title: Automatic vessel wall contour detection and quantification of wall thickness in in-vivo MR images of the human aorta.
abstract: OBJECTIVE
  To develop an automated technique to trace the contours of the lumen and outer boundary of the aortic wall, and measure aortic wall thickness in axial MR images.
  
  
  METHODS
  The algorithm uses prior knowledge of vessel wall morphology. A geometrical model (ellipse) is deformed, translated and rotated to obtain a rough approximation of the contours. Model-matching is based on image gradient measurements. To enhance edges, the images were preprocessed using gray-level stretching. Refinement is performed by means of dynamic programming. Wall thickness is computed by measuring the distance between inner and outer contour of the aortic wall.
  
  
  RESULTS
  The algorithm has been tested on high-resolution axial MR images from 28 human subjects of the descending thoracic aorta. The results demonstrate: High correspondence between automatic and manual area measurements: lumen (r = 0.99), outer (r = 0.96), and wall thickness (r = 0.85).
  
  
  CONCLUSIONS
  Though further optimization is required, our algorithm is a powerful tool to automatically draw the boundaries of the aortic wall and measure aortic wall thickness in aortic wall devoid of major lesions. J. Magn. Reson. Imaging 2006. (c) 2006 Wiley-Liss, Inc.
authors.affiliation: Department of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, the Netherlands. I.M.Adame@lumc.nl
authors.forename: Isabel M
authors.initials: IM
authors.lastname: Adame
authors.forename: Rob J
authors.initials: RJ
authors.lastname: van der Geest
authors.forename: David A
authors.initials: DA
authors.lastname: Bluemke
authors.forename: João A C
authors.initials: JA
authors.lastname: Lima
authors.forename: Johan H C
authors.initials: JH
authors.lastname: Reiber
authors.forename: Boudewijn P F
authors.initials: BP
authors.lastname: Lelieveldt
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001011:Aorta / Q000473:pathology*; D050197:Atherosclerosis / Q000473:pathology; D001331:Automation; D004730:Endothelium, Vascular / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D008962:Models, Theoretical; D012044:Regression Analysis; D015203:Reproducibility of Results; D013997:Time Factors