Analysis of carotid artery plaque and wall boundaries on CT images by using a semi-automatic method based on level set model

Saba, L., Gao, H. , Acharya, U.R., Sannia, S., Ledda, G. and Suri, J.S. (2013) Analysis of carotid artery plaque and wall boundaries on CT images by using a semi-automatic method based on level set model. Neuroradiology, 54(11), pp. 1207-1214. (doi: 10.1007/s00234-012-1040-x)

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Introduction: The purpose of this study was to evaluate the potentialities of a semi-automated technique in the detection and measurement of the carotid artery plaque. <p/>Methods: Twenty-two consecutive patients (18 males, 4 females; mean age 62 years) examined with MDCTA from January 2011 to March 2011 were included in this retrospective study. Carotid arteries are examined with a 16-multi-detector-row CT system, and for each patient, the most diseased carotid was selected. In the first phase, the carotid plaque was identified and one experienced radiologist manually traced the inner and outer boundaries by using polyline and radial distance method (PDM and RDM, respectively). In the second phase, the carotid inner and outer boundaries were traced with an automated algorithm: level-set-method (LSM). Data were compared by using Pearson rho correlation, Bland–Altman, and regression. <p/>Results: A total of 715 slices were analyzed. The mean thickness of the plaque using the reference PDM was 1.86 mm whereas using the LSM-PDM was 1.96 mm; using the reference RDM was 2.06 mm whereas using the LSM-RDM was 2.03 mm. The correlation values between the references, the LSM, the PDM and the RDM were 0.8428, 0.9921, 0.745 and 0.6425. Bland–Altman demonstrated a very good agreement in particular with the RDM method. <p/>Conclusion: Results of our study indicate that LSM method can automatically measure the thickness of the plaque and that the best results are obtained with the RDM. Our results suggest that advanced computer-based algorithms can identify and trace the plaque boundaries like an experienced human reader.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Gao, Dr Hao
Authors: Saba, L., Gao, H., Acharya, U.R., Sannia, S., Ledda, G., and Suri, J.S.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Mathematics
Journal Name:Neuroradiology
ISSN (Online):1432-1920
Published Online:06 May 2012

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