Dynamic contrast enhanced MRI has benefitted substantially from developments in sparse sampling and constrained reconstruction. Thus far, temporal constraints have proven to be the most powerful. In this work, we explore the use of temporal dictionaries that are learned from a clinical database. We demonstrate that this method provides improved reconstruction quality compared to state-of-the-art TK-model-based constraints or low-rank constraints. The inclusion of spatial information while constructing dictionaries is also explored.
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