The cerebral vasculature is a complex vessel network with high variations among human subjects. Although the coarse structure and spatial relationships of the main cerebrovascular branches are well known, not much knowledge about inter-individual vessel variability of humans at a finer level is available. The aim of this work is to present a probabilistic atlas of cerebral arterial vascular structures derived from 700 Time-of-Flight (TOF) magnetic resonance angiography (MRA) datasets of healthy subjects. Therefore, the cerebrovascular system was automatically segmented in each TOF datasets. In a following step, each TOF dataset and corresponding segmentation was registered to the MNI brain atlas. The registered datasets were then used for generation of a probabilistic cerebrovascular atlas. The generated atlas was evaluated with respect to three possible applications. The results suggest that the atlas is especially helpful to obtain knowledge about the cerebrovascular anatomy and its variations in terms of vessel occurrence probability. Furthermore, it appears useful for initialization of automatic cerebrovascular segmentation methods while an application for detection of vessel pathologies seems only feasible for large malformations.
The cerebral vasculature is a complex vessel network with high variations among human subjects. Although the coarse structure and spatial relationships of the main cerebrovascular branches are well known, not much knowledge about inter-individual vessel variability of humans at a finer level is available. The aim of this work is to present a probabilistic atlas of cerebral arterial vascular structures derived from 700 Time-of-Flight (TOF) magnetic resonance angiography (MRA) datasets of healthy subjects. Therefore, the cerebrovascular system was automatically segmented in each TOF datasets. In a following step, each TOF dataset and corresponding segmentation was registered to the MNI brain atlas. The registered datasets were then used for generation of a probabilistic cerebrovascular atlas. The generated atlas was evaluated with respect to three possible applications. The results suggest that the atlas is especially helpful to obtain knowledge about the cerebrovascular anatomy and its variations in terms of vessel occurrence probability. Furthermore, it appears useful for initialization of automatic cerebrovascular segmentation methods while an application for detection of vessel pathologies seems only feasible for large malformations.