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A method of ultrasound diagnosis for unilateral peripheral entrapment neuropathy based on multilevel side-to-side image contrast

  • Individual variations have been reported in the existing methods for examining peripheral entrapment neuropathy, by which limited sites can be examined. In this study, the patients with unilateral carpal tunnel syndrome (CTS), cubital tunnel syndrome (CuTS) and radial nerve compression (RNC) were selected as research subjects and an ultrasound technique was proposed based on multilevel side-to-side image contrast for the diagnosis of unilateral peripheral entrapment neuropathy. According to the statistical analysis of 62 patients with CTS, CuTS or RNC, the diagnostic thresholds of the cross-sectional area swelling ratio (CSASR) for diagnosis of CTS, CuTS or RNC were 1.22, 1.51 and 1.50, respectively. The surgical therapeutic thresholds of CSASR for the treatment of CTS, CuTS and RNC were 1.48, 1.67 and 3.04, respectively. When the maximal CSASR of the diseased nerve was greater than or equal to the diagnostic threshold, the nerve compression could be diagnosed. If it was less than the diagnostic threshold, nerve compression was excluded. Conservative treatment was indicated when the maximal CSASR of the diseased nerve was less than the therapeutic threshold. When the maximal CSASR was greater than or equal to the therapeutic threshold, surgical treatment was indicated, and the nerve release procedure was selected. The novel multilevel side-to-side image contrast ultrasound technique proposed in this study can substantially reduce the impact of individual variation and explore the full course of the diseased nerve. It is a novel approach for diagnosis, treatment selection, and determination of treatment sites of unilateral peripheral entrapment neuropathy.

    Citation: Xueyuan Li, MiaoYu, Xiaoling Zhou, Yi Li, Hong Chen, Liping Wang, Jianghui Dong. A method of ultrasound diagnosis for unilateral peripheral entrapment neuropathy based on multilevel side-to-side image contrast[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 2250-2265. doi: 10.3934/mbe.2019111

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  • Individual variations have been reported in the existing methods for examining peripheral entrapment neuropathy, by which limited sites can be examined. In this study, the patients with unilateral carpal tunnel syndrome (CTS), cubital tunnel syndrome (CuTS) and radial nerve compression (RNC) were selected as research subjects and an ultrasound technique was proposed based on multilevel side-to-side image contrast for the diagnosis of unilateral peripheral entrapment neuropathy. According to the statistical analysis of 62 patients with CTS, CuTS or RNC, the diagnostic thresholds of the cross-sectional area swelling ratio (CSASR) for diagnosis of CTS, CuTS or RNC were 1.22, 1.51 and 1.50, respectively. The surgical therapeutic thresholds of CSASR for the treatment of CTS, CuTS and RNC were 1.48, 1.67 and 3.04, respectively. When the maximal CSASR of the diseased nerve was greater than or equal to the diagnostic threshold, the nerve compression could be diagnosed. If it was less than the diagnostic threshold, nerve compression was excluded. Conservative treatment was indicated when the maximal CSASR of the diseased nerve was less than the therapeutic threshold. When the maximal CSASR was greater than or equal to the therapeutic threshold, surgical treatment was indicated, and the nerve release procedure was selected. The novel multilevel side-to-side image contrast ultrasound technique proposed in this study can substantially reduce the impact of individual variation and explore the full course of the diseased nerve. It is a novel approach for diagnosis, treatment selection, and determination of treatment sites of unilateral peripheral entrapment neuropathy.




