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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