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.

翻译：目的：开发一种高效的双域重建框架，用于多对比度MRI，重点是在图像域和频率域中最小化交叉对比度错位以增强优化。理论与方法：我们提出的基于深度学习的框架，利用采集速度更快的全采样参考对比度，促进欠采样目标对比度的优化。该方法包含三个关键步骤：1）学习从参考对比度合成类似目标对比度的数据；2）对多对比度数据进行配准以减少扫描间运动；3）利用配准后的数据重建目标对比度。这些步骤涉及在两个域中的学习，并通过施加正则化确保其一致性。我们还使用脑部MRI扫描数据集，将重建性能与现有的深度学习方法进行比较。结果：大量实验表明，与最先进算法相比，我们提出的框架在高达8倍加速率下均具有优越性。全面的分析和消融研究进一步展示了所提组件的有效性。结论：我们的双域框架为多对比度MRI重建提供了一种有前景的方法，它也可以与现有方法集成以进一步增强重建效果。