    [1] S. I. Holtzman, The electromyogram (EMG) practical and valuable in clinical medicine, Ariz. Med., 31 (1974), 351–355.
    [2] K. K. Nakano, The entrapment neuropathies, Muscle Nerve, 1 (1978), 264–279.
    [3] O. Heinemeyer and C. D. Reimers, Ultrasound of radial, ulnar, median, and sciatic nerves in healthy subjects and patients with hereditary motor and sensory neuropathies, Ultrasound Med. Biol., 25 (1999), 481–485.
    [4] J. Y. Kim, J. S. Yoon and S. J. Kim, et al., Carpal tunnel syndrome: Clinical, electrophysiological, and ultrasonographic ratio after surgery, Muscle Nerve, 45 (2012), 183–188.
    [5] R. Beekman, J. P. Van Der Plas and B. M. Uitdehaag, et al., Clinical, electrodiagnostic, and sonographic studies in ulnar neuropathy at the elbow, Muscle Nerve, 30 (2004), 202–208.
    [6] T. Djurdjevic, A. Loizides and W. Loscher, et al., High resolution ultrasound in posterior interosseous nerve syndrome, Muscle Nerve, 49 (2014), 35–39.
    [7] W. T. Oh, H. J. Kang and I. H. Koh, et al., Morphologic change of nerve and symptom relief are similar after mini-incision and endoscopic carpal tunnel release: a randomized trial, BMC Musculoskelet Disord., 18 (2017), 65.
    [8] Q. Huang, J. Lan and X. Li, Robotic arm based automatic ultrasound scanning for three-dimensional imaging, IEEE Trans. Ind. Informat., (2018), 1173–1182.
    [9] Q. Huang, F. Zhang and X. Li, Machine learning in ultrasound computer-aided diagnostic systems: A survey, Biomed Res. Int., 2018 (2018), 5137904.
    [10] Q. Huang, Z. Zeng and X. Li, 2.5-Dimensional Extended Field-of-View Ultrasound, IEEE Trans. Med. Imag., 37 (2017), 851–859.
    [11] Q. Huang and Z. Zeng, A review on real-time 3D ultrasound imaging technology, Biomed Res. Int., 2017 (2017), 6027029
    [12] Q. Huang, B. Wu and J. Lan, et al., Fully automatic three-dimensional ultrasound imaging based on conventional B-Scan, IEEE Trans. Biomed. Circuits Syst., 12 (2018), 426–436.
    [13] B. D. Fornage and M. D. Rifkin, Ultrasound examination of the hand and foot, Radiol. Clin. North Am., 26 (1988), 109–129.
    [14] S. Podnar, Contribution of ultrasonography to the evaluation of peripheral nerve disorders, Neurophysiol. Clin., 48 (2018), 119–123.
    [15] D. Azman, P. Hrabac and V. Demarin, Use of Multiple Ultrasonographic Parameters in Confirmation of Carpal Tunnel Syndrome, J. Ultrasound Med., 37 (2018), 879–889.
    [16] N. G. Simon, J. W. Ralph and A. N. Poncelet, et al., A comparison of ultrasonographic and electrophysiologic 'inching' in ulnar neuropathy at the elbow, Clin. Neurophysiol., 126 (2015), 391–398.
    [17] R. Terlemez, F. Yilmaz and B. Dogu, et al., Comparison of ultrasonography and short-segment nerve conduction study in ulnar neuropathy at the elbow, Arch. Phy. Med. Rehabil., 99 (2018), 116–120.
    [18] T. Atan and Z. Gunendi, Diagnostic utility of the sonographic median to ulnar nerve cross-sectional area ratio in carpal tunnel syndrome, Turk. J. Med. Sci., 48 (2018), 110–116.
    [19] A. Y. Karahan, S. Arslan and B. Ordahan, et al., Superb microvascular imaging of the median nerve in carpal tunnel syndrome: An electrodiagnostic and ultrasonographic study, J. Ultrasound Med., 37(2018), 2855–2861.
    [20] A. R. Ghasemi-Esfe, O. Khalilzadeh and S.M. Vaziri-Bozorg, et al., Color and power Doppler US for diagnosing carpal tunnel syndrome and determining its severity: a quantitative image processing method, Radiology, 261 (2011), 499–506.
    [21] S. Kesikburun, E. Adiguzel and B. Kesikburun, et al., Sonoelastographic assessment of the median nerve in the longitudinal plane for carpal tunnel syndrome, P M & R, 8 (2016), 183–185.
    [22] F. Kantarci, F. E. Ustabasioglu and S. Delil, et al., Median nerve stiffness measurement by shear wave elastography: a potential sonographic method in the diagnosis of carpal tunnel syndrome, Eur. Radiol., 24 (2014), 434–440.
    [23] M. A. Bedewi, A. Abodonya and M. Kotb, et al., Estimation of ultrasound reference values for the upper limb peripheral nerves in adults: A cross-sectional study, Medicine (Baltimore), 96 (2017), e9306.
    [24] J. Boehm, E. Scheidl and D. Bereczki, et al., High-resolution ultrasonography of peripheral nerves: measurements on 14 nerve segments in 56 healthy subjects and reliability assessments, Eur. J. Ultrasound, 35 (2014), 459–467.
    [25] L. Padua, C. Martinoli and C. Pazzaglia, et al., Intra- and internerve cross-sectional area variability: new ultrasound measures, Muscle Nerve, 45 (2012), 730–733.
    [26] D. B. Sanders, M. Benatar and T. M. Burns, et al., Intra- and internerve cross-sectional area variability: new ultrasound measures, Muscle Nerve, 47 (2013), 145–146.
    [27] K. Thoirs, M. A. Williams and M. Phillips, Ultrasonographic measurements of the ulnar nerve at the elbow, J. Ultrasound Med., 27 (2008), 737–743.
    [28] E. Yalcin, E. Unlu and M. Akyuz, et al., Karaahmet, Ultrasound diagnosis of ulnar neuropathy: comparison of symptomatic and asymptomatic nerve thickness, J. Hand Surg. Eur., 39 (2014), 167–171.
    [29] T. Toros, Commentary on Zyluk et al. No correlation between sonographic and electrophysiological parameters in carpal tunnel syndrome; and Yalcin et al. Ultrasound diagnosis of ulnar neuropathy: comparison of symptomatic and asymtomatic nerve thickness, J. Hand Surg. Eur., 39 (2014), 172–174.
    [30] A. Kerasnoudis and G. Tsivgoulis, Nerve ultrasound in peripheral neuropathies: A review, J. Neuroimaging, 25 (2015), 528–538.
    [31] A. Kerasnoudis, K. Pitarokoili and V. Behrendt, et al., Cross sectional area reference values for sonography of peripheral nerves and brachial plexus, Clin. Neurophysiol.,124 (2013), 1881–1888.
    [32] A. M. Ulasli, M. Duymus and B. Nacir, et al., Reasons for using swelling ratio in sonographic diagnosis of carpal tunnel syndrome and a reliable method for its calculation, Muscle Nerve, 47 (2013), 396–402.
    [33] A. B. Lammer, S. Schwab and A. Schramm, Ultrasound in dual nerve impairment after proximal radial nerve lesion, PLoS One, 10 (2015), e0127456.
    [34] L. Liu, X. Wang and Y. Li, et al., Adhesion pulmonary nodules detection based on dot-filter and extracting centerline algorithm, Comput. Math. Methods Med., 2015 (2015), 597313
    [35] Y. Zhong, L. Wang and J. Dong, et al., Three-dimensional reconstruction of peripheral nerve internal fascicular groups, Sci. Rep., 5 (2015), 17168.
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