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Abstract #4311

Vascular Heterogeneity Model-based Deep Learning Reconstruction for High-Definition Dynamic Contrast Enhanced MRI

Nhan Duc Nguyen1, Joon Sik Park1, Seung Hong Choi2, Roh-Eul Yoo2, and Jaeseok Park1,3
1Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea, Republic of, 2Department of Radiology, Seoul National University Hospital, Seoul, Korea, Republic of, 3Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea, Republic of


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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