Book contents
- Frontmatter
- Contents
- List of contributors
- List of abbreviations
- Introduction
- Background
- Luminal imaging techniques
- Morphological plaque imaging
- 14 MR plaque imaging
- 15 CT plaque imaging
- 16 Assessment of carotid plaque with conventional ultrasound
- 17 Assessment of carotid plaque with intravascular ultrasound
- 18 Image postprocessing
- Functional plaque imaging
- Plaque modelling
- Monitoring the local and distal effects of carotid interventions
- Monitoring pharmaceutical interventions
- Future directions in carotid plaque imaging
- Index
- References
18 - Image postprocessing
from Morphological plaque imaging
Published online by Cambridge University Press: 03 December 2009
Book contents
- Frontmatter
- Contents
- List of contributors
- List of abbreviations
- Introduction
- Background
- Luminal imaging techniques
- Morphological plaque imaging
- 14 MR plaque imaging
- 15 CT plaque imaging
- 16 Assessment of carotid plaque with conventional ultrasound
- 17 Assessment of carotid plaque with intravascular ultrasound
- 18 Image postprocessing
- Functional plaque imaging
- Plaque modelling
- Monitoring the local and distal effects of carotid interventions
- Monitoring pharmaceutical interventions
- Future directions in carotid plaque imaging
- Index
- References
Summary
Introduction
Atherosclerosis, the disease behind heart attacks and strokes, is characterized by build-up of plaque within the intimal layer of arteries. Clinical events occur due to thrombosis, in which a clot occludes the vessel at the lesion site and embolization, in which thrombotic materials from the site of a lesion are released into the blood stream and occlude distal vessels. To date, the primary clinical indicator for risk from atherosclerotic plaque has been stenosis, expressed as a percentage reduction in the lumen diameter of the vessel. Stenosis is typically assessed by angiography or duplex ultrasound.
However, stenosis provides an incomplete picture of risk. Arteries exhibiting only moderate stenosis account for a large percentage of strokes and heart attacks. Histological studies in various vascular beds have established that features of the atherosclerotic plaque itself dictate its clinical course in cases of moderate stenosis (Falk, 1992). Specific plaque features associated with clinical risk include a fibrous cap that is thin, ruptured, or ulcerated and a large lipid-rich necrotic core (Virmani et al., 2000). Together these features define the “vulnerable plaque.”
Given the significance of vulnerable plaque for patient prognosis, considerable interest exists in developing noninvasive means to measure plaque features and provide clinical indicators that augment stenosis. Research in recent years has shown that magnetic resonance imaging (MRI) is a powerful tool for identifying plaque features in the carotid artery.
Keywords
- Type
- Chapter
- Information
- Carotid DiseaseThe Role of Imaging in Diagnosis and Management, pp. 235 - 250Publisher: Cambridge University PressPrint publication year: 2006
References
, , , et al. (2004). Automatic segmentation and plaque characterization in atherosclerotic carotid artery Magnetic resonance images. Magma, 16, 227–34.CrossRefGoogle Scholar
, and (2000). B-spline snakes: A flexible tool for parametric contour detection. IEEE Transactions on Image Processing, 9, 1484–96.CrossRefGoogle ScholarPubMed
(1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis, 8, 679–98.CrossRefGoogle ScholarPubMed
, and , et al. (2004). Hemorrhage in the atherosclerotic carotid plaque: A high-resolution Magnetic resonance imaging study. Stroke, 35, 1079–84.CrossRefGoogle Scholar
, , and (2003). Quantitative assessment of carotid plaque composition using multicontrast Magnetic resonance imaging and registered histology. Magnetic Resonance in Medicine, 50, 1199–208.CrossRefGoogle ScholarPubMed
and (2002). Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 603–19.CrossRefGoogle Scholar
and (1985). Plaque fissuring–the cause of acute myocardial infarction, sudden ischaemic death, and crescendo angina. British Heart Journal, 53, 363–73.CrossRefGoogle ScholarPubMed
, , , and (1993). Risk of thrombosis in human atherosclerotic plaques: role of extracellular lipid, macrophage, and smooth muscle cell content. British Heart Journal, 69, 377–81.CrossRefGoogle ScholarPubMed
and (1975a). The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 32–40.Google Scholar
and (1975b). k-nearest-neighbor bayes-risk estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21, 285–93.Google Scholar
and (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis, 6, 721–41.CrossRefGoogle ScholarPubMed
, , and (2001). A fast minimal path active contour model. IEEE Transactions on Image Processing, 10, 865–73.CrossRefGoogle Scholar
, , , and (2003). Detecting objects in image sequences using rule-based control in an active contour model. IEEE Transactions on Biomedical Engineering, 50, 705–10.CrossRefGoogle Scholar
, , and (2000). Visualization of fibrous cap thickness and rupture in human atherosclerotic carotid plaque in vivo with high resolution magnetic resonance imaging. Circulation, 102, 959–64.CrossRefGoogle ScholarPubMed
, and (1996). Surface coil phased arrays for high-resolution imaging of the carotid arteries. Journal of Magnetic Resonance Imaging, 6, 109–12.CrossRefGoogle ScholarPubMed
(1995). A tutorial on learning with Bayesian networks. Technical report, MSR-TR-95–06.
