Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2-weighted images (T2WIs), is time-consuming and prone to be with motion artifacts. It negatively impacts subsequent multi-modal image analysis. To address this issue, we propose an end-to-end deep learning framework that utilizes T1-weighted images (T1WIs) as auxiliary modalities to expedite T2WIs' acquisitions. While image pre-processing is capable of mitigating misalignment, improper parameter selection leads to adverse pre-processing effects, requiring iterative experimentation and adjustment. To overcome this shortage, we employ Optimal Transport (OT) to synthesize T2WIs by aligning T1WIs and performing cross-modal synthesis, effectively mitigating spatial misalignment effects. Furthermore, we adopt an alternating iteration framework between the reconstruction task and the cross-modal synthesis task to optimize the final results. Then, we prove that the reconstructed T2WIs and the synthetic T2WIs become closer on the T2 image manifold with iterations increasing, and further illustrate that the improved reconstruction result enhances the synthesis process, whereas the enhanced synthesis result improves the reconstruction process. Finally, experimental results from FastMRI and internal datasets confirm the effectiveness of our method, demonstrating significant improvements in image reconstruction quality even at low sampling rates.

翻译：多模态磁共振成像（MRI）在临床医学综合疾病诊断中发挥着关键作用。然而，某些模态（如T2加权图像，T2WIs）的采集耗时较长且易产生运动伪影，这会对后续多模态图像分析产生负面影响。为解决该问题，我们提出一种端到端深度学习框架，利用T1加权图像（T1WIs）作为辅助模态加速T2WIs的采集。尽管图像预处理能够缓解配准误差，但不当的参数选择会导致预处理效果不理想，需要反复实验和调整。为克服这一不足，我们采用最优传输（OT）通过对齐T1WIs并进行跨模态合成来生成T2WIs，有效缓解空间配准误差的影响。此外，我们采用重建任务与跨模态合成任务交替迭代的框架以优化最终结果。我们证明，随着迭代次数的增加，重建的T2WIs与合成的T2WIs在T2图像流形上逐渐接近，并进一步阐明：改进的重建结果增强了合成过程，而增强的合成结果又改进了重建过程。最后，FastMRI和内部数据集的实验结果验证了该方法的有效性，表明即使在低采样率下，该方法也能显著提升图像重建质量。