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- Title
Automatic plaque characterization employing quantitative and multicontrast MRI.
- Authors
Sun, Binjian; Giddens, Don P.; Long, Robert; Taylor, W. Robert; Weiss, Diana; Joseph, Giji; Vega, David; Oshinski, John N.
- Abstract
Multicontrast magnetic resonance imaging (MRI) has shown promise in identifying and characterizing atherosclerotic plaques. One of the limitations of this technique is the lack of a practical automated plaque characterization scheme. In the current study, a prior-information-enhanced clustering (PIEC) technique that utilizes both multicontrast MR images and quantitative T2 maps is proposed to characterize atherosclerotic plaque components automatically. The PIEC algorithm was assessed on computationally simulated images and multicontrast MRI data of coronary arteries. Multicontrast ( T1-, T2-, partial T2-, and proton density-weighted) MR images were acquired from freshly excised human coronary arteries using a 4.7T small-animal scanner. The T2 distribution for each plaque constituent was measured by exponentially fitting the signal from multiple MR images with different TEs and the same TR. The calculated T2 distributions were used as the a priori information and combined with the Fuzzy C-Means (FCM)-based clustering algorithm to characterize plaque constituents. The proposed PIEC technique appears to be a promising algorithm for accurate automated plaque characterization. Magn Reson Med, 2007. © 2007 Wiley-Liss, Inc.
- Publication
Magnetic Resonance in Medicine, 2008, Vol 59, Issue 1, p174
- ISSN
0740-3194
- Publication type
Article
- DOI
10.1002/mrm.21279