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 (DSACBCT)
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.
Preoperative three-dimensional (3-D) visualization of brain vasculature by digital subtraction angiography from computerized tomography (CT) in neurosurgery is gaining more and more importance, since vessels are the primary landmarks both for organs at risk and for navigation. Surgical embolization of cerebral aneurysms and arteriovenous malformations, epilepsy surgery, and stereoelectroencephalography are a few examples. Contrast-enhanced cone-beam computed tomography (CE-CBCT) represents a powerful facility, since it is capable of acquiring images in the operation room, shortly before surgery. However, standard 3-D reconstructions do not provide a direct distinction between arteries and veins, which is of utmost importance and is left to the surgeon’s inference so far. Pioneering attempts by true four-dimensional (4-D) CT perfusion scans were already described, though at the expense of longer acquisition protocols, higher dosages, and sensible resolution losses. Hence, space is open to approaches attempting to recover the contrast dynamics from standard CE-CBCT, on the basis of anomalies overlooked in the standard 3-D approach. This paper aims at presenting algebraic reconstruction technique (ART) 3.5D, a method that overcomes the clinical limitations of 4-D CT, from standard 3-D CE-CBCT scans. The strategy works on the 3-D angiography, previously segmented in the standard way, and reprocesses the dynamics hidden in the raw data to recover an approximate dynamics in each segmented voxel. Next, a classification algorithm labels the angiographic voxels and artery or vein. Numerical simulations were performed on a digital phantom of a simplified 3-D vasculature with contrast transit. CE-CBCT projections were simulated and used for ART 3.5D testing. We achieved up to 90% classification accuracy in simulations, proving the feasibility of the presented approach for dynamic information recovery for arteries and veins segmentation.