Self-supervised multi-contrast MRI reconstruction via mask-guided frequency-domain fusion.

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Abstract

Multi-contrast magnetic resonance imaging (MC-MRI) enables detailed tissue characterization by acquiring multiple contrast-weighted images from the same anatomical region. Redundancy across contrasts can improve reconstruction from under-sampled data. However, conventional deep learning approaches rely on fully-sampled, contrast-aligned datasets, which require long acquisition times. Extended scanning durations not only increase patient burden but may also introduce physiological risks, limiting the practical adoption of accelerated MC-MRI in clinical settings. This study introduces Self-MF, a self-supervised MC-MRI reconstruction framework that leverages contrast redundancy without requiring fully sampled target-contrast labels. The framework aims to provide a high-fidelity, scalable solution for reconstructing under-sampled multi-contrast images while preserving anatomical details and contrast fidelity. Self-MF implements a contrast-aware self-supervised learning strategy that randomly partitions the acquired target k-space into input and held-out subsets, allowing the model to learn joint anatomical priors from under-sampled data without fully sampled target-contrast labels. A mask-guided frequency-domain conditioning module constructs reference-conditioned auxiliary frequency channels from auxiliary contrasts and acquired target measurements, enhancing the recovery of fine structural features during score-based reconstruction. Experiments were conducted on public datasets (IXI, fastMRI) and a clinical multi-coil dataset (SIAT), spanning multiple acceleration factors and undersampling patterns to assess robustness and generalizability. Quantitative and qualitative evaluations demonstrate that Self-MF outperforms existing representative reconstruction methods across different contrasts, datasets, and acceleration factors. On the SIAT multi-contrast dataset, Self-MF achieved PSNR/SSIM values of 35.16/0.8870, 33.46/0.9054, and 35.31/0.9187 for T1WI, T2WI, and PDWI reconstruction under 2D random undersampling at R = 4, respectively. On the fastMRI brain dataset, Self-MF achieved 41.15 dB/0.9637 and 35.02 dB/0.8814 for T2WI reconstruction under R = 4 and R = 8, respectively, consistently outperforming the compared classical, learning-based, and diffusion-based baselines. Self-MF provides an effective, self-supervised solution for high-fidelity MC-MRI reconstruction without fully sampled target-contrast labels. By combining contrast-aware self-supervision, frequency-domain conditioning, and diffusion-based reconstruction, the framework achieves superior image quality and robust anatomical preservation, representing a significant advancement for multi-contrast MRI in both research and clinical practice.

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