To take characterization of various vascular contrast dynamics into account, in this work we propose a novel, vascular heterogeneity model based deep learning reconstruction from highly undersampled data for high-definition whole brain DCE MRI. To this end, we introduce a new, vascular contrast dynamics (VCD) weighted deep attention neural network (VACAN) consisting of: 1) a vascular adaptive attention 3D U-Net, 2) a multilayered non-negative matrix factorization (NMF) layer, and 3) a data consistency layer. Experimental studies are performed using highly undersampled patient data to validate the effectiveness of the proposed VACAN against conventional 3D U-Net.
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