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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