Purpose: To develop an efficient dual-domain reconstruction framework for
multi-contrast MRI, with the focus on minimising cross-contrast misalignment in
both the image and the frequency domains to enhance optimisation. Theory and
Methods: Our proposed framework, based on deep learning, facilitates the
optimisation for under-sampled target contrast using fully-sampled reference
contrast that is quicker to acquire. The method consists of three key steps: 1)
Learning to synthesise data resembling the target contrast from the reference
contrast; 2) Registering the multi-contrast data to reduce inter-scan motion;
and 3) Utilising the registered data for reconstructing the target contrast.
These steps involve learning in both domains with regularisation applied to
ensure their consistency. We also compare the reconstruction performance with
existing deep learning-based methods using a dataset of brain MRI scans.
Results: Extensive experiments demonstrate the superiority of our proposed
framework, for up to an 8-fold acceleration rate, compared to state-of-the-art
algorithms. Comprehensive analysis and ablation studies further present the
effectiveness of the proposed components. Conclusion:Our dual-domain framework
offers a promising approach to multi-contrast MRI reconstruction. It can also
be integrated with existing methods to further enhance the reconstruction.

通过深度学习，开发了一种高效的双域重建框架用于多对比度磁共振成像，重点在于最小化图像和频率域中的对比度错位以增强优化，实验证明该框架相较于现有算法在加速倍数高达 8 倍情况下具备较高的重建优势。