Abstract #4169

Small-patch CNN and random forest to model and quantify the lenticulostriate artery from 7T time-of-flight MR angiography

Zhixin Li^1,2,3, Dongbiao Sun^1,2,3, Yue Wu^1,2,3, Chen Ling^4,5, Jing An⁶, Yun Yuan^4,5, Zhaoxia Wang^4,5, Rong Xue^1,2,3, Yan Zhuo^1,2,3, and Zihao Zhang^1,2,3

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, Peking University First Hospital, Beijing, 100034, China, Beijing, China, ⁵Beijing Key Laboratory of Neurovascular Disease Discovery, Peking University First Hospital, Beijing, 100034, China, Beijing, China, ⁶Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China, Shenzhen, China

Synopsis

Time-of-flight MR angiography at 7T allowed noninvasive visualization of the lenticulostriate artery (LSA). However, vasculature modeling of LSA remained challenging due to limited signal-to-noise ratio and pulsation artifact. In this study, we introduced an automated vascular segmentation and tracing method based on small-patch convolutional neural network (CNN), random forest, and multiple filtering. This method outperformed existing U-NET based methods and found radius changes of LSA branches in patients with cerebral small vessel diseases (cSVD). The automated quantification of LSA vasculatures will potentially facilitate diagnosis and clinical studies of cSVD.

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