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具有至关重要的作用。然而，某些模态（如T2加权图像）的采集耗时较长且易产生运动伪影，这对后续的多模态图像分析产生了负面影响。为解决这一问题，我们提出了一种端到端的深度学习框架，利用T1加权图像作为辅助模态来加速T2WI的采集过程。虽然图像预处理能够缓解空间未对准问题，但参数选择不当会导致预处理效果不佳，需要反复实验和调整。为克服这一不足，我们采用最优传输（Optimal Transport, OT）技术，通过对齐T1WI并进行跨模态合成来生成T2WI，有效减轻了空间未对准效应。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来优化最终结果。随后，我们证明随着迭代次数增加，重建的T2WI与合成的T2WI在T2图像流形上逐渐接近，并进一步说明改进的重建结果能增强合成过程，而增强的合成结果同样能改善重建过程。最后，在FastMRI数据集与内部数据集上的实验结果证实了该方法的有效性，表明即使在低采样率下，图像重建质量也能得到显著提升。