Radiology Commons^™

Automated Assessment Of Acute Aortic Dissection On Thoracic Ct Using Deep Learning, Varun Singh, Richard Gorniak, Md, Adam Flanders, Md, Paras Lakhani, Md

Department of Radiology Posters

Purpose

To assess the efficacy of deep convolutional neural networks (DCNNs ) in differentiating acute aortic dissections from non-dissected aortas on thoracic CT.

Li-Rads: A Conceptual And Historical Review From Its Beginning To Its Recent Integration Into Aasld Clinical Practice Guidance, Khaled M. Elsayes, Ania Z. Kielar, Victoria Chernyak, Ali Morshid, Alessandro Furlan, William R. Masch, Robert M. Marks, Aya Kamaya, Richard K. G. Do, Yuko Kono, Kathryn J. Fowler, An Tang, Mustafa R. Bashir, Elizabeth M. Hecht, Kedar Jambhekar, Andrej Lyshchik, Shuchi K. Rodgers, Jay P. Heiken, Marc Kohli, David T. Fetzer, Stephanie R. Wilson, Zahra Kassam, Mishal Mendiratta-Lala, Amit G. Singal, Christopher S. Lim, Irene Cruite, James Lee, Ryan Ash, Donald G. Mitchell, Matthew D. F. Mcinnes

Radiology Faculty Publications

The Liver Imaging Reporting and Data System (LI-RADS^®) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver observations in individuals at high risk for hepatocellular carcinoma (HCC). LI-RADS is supported and endorsed by the American College of Radiology (ACR). Upon its initial release in 2011, LI-RADS applied only to liver observations identified at CT or MRI. It has since been refined and expanded over multiple updates to now also address ultrasound-based surveillance, contrast-enhanced ultrasound for HCC diagnosis, and CT/MRI for assessing treatment response after locoregional therapy. The LI-RADS 2018 version was …