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Abstract #3481

AUTO-DCE-MRI: A Deep-Learning Augmented Liver Imaging Framework for Fully-Automated Multiphase Assessment and Perfusion Mapping

Li Feng1, Fang Liu2, Henry Rusinek1, Bari Dane1, Henry Brody1, Teodora Chitiboi1, Daniel K Sodickson1, Ricardo Otazo1, and Hersh Chandarana1

1Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, United States, 2Department of Radiology, University of Wisconsin School of Medicine, Madsion, WI, United States

This work proposes and tests a novel dynamic contrast-enhanced liver MRI framework called AUTO-DCE-MRI, which allows for simultaneous multiphase assessment and automated perfusion mapping from a single continuous free-breathing data acquisition. A deep convolutional neural network is trained to automatically select the abdominal aorta and the main portal vein. For low temporal-resolution multiphase assessment, the contrast bolus information is extracted from the aorta to guide image reconstruction of desired contrast phases. For high temporal-resolution perfusion analysis, the arterial/venous input functions are generated from the automatically selected regions in the aorta and main portal vein for pharmacokinetic modeling.

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