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Abstract #2735

U-net: Convolutional Networks for Carotid Artery Wall Segmentation in Simultaneous Non-Contrast Angiography and intra-Plaque hemorrhage (SNAP) imaging

Mingquan LIN1, Bernard Chiu 1, Qiang Zhang2, Huiyu Qiao2, Jiaqi Dou3, Binbin Sui4, Shuo Chen2, Xihai Zhao2, Zhensen Chen2, and Huijun Chen2

1Department of Electronic Engineering, City University of Hong Kong, Hong Kong, Hong Kong, 2Center of Biomedical Imaging Research, Tsinghua University, Beijing, China, 3Beijing Jiaotong University, Beijing, China, 4Beijing Tian Tan Hospital, Beijing, China

The purpose of this study is to develop a U-net deep learning model to segment the carotid artery wall using a single 3D Simultaneous Non-Contrast Angiography and intra-Plaque hemorrhage (SNAP) acquisition. Using U-net convolutional Networks can achieve acceptable dice similarity coefficient. In addition, by adding more SNAP imaging such as phase-corrected images (CR), the magnitude of REF and the real part of IR as well as excluded the slice that cannot register and has low image quality may further improve the result.

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