Stenosis Detection in Internal Carotid and Vertebral Arteries With the Use of Diameters Estimated from MRI Data




Stenosis, Internal carotid arteries, Vertebral arteries, Magnetic resonance imaging, Computerized angiography, Receiver operating characteristic, Optimization


Background. Magnetic resonance imaging (MRI) offers the opportunity to quantify the vessel diameters in vivo. This technique can have a breakthrough impact on the evaluation, risk stratification and therapeutical planning in hemodynamic-related pathologies, e.g., arterial stenosis. However, its applicability in clinics is limi­ted due to the complex post-processing required to extract the information and the difficulty to synthesize the obtained data into clinical useful parameters.

Objective. In this work, we use the vessel diameter distribution along its central line obtained with the use of MRI technology in order to detect the existence of stenosis in internal carotid arteries (ICA) and vertebral arteries (VA) with the minimal amount of False Negative predictions and to estimate the efficiency of therapy.

Methods. Special normalized and smoothed characteristics will be used to develop the stenosis detection criteria which can be used for every artery separately and for both vessels simultaneously. Linear and non-linear characteristics were used to increase the reliability of diagnostics. Study is based on the Receiver Operating Characteristics (ROC) and optimization methods. Real diameter data of 10 patients (80 data sets) were used.

Results. To detect stenosis, three different criteria have been proposed, based on the optimal smoothing parameters of vessel diameter distributions and the corresponding threshold values for linear and nonlinear characteristics. The use of the developed criteria allows increasing the reliability of stenosis detection.

Conclusions. Different linear, non-linear, smoothed and non-smoothed parameters and ROC were applied to detect stenosis in internal carotid and vertebral arteries. It was shown that smoothed data are necessary for VA and the criterion applicable both for VA and ICA. For ICA it is possible to use initial (unsmoothed) data. Only one False Positive case was detected for every artery. Results of application of proposed criteria are presented, tested and discussed. For VA it is possible to use criteria 1 and 2 and smoothed normalized diameter data. For ICA criterion 2 can be recommended to detect long enough narrowing areas. To detect short zones of stenosis in ICA, the criterion 3 is useful, since it uses the non-smoothed diameter data.

Author Biographies

Sergiy Jr. Pereverzyev, Medical University of Innsbruck

Department of Neuroradiology; Neuroimaging Research Core Facility

Lukas Mayer, Medical University of Innsbruck

Department of Neurology

Ruth Steiger, Medical University of Innsbruck

Department of Neuroradiology; Neuroimaging Research Core Facility

Lukas Kusstatscher, Medical University of Innsbruck

Department of Neuroradiology

Karl Fritscher, University for Health Sciences, Medical Informatics and Technology

Department of Medical Image Analysis

Michael Knoflach, Medical University of Innsbruck

Department of Neurology

Elke Ruth Gizewski, Medical University Innsbruck

Department of Neuroradiology; Neuroimaging Research Core Facility


Kirişli H, Schaap M, Metz CT, Dharampal AS, Meijboom WB, Papadopoulou SL, et al. Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomo­graphy angiography. Medical Image Analysis. 2020;17(8):859-76. DOI: 10.1016/

Shahzad R, van Walsum T, Kirisli H, Tang, H. Automatic stenoses detection, quantification and lumen segmentation of the coronary arteries using a two point centerline extraction scheme. In: Proceedings of MICCAI Workshop 3D Cardiovascular Imaging: A MICCAI Segmentation Challenge; 2012.

Mayer L, Boehme C, Toell T, Dejakum B, Willeit J, Schmidauer C, et al. Local signs and symptoms in spontaneous cervical artery dissection: a single centre cohort study. J Stroke. 2019;21(1):112. DOI: 10.5853/jos.2018.03055

Yushkevich PA, Piven J, Hazlett HC, Smith RG, Ho S, Gee JC, et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage. 2006;31(3):1116-28. DOI: 10.1016/j.neuroimage.2006.01.015

Quek F, Kirbas C. Vessel extraction in medical images by wVAe propagation and trace-back. IEEE Trans Med Imaging. 2001;20(2):117-31. DOI: 10.1109/42.913178

Raman V, Then P. Novelty towards hybrid segmentation of coronary artery in CT cardiac images. In: Ninth ACIS International Conference on Software Engineering, Artificial Intel-ligence, Networking, and Parallel/Distributed Computing; 2008. p. 513-16. DOI: 10.1109/snpd.2008.53

Benmansour F, Cohen LD. A new interactive method for coronary arteries segmentation based on tubular anisotropy. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro; 2009. p. 41-4. DOI: 10.1109/isbi.2009.5192978

Chen K, Zhang Y, Pohl K, Syeda-Mahmood T, Song Z, Wong STC. Coronary artery segmentation using geometric moments-based tracking and snake-driven refinement. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS); 2010. p. 3133-7. DOI: 10.1109/iembs.2010.5627192

Xu Y, Liang G, Hu G, Yang Y, Geng J, Saha PK. Quantification of coronary arterial stenoses in CTA using fuzzy distance transform. Comput Med Imaging Graph. 2012;36(1):11-24. DOI: 10.1016/j.compmedimag.2011.03.004

Yang G, Broersen A, Petr R, Kitslaar P, de Graaf MA, Bax JJ, et al. Automatic coronary artery tree labeling in coronary computed tomographic angiography datasets. In: 2011 Computing in Cardiology; Hangzhou; 2011. p. 109-12.

Nesteruk I, Redaelli A, Kudybyn I, Piatti F, Sturla F. Global and local characteristics of the blood flow in large vessels based on 4D MRI data. Naukovi Visti NTUU KPI. 2017;2:37-44. DOI: 10.20535/1810-0546.2017.2.99724

Nesteruk I, Piatti F, Sytnyk D, Redaelli A. Differentiation of the 4D MRI blood flow data to estimate the vorticity and shear stress in aorta, pulmonary artery and the heart. In: Proceedings of 2019 IEEE 39th Internatiomal Conference on Electronics and Nanotechnology; Kyiv; 2019, Apr 16-18. p. 415-20. DOI: 10.1109/elnano.2019.8783689

Öksüz İ, Ünay D, Kadıpaşaoğlu K. A hybrid method for coronary artery stenoses detection and quantificationin CTA images [Internet]. 2020 [cited 2020 May 25]. Available from:

Hajian-Tilaki K. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med. 2013;4(2):627-35.




How to Cite

Nesteruk I, Pereverzyev SJ, Mayer L, Steiger R, Kusstatscher L, Fritscher K, Knoflach M, Gizewski ER. Stenosis Detection in Internal Carotid and Vertebral Arteries With the Use of Diameters Estimated from MRI Data. Innov Biosyst Bioeng [Internet]. 2020Jul.13 [cited 2024May28];4(3):131-42. Available from: