The goal of this work is to reconstruct highly undersampled frames from dynamic Non-Cartesian acquisitions using deep learning. In this setting, supervised data is difficult to obtain due to organ motion necessitating self-supervision. The challenge is that acquisitions are often so data-starved that self-supervised reconstructions that use only spatial correlations fail to recover fine details. Here, we leverage correlations across time frames, and show that even when data is misaligned, it is possible to reconstruct highly accelerated frames using self-supervised methods. We demonstrate the feasibility of this technique by reconstructing end-inspiratory phase images from respiratory binned Pulmonary UTE acquisitions.
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