Abstract #3023

An Integrated Framework for Whole Brain Arteries Modeling on Compressed Sensing TOF-MRA at 7T

Zhixin Li^1,2,3, Jinyuan Zhang^1,2,4, Jing An⁵, Rong Xue^1,2,4, Yan Zhuo^1,2,4, and Zihao Zhang^1,2,6

¹State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China, University of Chinese Academy of Sciences, Beijing, China, The Innovation Center of Excellence on Brain Science, 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, ⁴The Innovation Center of Excellence on Brain Science, Chinese Academy of Sciences, Beijing, China, Beijing, China, ⁵Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China., Beijing, China, ⁶Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China, Hefei, China

Synopsis

Keywords: Segmentation, Brain

Motivation: Whole-brain vasculature modeling remains challenging due to background variations and diverse vessel sizes.

Goal(s): We pursued an integrated framework for the accurate segmentation and tracking of vasculature in the whole brain.

Approach: Small-patch CNN (SP-CNN) and centerline-Dice nnU-Net (CDNN) were utilized for multiscale segmenting of vessels. A random forest and soft-skeletonization were applied for tracking. A novel rotation algorithm was used to calculate diameters accurately.

Results:

We accurately reconstructed the arteries from compressed sensing time-of-flight MR angiography (CS-TOF-MRA) and geometric parameters of full-size arteries were obtained.

Impact: Our work enables the segmentation of full-size-arteries in CS-TOF-MRA. As this method integrates algorithms for higher accuracy and robustness, it will be beneficial for clinical diagnosis.

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