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Magnetic resonance imaging (MRI) is known to have reduced signal-to-noiseratios (SNR) at lower field strengths, leading to signal degradation whenproducing a low-field MRI image from a high-field one. Therefore,reconstructing a high-field-like image from a low-field MRI is a complexproblem due to the ill-posed nature of the task. Additionally, obtaining pairedlow-field and high-field MR images is often not practical. We theoreticallyuncovered that the combination of these challenges renders conventional deeplearning methods that directly learn the mapping from a low-field MR image to ahigh-field MR image unsuitable. To overcome these challenges, we introduce anovel meta-learning approach that employs a teacher-student mechanism. Firstly,an optimal-transport-driven teacher learns the degradation process fromhigh-field to low-field MR images and generates pseudo-paired high-field andlow-field MRI images. Then, a score-based student solves the inverse problem ofreconstructing a high-field-like MR image from a low-field MRI within theframework of iterative regularization, by learning the joint distribution ofpseudo-paired images to act as a regularizer. Experimental results on reallow-field MRI data demonstrate that our proposed method outperformsstate-of-the-art unpaired learning methods.

Meta-Learning Enabled Score-Based Generative Model for 1.5T-Like Image Reconstruction from 0.5T MRI