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Abstract #0998

k-t SANTIS: Subspace Augmented Neural neTwork with Incoherent Sampling for dynamic image reconstruction

Fang Liu1,2 and Li Feng3
1Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, 2Radiology, University of Wisconsin-Madison, Madison, WI, United States, 3Biomedical Engineering and Imaging Institute and Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States

A novel deep learning-based dynamic image reconstruction technique called k-t SANTIS (Subspace Augmented Neural neTwork with Incoherent Sampling) is presented in this study. Different from prior deep learning-based reconstruction approaches that rely primarily on data-driven learning, k-t SANTIS incorporates a low-rank subspace model into the deep-learning reconstruction architecture, which is implemented by adding a subspace layer to enforce an explicit subspace constraint during network training. k-t SANTIS represents a new deep image reconstruction framework with hybrid data-driven and physics-informing learning, taking additional prior knowledge available in the dataset into consideration during the training process to achieve better reconstruction performance.

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