, , ., et al. (2004). Quantification of human atherosclerotic plaques using spatially enhanced cluster analysis of multicontrast-weighted magnetic resonance images. Magnetic Resonance in Medicine, 52, 515–23.CrossRefGoogle ScholarPubMed
, and (1987). Snakes: active contour models. International Journal of Computer Vision, 1, 321–31.CrossRefGoogle Scholar
and (2001). Active edge maps for medical image registration. Proceedings of SPIE, 4322, 516–26.CrossRefGoogle Scholar
, , , et al. (2006). Automated in vivo Segmentation of Carotid Plaque Magnetic resonance imaging with Morphology-Enhanced Probability Maps. Magnetic Resonance in Medicine, in press.CrossRefGoogle ScholarPubMed
, , , et al. (2003). Accuracy and uniqueness of three in vivo measurements of atherosclerotic carotid plaque morphology with black blood Magnetic resonance imaging. Magnetic Resonance in Medicine, 50, 75–82.CrossRefGoogle Scholar
, , , et al. (2004). Quantification of multicontrast vascular Magnetic resonance images with nlsnake, an active contour model: in vitro validation and in vivo evaluation. Magnetic Resonance in Medicine, 51, 370–9.CrossRefGoogle ScholarPubMed
, , , et al. (2003). In vivo accuracy of multisequence Magnetic resonance imaging for identifying unstable fibrous caps in advanced human carotid plaques. Journal of Magnetic Resonance Imaging, 17, 410–20.CrossRefGoogle ScholarPubMed
and (2000). Coupled geodesic active regions for image segmentation: a level set approach. European Conference in Computer Vision, 224–40.Google Scholar
, , and (1999). Electron beam computed tomographic coronary calcium scanning: a review and guidelines for use in asymptomatic persons. Mayo Clinic Proceedings, 74, 538.CrossRefGoogle ScholarPubMed
, , , et al. (2005). Quantitative evaluation of carotid plaque composition by in vivo Magnetic resonance imaging. Arteriosclerosis, Thrombosis and Vascular Biology, 25, 234–9.Google Scholar
, , , et al. (2006). Sample size calculation for clinical trials using magnetic resonance imaging for the quantitative assessment of carotid atherosclerosis. Journal of Cardiovascular Magnetic Resonance, in press.Google Scholar
, , , et al. (1999). The diagnostic accuracy of ex vivo Magnetic resonance imaging for human atherosclerotic plaque characterization. Arteriosclerosis, Thrombosis and Vascular Biology, 19, 2756–61.CrossRefGoogle ScholarPubMed
, , , et al. (2001). Usefulness of electron beam tomography to detect progression of coronary and aortic calcium in middle-aged women. American Journal of Cardiology, 87, 560–4.CrossRefGoogle ScholarPubMed
, , and (2005). Is common carotid artery mean wall thickness by Magnetic resonance imaging comparable to intima-media thickness by B-Mode Ultrasound?Journal of Cardiovascular Magnetic Resonance, 7, 140–1.Google Scholar
, , , and (2000). Lessons from sudden coronary death: a comprehensive morphological classification scheme for atherosclerotic lesions. Arteriosclerosis, Thrombosis and Vascular Biology, 20, 1262–75.CrossRefGoogle ScholarPubMed
, , , et al. (2002). Stroke is associated with coronary calcification as detected by electron-beam Computerized tomography: the Rotterdam Coronary Calcification Study. Stroke, 33, 462–5.CrossRefGoogle ScholarPubMed
and (2004). Proteoglycans in atherosclerosis and restenosis: key roles for versican. Circulation Research, 94, 1158 –67.CrossRefGoogle ScholarPubMed
, , , and (2004). CASCADE: Computer aided system for cardiovascular disease evaluation. International Society for Magnetic Resonance in Medicine, abstract 1922.Google Scholar
and (2004). Magnetic resonance imaging of atherosclerosis. Journal of Magnetic Resonance Imaging, 19, 710–19.CrossRefGoogle ScholarPubMed
, , , et al. (2001). In vivo accuracy of multispectral magnetic resonance imaging for identifying lipid-rich necrotic cores and intraplaque hemorrhage in advanced human carotid plaques. Circulation, 104, 2051–6.CrossRefGoogle ScholarPubMed
, , , et al. (2001). Effects of prolonged intensive lipid-lowering therapy on the characteristics of carotid atherosclerotic plaques in vivo by Magnetic resonance imaging: a case-control study. Arteriosclerosis, Thrombosis and Vascular Biology, 21, 1623–9.CrossRefGoogle ScholarPubMed
and (1996). Region competition: unifying snakes, region growing, and Bayesian/MDL for multiband image segmentation. IEEE Transactions on Pattern Analysis, 18, 884–900.Google Scholar