The introduction of capillary structures in 4D simulated vascular tree for ART 3.5D algorithm further validation

Abstract

Several neurosurgical procedures, such as Artero Venous Malformations (AVMs), aneurysm embolizations and StereoElectroEncephaloGraphy (SEEG) require accurate reconstruction of the cerebral vascular tree, as well as the classification of arteries and veins, in order to increase the safety of the intervention. Segmentation of arteries and veins from 4D CT perfusion scans has already been proposed in different studies. Nonetheless, such procedures require long acquisition protocols and the radiation dose given to the patient is not negligible. Hence, space is open to approaches attempting to recover the dynamic information from standard Contrast Enhanced Cone Beam Computed Tomography (CE-CBCT) scans. The algorithm proposed by our team is called ART 3.5 D. It is a novel algorithm based on the postprocessing of both the angiogram and the raw data of a standard Digital Subtraction Angiography from a CBCT (DSA-CBCT) allowing arteries and veins segmentation and labeling without requiring any additional radiation exposure for the patient and neither lowering the resolution. In addition, while in previous versions of the algorithm just the distinction of arteries and veins was considered, here the capillary phase simulation and identification is introduced, in order to increase further information useful for more precise vasculature segmentation.

Automatic artery/vein classification in 2D-DSA images of stroke patients

“…The filter uses eigenvectors of the Hessian matrix, which describes the second order derivatives of the image, 9 to compute the likeliness of a pixel to be a vessel. 10 The values given by the Frangi filter were binarized (value 0 for background, value 1 for vessels) and used to create a vessel mask. This mask served as an overlay for the DSA images.…”