I. Introduction
Magnetic resonance imaging (MRI) is commonly used clinically for its flexible contrast. The major shortcoming of MRI is its long scan time, especially for volumetric images. Undersampling is often necessary to reduce scan time and cope with motion, but reconstructing undersampled MRI is solving an undetermined system and conventional reconstruction methods such as compressed sensing (CS) are time intensive. Recently, data-driven methods based on neural networks (NNs) are adopted to reconstruct MR images with rapid reconstruction speed. However, most of these models require supervised training on a large and specific set of labels, that are fully-sampled high-quality images. We refer to the label image used for training supervision as ‘label’ in this article.