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Computer-assisted characterisation of a carotid plaque

Aleksander Falkowski, Mariusz Kaczmarczyk, Andrzej Ciechanowicz, Iwona Gorący, Wojciech Poncyljusz, Grażyna Wilk

Med Sci Monit 2004; 10(3): 67-70

ID: 878555

Published: 1999-11-30


Background: The aim of this study was to attempt an objective assessment of carotid plaque echogenicity by means of computer-aided technique and to determine the association between the echogenicity and the incidence of brain infarctions.
Material and Methods: 54 carotid plaques causing internal carotid artery stenosis within the range of 60–99% constituted the material for our studies. The images of the plaques obtained by means of USG were transferred to, and saved on the computer. Using image analysis software, a histogram of each plaque was obtained. The grey scale median (GSM range 0–255) for each histogram, which was used as a measure of plaque echogenicity, was determined. The value of GSM was assessed in patients who suffered from brain infarctions in the area of the stenosed artery and in patients without infarctions.
Results: The grey scale median associated with cerebral infarction was nearly two times lower in comparison with GSM of plaques found in patients without infarction. Among 30 plaques with GSM >35, only 5.5% were connected with brain infarction, and 27.8% of the 24 plaques with GSM <35 were associated with brain infarction.
No correlation with cerebral infarction was found for 50% of the plaques with GSM >35 and 16.7% of the plaques with GSM <35.
Conclusions: The use of computer-aided method allows assessment of carotid plaque echogenicity and can be used for clinical studies aimed at evaluation of the correlation between carotid plaque type and a risk of ischemic stroke.

Keywords: usg, carotid plaque, grey scale median, brain infarction, Aged, Blood Flow Velocity, Brain Infarction - ultrasonography, Carotid Artery, Internal - ultrasonography, Carotid Stenosis - ultrasonography, Diagnosis, Computer-Assisted - methods, Female, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Risk Factors



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