Shuai Shen1,2,3,4, Xiong Yang5, Jin Fang6, Guihua Jiang6, Shuheng Zhang5, Yanqun Teng5, Xiaomin Ren5, Lele Zhao5, Jiayu Zhu5, Qiang He5, Hairong Zheng1,3,4, Xin Liu1,3,4, and Na Zhang1,3,4
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
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
In this study, we developed and evaluated a 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 obtained results for different groups were similar.
Figure
1 Representative original images and undersampled
images with different undersampled rate.
Figure
2 Representative images of the segmentation results
of different three group experiments.