Abstract #3269

Automatic segmentation of arterial vessel wall on undersampled MR image using deep learning

Shuai Shen^1,2,3,4, Xiong Yang⁵, Jin Fang⁶, Guihua Jiang⁶, Shuheng Zhang⁵, Yanqun Teng⁵, Xiaomin Ren⁵, Lele Zhao⁵, Jiayu Zhu⁵, Qiang He⁵, Hairong Zheng^1,3,4, Xin Liu^1,3,4, and Na Zhang^1,3,4

¹Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, shenzhen, China, ²College of Software, Xinjiang University, Urumqi,, China, ³Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, shenzhen, China, ⁴CAS key laboratory of health informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, shenzhen, China, ⁵Shanghai United Imaging Healthcare Co., Ltd., shanghai, China, ⁶Department of Radiology, Guangdong Second Provincial General Hospital, guangdong, China

A total of 124 patients were included in this study. We used U-net neural network architecture to segment the arterial vessel wall on original acquired MR vessel wall images and the corresponding images reconstructed from undersampled K-space data. The Dice coefficients based on the original K-space data, the K-space data with a sampling rate of 7.7%, and K-space data with a sampling rate of 1.9% were 88.66%, 88.19%, and 87.66%, respectively. The effectiveness of arterial vessel wall segmentation on undersampled images using U-net network was verified. The result demonstrated the potential to improve the acceleration performance of MR imaging.

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