Automated measurement of carotid angle with use of CT images

Year 2022, Volume: 4 Issue: 2, 37 - 44, 25.12.2022

Nusret Demir , Serkan Demir

https://doi.org/10.53093/mephoj.1166415

Abstract

Carotid stenosis is an important etiological factor in the forming of ischemic stroke. The weight of stroke which is formed as a result of extracranial internal carotid artery stenosis or occlusion differs according to the location, size of interaction, collateral supply, and the mechanisms those cause interact. Therefore, it is important to measure the narrowness of the carotid with the calculation of the bifurcation angle. In this study, CT cross-sectional image sequences are used. The images are unsupervised classified, and the carotid veins are identified with the boundaries and centers of the clusters. Then, the angles are calculated with three center points of the veins from successive images. The center point of the calculation is from the vein which has the maximum area difference between one of the successive images. The results are evaluated using 5 samples with visual interpretation regarding the position and the correctness of the three successive images which have maximum area jump.

Keywords

Medical Image Processing, Photogrammetry, CT, Neurology, Stroke

References

• Demir, N., & Demir, S. (2015, May). Automated calculation of bifurcation carotid angle for analyzing the risk of carotis plaques by using carotid CT angiographic images. In Smart Biomedical and Physiological Sensor Technology XII (Vol. 9487, pp. 91-99). SPIE.
• Midi, İ., & Afşar, N. (2010). İnme risk faktörleri. Klinik gelişim, 10(1), 1-14.
• Bouthillier, A., Van Loveren, H. R., & Keller, J. T. (1996). Segments of the internal carotid artery: a new classification. Neurosurgery, 38(3), 425-433.
• Adams, R. D., Victor, M., Ropper, A. H., & Daroff, R. B. (1997, July). Principles of neurology. Neuropsychiatry, Neuropsychology & Behavioral Neurology, 10(3), 220
• Autret, A., Saudeau, D., Bertrand, P. H., Pourcelot, L., Marchal, C., & De Boisvilliers, S. (1987). Stroke risk in patients with carotid stenosis. The Lancet, 329(8538), 888-890.
• Inzitari, D., Eliasziw, M., Gates, P., Sharpe, B. L., Chan, R. K., Meldrum, H. E., & Barnett, H. J. (2000). The causes and risk of stroke in patients with asymptomatic internal-carotid-artery stenosis. New England Journal of Medicine, 342(23), 1693-1701.
• Ringelstein, E. B., Koschorke, S., Holling, A., Thron, A., Lambertz, H., & Minale, C. (1989). Computed tomographic patterns of proven embolic brain infarctions. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 26(6), 759-765.
• European Carotid Surgery Trialists Collaborative Group. (1991). MRC European Carotid Surgery Trial: interim results for symptomatic patients with severe (70-99%) or mild (0-29%) carotid stenosis. Lancet, 337, 1235-1243.
• Mayberg, M. R., & Winn, H. R. (1995). Endarterectomy for asymptomatic carotid artery stenosis: resolving the controversy. JAMA, 273(18), 1459-1461.
• Dix, J. E., Evans, A. J., Kallmes, D. F., Sobel, A. H., & Phillips, C. D. (1997). Accuracy and precision of CT angiography in a model of carotid artery bifurcation stenosis. American journal of neuroradiology, 18(3), 409-415.
• Suinesiaputra, A., de Koning, P. J., Zudilova-Seinstra, E., Reiber, J. H., & van der Geest, R. J. (2012). Automated quantification of carotid artery stenosis on contrast-enhanced MRA data using a deformable vascular tube model. The International Journal of Cardiovascular Imaging, 28(6), 1513-1524.
• Kefayati, S., & Poepping, T. L. (2010, January). 3-D flow characterization and shear stress in a stenosed carotid artery bifurcation model using stereoscopic PIV technique. In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology (pp. 3386-3389). IEEE.
• Fisher, M. (2012). Geometry is destiny for carotid atherosclerotic plaques. Nature Reviews Neurology, 8(3), 127-129.
• Stroud, J. S., Berger, S. A., & Saloner, D. (2002). Numerical analysis of flow through a severely stenotic carotid artery bifurcation. Journal of Biomechanical Engineering, 124(1), 9-20.
• Cebral, J. R., Yim, P. J., Löhner, R., Soto, O., & Choyke, P. L. (2002). Blood flow modeling in carotid arteries with computational fluid dynamics and MR imaging. Academic radiology, 9(11), 1286-1299.
• Tan, F. P. P., Soloperto, G., Bashford, S., Wood, N. B., Thom, S., Hughes, A., & Xu, X. Y. (2008). Analysis of flow disturbance in a stenosed carotid artery bifurcation using two-equation transitional and turbulence models. Journal of biomechanical engineering, 130(6), 061008
• Wong, K. K., Thavornpattanapong, P., Cheung, S. C., Sun, Z., & Tu, J. (2012). Effect of calcification on the mechanical stability of plaque based on a three-dimensional carotid bifurcation model. BMC cardiovascular disorders, 12(1), 1-18.
• Gul, R., & Bernhard, S. (2015). Parametric uncertainty and global sensitivity analysis in a model of the carotid bifurcation: Identification and ranking of most sensitive model parameters. Mathematical biosciences, 269, 104-116.
• van ˈt Klooster, R., Staring, M., Klein, S., Kwee, R. M., Kooi, M. E., Reiber, J. H., ... & van der Geest, R. J. (2013). Automated registration of multispectral MR vessel wall images of the carotid artery. Medical physics, 40(12), 121904.
• Lawrence‐Brown, M., Stanley, B. M., Sun, Z., Semmens, J. B., & Liffman, K. (2011). Stress and strain behaviour modelling of the carotid bifurcation. ANZ Journal of Surgery, 81(11), 810-816.
• McNamara, J. R., Fulton, G. J., & Manning, B. J. (2015). Three-dimensional computed tomographic reconstruction of the carotid artery: identifying high bifurcation. European Journal of Vascular and Endovascular Surgery, 49(2), 147-153.
• Ridler, T. W., & Calvard, S. (1978). Picture thresholding using an iterative selection method. IEEE trans syst Man Cybern, 8(8), 630-632.