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Journal of Medical Imaging

ART 3.5D: an algorithm to label arteries and veins from three-dimensional angiography
Author(s): Beatrice Barra; Elena De Momi; Giancarlo Ferrigno; Guglielmo Pero; Francesco Cardinale; Giuseppe Baselli
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Paper 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.

Paper Details

Date Published: 29 November 2016
PDF: 10 pages
J. Med. Imag. 3(4) 044002 doi: 10.1117/1.JMI.3.4.044002
Published in: Journal of Medical Imaging Volume 3, Issue 4
Show Author Affiliations
Beatrice Barra, Politecnico di Milano (Italy)
Elena De Momi, Politecnico di Milano (Italy)
Giancarlo Ferrigno, Politecnico di Milano (Italy)
Guglielmo Pero, Grande Ospedale Metropolitano Niguarda (Italy)
Francesco Cardinale, Grande Ospedale Metropolitano Niguarda (Italy)
Giuseppe Baselli, Politecnico di Milano (Italy)

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