Automatic vessel wall contour detection and quantification of wall thickness in in-vivo MR images of the human aorta

Isabel M. Adame, Rob J. Van Der Geest, David A. Bluemke, Joã A.C. Lima, Johan H.C. Reiber, Boudewijn P.F. Lelieveldt

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Purpose: 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. Materials and 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). Conclusion: 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.

Original languageEnglish (US)
Pages (from-to)595-602
Number of pages8
JournalJournal of Magnetic Resonance Imaging
Issue number3
StatePublished - Sep 2006


  • Aortic wall thickness
  • Atherosclerosis
  • Contour detection
  • Magnetic resonance imaging
  • Model-based segmentation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Automatic vessel wall contour detection and quantification of wall thickness in in-vivo MR images of the human aorta'. Together they form a unique fingerprint.

Cite this