Automatic segmentation and plaque characterization in atherosclerotic carotid artery MR images

I. M. Adame, R. J. Van Der Geest, B. A. Wasserman, M. A. Mohamed, J. H.C. Reiber, B. P.F. Lelieveldt

Research output: Contribution to journalArticlepeer-review

Abstract

In vivo MRI provides a means to non-invasively image and assess the morphological features of atherosclerotic carotid arteries. To assess quantitatively the degree of vulnerability and the type of plaque, the contours of the lumen, outer boundary of the vessel wall and plaque components, need to be traced. Currently this is done manually, which is time-consuming and sensitive to inter- and intra-observer variability. The goal of this work was to develop an automated contour detection technique for tracing the lumen, outer boundary and plaque contours in carotid MR short-axis black-blood images. Seventeen patients with carotid atherosclerosis were imaged using high-resolution in vivo MRI, generating a total of 50 PD- and T1-weighted MR images. These images were automatically segmented using the algorithm presented in this work, which combines model-based segmentation and fuzzy clustering to detect the vessel wall, lumen and lipid core boundaries. The results demonstrate excellent correspondence between automatic and manual area measurements for lumen (r = 0.92) and outer (r = 0.91), and acceptable correspondence for fibrous cap thickness (r = 0.71). Though further optimization is required, our algorithm is a powerful tool for automatic detection of lumen and outer boundaries, and characterization of plaque in atherosclerotic vessels.

Original languageEnglish (US)
Pages (from-to)227-234
Number of pages8
JournalMagnetic Resonance Materials in Physics, Biology and Medicine
Volume16
Issue number5
DOIs
StatePublished - Apr 1 2004

Keywords

  • Atherosclerosis
  • Carotid artery MRI
  • Magnetic resonance imaging
  • Plaque composition

ASJC Scopus subject areas

  • Biophysics
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging

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