title: ART 3.5D: an algorithm to label arteries and veins from three-dimensional angiography.
abstract: 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.
authors.affiliation: Politecnico di Milano , Electronic Information and Bioengineering Department, Piazza Leonardo da Vinci, 32, Milano 20133, Italy.
authors.forename: Beatrice
authors.initials: B
authors.lastname: Barra
authors.affiliation: Politecnico di Milano , Electronic Information and Bioengineering Department, Piazza Leonardo da Vinci, 32, Milano 20133, Italy.
authors.forename: Elena
authors.initials: E
authors.lastname: De Momi
authors.affiliation: Politecnico di Milano , Electronic Information and Bioengineering Department, Piazza Leonardo da Vinci, 32, Milano 20133, Italy.
authors.forename: Giancarlo
authors.initials: G
authors.lastname: Ferrigno
authors.affiliation: Niguarda Hospital, "Claudio Munari" Center for Epilepsy Surgery, Piazza Ospedale Maggiore 3, Milano 20162, Italy; Niguarda Hospital, Department of Neuroradiology, Piazza Ospedale Maggiore, 3, Milano 20162, Italy.
authors.forename: Guglielmo
authors.initials: G
authors.lastname: Pero
authors.affiliation: Niguarda Hospital, "Claudio Munari" Center for Epilepsy Surgery, Piazza Ospedale Maggiore 3, Milano 20162, Italy; Niguarda Hospital, Department of Neuroradiology, Piazza Ospedale Maggiore, 3, Milano 20162, Italy.
authors.forename: Francesco
authors.initials: F
authors.lastname: Cardinale
authors.affiliation: Politecnico di Milano , Electronic Information and Bioengineering Department, Piazza Leonardo da Vinci, 32, Milano 20133, Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Baselli
keywords: brain angiography; classification of veins and arteries; contrast-enhanced cone-beam CT