods. Conclusion: The proposed WCCAN framework provides an efficient and accurate solution for MCMSR, demonstrating superior reconstruction quality with reduced computational cost compared to existing methods.