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

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.