Abstract
Doppler ultrasound is an important noninvasive diagnostic tool for cardiovascular diseases. Modern ultrasound imaging systems utilize spectral Doppler techniques for quantitative evaluation of blood flow velocities, and these measurements play a crucial rule in the diagnosis and grading of arterial stenosis. One drawback of Doppler-based blood flow quantification is that the operator has to manually specify the angle between the Doppler ultrasound beam and the vessel orientation, which is called the Doppler angle, in order to calculate flow velocities. In this paper, we will describe a computer vision approach to automate the Doppler angle estimation. Our approach starts with the segmentation of blood vessels in ultrasound color Doppler images. The segmentation step is followed by an estimation technique for the Doppler angle based on a skeleton representation of the segmented vessel. We conducted preliminary clinical experiments to evaluate the agreement between the expert operator’s angle specification and the new automated method. Statistical regression analysis showed strong agreement between the manual and automated methods. We hypothesize that the automation of the Doppler angle will enhance the workflow of the ultrasound Doppler exam and achieve more standardized clinical outcome.
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References
Strandness E: Duplex Scanning in Vascular Disorders, 3rd edition. Philadelphia, USA: Williams and Wilkins, 2002
Beebe HG, Salles-Cunha SX, Scissons RP, et al: Carotid arterial ultrasound scan imaging: A direct approach to stenosis measurement. J Vasc Surg 29:838–844, 1999
Soulez G, Therasse E, Robillard P, et al: The value of internal carotid systolic velocity ratio for assessing carotid artery stenosis with Doppler sonography. AJR Am J Roentgenol 172:207–212, 1999
Grant EG, Benson CB, Moneta GL, Alexandrov AV, Baker JD, Bluth EI, et al: Carotid artery stenosis: gray-scale and Doppler US diagnosis—Society of Radiologists in Ultrasound Consensus Conference. Radiology 229:340–346, 2003
Ranke C, Creutzig A, Becker H, Trappe HJ: Standardization of carotid ultrasound: a hemodynamic method to normalize for interindividual and interequipment variability. Stroke 30:402–6, 1999
Lewis SC, Wardlaw JM: Which Doppler velocity is best for assessing suitability for carotid endarterectomy? Eur J Ultrasound 15:9–20, 2002
Corriveau MM, Johnston KW: Interobserver variability of carotid Doppler peak velocity measurements among technologists in an ICAVL-accredited vascular laboratory. J Vasc Surg 39:735–41, 2004
Mead GE, Lewis SC, Wardlaw JM: Variability in Doppler ultrasound influences referral of patients for carotid surgery. Eur J Ultrasound 12:137–43, 2000
Hoskins PR: Accuracy of maximum velocity estimates made using Doppler ultrasound systems. Br J Radiol 69:172–7, 1996
Daigle RJ, Stavros AT, Lee RM: Overestimation of velocity and frequency values by multielement linear array Doppler. J Vasc Technol 14:206–13, 1990
Steinman AH, Tavakkoli J, Myers JG, Cobbold RSC, Johnston KW: Sources of error in maximum velocity estimation using linear phased array Doppler systems with steady flow. Ultrasound Med Biol 27:655–64, 2001
Lui E, Steinman A, Cobbold R, Johnston K: Human factors as a source of error in peak Doppler velocity measurement. J Vasc Surg 42:972.el–972.el10, 2005
ICAVL: Available at http://www.intersocietal.org/icavl/news/articles/anglecorrection.htm. Accessed 04 February 2008
Lihong P, Michael J, Larry Y: Method and apparatus for automatic Doppler angle estimation in ultrasound imaging. US patent no. 6,068,598, 2000
Criton A, Routh H: Automatic flow angle correction by ultrasound vector. US patent no. 6,464,637 B1, 2002
Philips Healthcare: Available at http://www.medical.philips.com/us/products/ultrasound/general/iu22/. Accessed 04 February 2008
Evans D, McDicken W: Doppler Ultrasound Physics, Instrumentation, and Signal Processing, 2nd edition. Chichester, England: Wiley, 2000
Siddiqi K, Kimia B: Parts of visual form: computational aspects. IEEE Trans Pattern Anal Mach Intell 17:239–251, 1995
Zheng D, Lu G: Review of shape representation and description techniques. Pattern Recogn 37:1–19, 2004
Saad A, Shapiro L: Shape Decomposition Approach for Ultrasound Color Doppler Image Segmentation, IEEE ICPR: Hong Kong, 2006
Marchand-Maillet S, Sharaiha Y: Binary Digital Image Processing, A Discrete Approach, London, UK: Academic, 2000
Blum H: A Transformation for Extracting New Descriptors of Shape, Models for the Perception of Speech and Visual Form, Cambridge, MA: M.I.T Press, 1967
Montanari U: Continuous skeletons from digitized images. Journal of the ACM 16, 1969
Bitter I, Kauffman A: Penalized-distance volumetric skeleton algorithm. IEEE Trans Vis Comput Graph 7:195–206, 2001
Bland J, Altman D: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet i:307–310, 1986
Mochtarian F, Mackworth A: A Theory of multiscale, curvature-based shape representation of planar curves. IEEE Trans Pattern Anal Mach Intell 14, 1992
Wang Y, Lee S, Toraichi K: Multiscale curvature-based shape representation using B-Splines wavelets. IEEE Trans. Image Process 8, 1999
Lauchard J, Vialard A, Vieilleville F: Analysis and comparative evaluation of discrete tangent estimators. Proceedings of 12th International Conference on Discrete Geometry for Computer Imagery: 240–251, 2005
Madrazo BL, Guy WL, Bendick PJ, Dmuchowski C, Matasar KW, Ranval T: Duplex diagnostic criteria, a survey of ICAVL accredited laboratories. Ultrasound Med Biol 23(Suppl 1):S73, 1997
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Saad, A.A., Loupas, T. & Shapiro, L.G. Computer Vision Approach for Ultrasound Doppler Angle Estimation. J Digit Imaging 22, 681–688 (2009). https://doi.org/10.1007/s10278-008-9131-2
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DOI: https://doi.org/10.1007/s10278-008-9131-2