Keywords: Image acquisition: Reconstruction
In recent years, deep learning has made significant advancements in MRI reconstruction. However, conventional methods often require full-sampled MRI data, presenting challenges in data acquisition. Consequently, unsupervised learning methods have garnered attention. This discussion delves into various unsupervised deep learning approaches for MRI reconstruction, including unpaired, self-supervised, and zero-shot (untrained) learning. Moreover, we foresee a promising future for unsupervised learning in MRI reconstruction, particularly in collaboration with large-scale foundation models, thereby facilitating further progress in MRI technology.