Abstract #3069

Automated modeling and morphologic analysis of deep medullary veins at 7T MRI in patients with cognitive impairment

Zhixin Li^1,2,3, Jingyuan Zhang^1,2,3, Li Liang ⁴, Jing An⁵, Hairong Qian⁴, Rong Xue^1,2,3, Yan Zhuo^1,2,3, and Zihao Zhang^1,2,6

¹State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, Beijing, China, ²University of Chinese Academy of Sciences, Beijing, China, Beijing, China, ³The Innovation Center of Excellence on Brain Science, Chinese Academy of Sciences, Beijing, China, Beijing, China, ⁴Department of Neurology, the Sixth Medical Center, Chinese PLA General Hospital, Beijing, China, ⁵Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China, Shenzhen, China, ⁶Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China, Hefei, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, ModellingDeep medullary veins (DMVs) support cerebral venous drainage. They may display abnormal changes in patients with cognitive impairment. They can be visualized by multi-echo gradient echo imaging at 7T. This study proposed a segmentation and tracking method based on deep learning and shortest-path optimization. It automatically quantified the morphologic parameters of DMVs from the vascular model. These characteristics of DMVs correlated with the patients’ cognitive scores, and might reflect the pathology of vascular lesions in cognitive impairment.

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