Dynamic MRI holds high potential for real-time imaging of upper airway, which can provide insights into questions of speech science and also have important clinical applications. However, speech imaging places increased demands on spatial and temporal resolution, necessitating image reconstruction from severely undersampled data. Previously reported methods use low-rank constraints with spiral navigators to enable temporal basis estimation, otherwise infeasible with standard learning methods. We propose an alternative solution based on a novel concept of progressive learning, which does not require separate specialized pulse sequences for navigator acquisitions, while providing high 7.4 ms temporal and 1.25x1.25x8 mm spatial resolution.
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