title: Novel methodology for 3D reconstruction of carotid arteries and plaque characterization based upon magnetic resonance imaging carotid angiography data.
abstract: In this study, we present a novel methodology that allows reliable segmentation of the magnetic resonance images (MRIs) for accurate fully automated three-dimensional (3D) reconstruction of the carotid arteries and semiautomated characterization of plaque type. Our approach uses active contours to detect the luminal borders in the time-of-flight images and the outer vessel wall borders in the T(1)-weighted images. The methodology incorporates the connecting components theory for the automated identification of the bifurcation region and a knowledge-based algorithm for the accurate characterization of the plaque components. The proposed segmentation method was validated in randomly selected MRI frames analyzed offline by two expert observers. The interobserver variability of the method for the lumen and outer vessel wall was -1.60%±6.70% and 0.56%±6.28%, respectively, while the Williams Index for all metrics was close to unity. The methodology implemented to identify the composition of the plaque was also validated in 591 images acquired from 24 patients. The obtained Cohen's k was 0.68 (0.60-0.76) for lipid plaques, while the time needed to process an MRI sequence for 3D reconstruction was only 30 s. The obtained results indicate that the proposed methodology allows reliable and automated detection of the luminal and vessel wall borders and fast and accurate characterization of plaque type in carotid MRI sequences. These features render the currently presented methodology a useful tool in the clinical and research arena.
authors.affiliation: Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science, University of Ioannina, Ioannina, Greece.
authors.forename: Antonis I
authors.initials: AI
authors.lastname: Sakellarios
authors.forename: Kostas
authors.initials: K
authors.lastname: Stefanou
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Siogkas
authors.forename: Vasilis D
authors.initials: VD
authors.lastname: Tsakanikas
authors.forename: Christos V
authors.initials: CV
authors.lastname: Bourantas
authors.forename: Lambros
authors.initials: L
authors.lastname: Athanasiou
authors.forename: Themis P
authors.initials: TP
authors.lastname: Exarchos
authors.forename: Evangelos
authors.initials: E
authors.lastname: Fotiou
authors.forename: Katerina K
authors.initials: KK
authors.lastname: Naka
authors.forename: Michail I
authors.initials: MI
authors.lastname: Papafaklis
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Patterson
authors.forename: Victoria E L
authors.initials: VE
authors.lastname: Young
authors.forename: Jonathan H
authors.initials: JH
authors.lastname: Gillard
authors.forename: Lampros K
authors.initials: LK
authors.lastname: Michalis
authors.forename: Dimitrios I
authors.initials: DI
authors.lastname: Fotiadis
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D002339:Carotid Arteries / Q000473:pathology*; D016893:Carotid Stenosis / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity