While the globally low-rank (GLR) model has been demonstrated to be effective in representing global contrast change, it is expected to be less effective for spatially localized signal dynamics. In this work, we propose an improved reconstruction framework, which extends GRASP-Pro using a locally low-rank (LLR) model to represent spatially localized dynamics based on clusters or patches information. This approach has been tested in multiple DCE applications including both cancer and healthy subjects. In addition, we propose an anatomical cluster-based reconstruction approach for brain DCE-MRI.
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