Magnetic resonance imaging (MRI) is known to have reduced signal-to-noise ratios (SNR) at lower field strengths, leading to signal degradation when producing a low-field MRI image from a high-field one. Therefore, reconstructing a high-field-like image from a low-field MRI is a complex problem due to the ill-posed nature of the task. Additionally, obtaining paired low-field and high-field MR images is often not practical. We theoretically uncovered that the combination of these challenges renders conventional deep learning methods that directly learn the mapping from a low-field MR image to a high-field MR image unsuitable. To overcome these challenges, we introduce a novel meta-learning approach that employs a teacher-student mechanism. Firstly, an optimal-transport-driven teacher learns the degradation process from high-field to low-field MR images and generates pseudo-paired high-field and low-field MRI images. Then, a score-based student solves the inverse problem of reconstructing a high-field-like MR image from a low-field MRI within the framework of iterative regularization, by learning the joint distribution of pseudo-paired images to act as a regularizer. Experimental results on real low-field MRI data demonstrate that our proposed method outperforms state-of-the-art unpaired learning methods.

翻译：磁共振成像（MRI）在低场强下信噪比（SNR）降低，导致从高场强MRI生成低场强MRI图像时信号退化。因此，从低场强MRI重建类似高场强图像是一个复杂问题，因为该任务本质上具有病态性。此外，获取成对的低场强与高场强MRI图像通常不切实际。我们从理论上揭示，这些挑战的组合使得直接学习从低场强MRI图像到高场强MRI图像映射的传统深度学习方法不再适用。为克服这些挑战，我们提出了一种新颖的元学习方法，采用师生机制。首先，基于最优传输的教师网络学习从高场强到低场强MRI图像的退化过程，并生成伪配对的高场强与低场强MRI图像。随后，基于分数的学生网络在迭代正则化框架内，通过学习伪配对图像的联合分布作为正则化项，求解从低场强MRI重建类似高场强MRI图像的反问题。在真实低场强MRI数据上的实验结果表明，我们提出的方法优于当前最先进的非配对学习方法。