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Quantitative ultrasound of trapezius muscle involvement in myofascial pain: comparison of clinical and healthy population using texture analysis

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Abstract

Purpose

Ultrasound is a non-invasive quantitative method to characterize sonographic textures of skeletal muscles. To date, there is no information available on the trapezius muscle. This study assessed the trapezius muscles of patients with myofascial pain compared with normal healthy participants.

Methods

The trapezius muscles of 15 healthy and 17 myofascial pain participants were assessed using B-mode ultrasound to obtain 120 images for healthy and 162 images from myofascial pain participants. Texture features such as blob area, count and local binary patterns (LBP) were calculated. Multi-feature classification and analysis were performed using principal component analysis (PCA) and MANOVA to determine whether there were statistical differences.

Results

We demonstrate the two principal components composed of a combination of LBP and blob parameters which explain 92.55% of the cumulative variance of our data set. In addition, blob characteristics were significantly different between healthy and myofascial pain participants.

Conclusion

Our study provides evidence that texture analysis techniques can differentiate between healthy and myofascial pain affected trapezius muscles. Further research is necessary to evaluate the nature of these differences.

Sommario

Obiettivi

L’ecografia è un metodo quantitativo non invasivo utile per caratterizzare la texture sonografica dei muscoli scheletrici. Allo stato attuale, vi sono pochi dati in letteratura riguardo il muscolo trapezio. Questo studio ha valutato i muscoli trapezi dei pazienti con dolore miofasciale confrontandoli con quelli dei soggetti sani.

Metodi

L’ecografia B-mode è stata utilizzata per valutare i muscoli trapezi di 15 soggetti sani e di 17 pazienti con dolore miofasciale , ottenendo 120 immagini per i soggetti sani e 162 per i pazienti con dolore miofasciale. Sono state calcolate caratteristiche di texture come la blob area, i count ed i local binary pattern (LBP). La classificazione e l'analisi multiparametrica sono state eseguite utilizzando l'analisi delle componenti principali (PCA) e MANOVA per valutare se vi fossero differenze statistiche.

Risultati

Abbiamo dimostrato che due componenti principali, composte da una combinazione di LBP e parametri blob, spiegano il 92.55% della varianza cumulativa dei nostri dati. Inoltre, le caratteristiche blob erano significativamente differenti tra i pazienti con dolore miofasciale ed i soggetti sani.

Conclusioni

Il nostro studio fornisce l’evidenza che le tecniche di texture analysis possono differenziare i muscoli trapezi dei soggetti sani da quelli dei pazienti affetti da dolore miofasciale. Ulteriori studi sono necessari per valutare la natura di tali differenze.

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References

  1. Harden RN, Bruehl SP, Gass S, Niemiec C, Barbick B (2000) Signs and symptoms of the myofascial pain syndrome: a national survey of pain management providers. Clin J Pain 16(1):64–72

    Article  CAS  Google Scholar 

  2. Gerwin RD (2001) Classification, epidemiology, and natural history of myofascial pain syndrome. Curr Pain Headache Rep 5(5):412–420

    Article  CAS  Google Scholar 

  3. Skootsky SA, Jaeger B, Oye RK (1989) Prevalence of myofascical pain in general internal medicine practice. Retrieved From West J Med 151(20):157–160

    CAS  Google Scholar 

  4. Fishbain DA, Goldberg M, Meagher BR, Steele R, Rosomoff H (1986) Male and female chronic pain patients categorized by DSM-III psychiatric diagnostic criteria. Pain 26(2):181–197

    Article  CAS  Google Scholar 

  5. Fleckenstein J, Zaps D, Ruger LJ, Lehmeyer L, Freiberg F, Lang PM, Irnich D (2010) Discrepancy between prevalence and perceived effectiveness of treatment methods in myofascial pain syndrome: results of a cross-sectional, nationwide survey. BMC Musculoskelet Disord 11:32

    Article  Google Scholar 

  6. Mourtzakis M, Wischmeyer P (2014) Bedside ultrasound measurement of skeletal muscle. Curr Opin Clin Nutr Metab Care 17:389–395

    Article  Google Scholar 

  7. Shirley IM, Blackwell R, Cusick G, Farman DJ, Vicary FR (1988) Ultrasound. In: Shirley IM, Blackwell R, Cusick G, Farman DJ, Vicary FR (eds) A user’s guide to diagnostic ultrasound. Pitman Medical Publishing Co LtD, London, pp 32–39

    Google Scholar 

  8. Heckmatt JZ, Leeman S, Dubowitz V (1982) Ultrasound imaging in the diagnosis of muscle disease. J Pediatr 101:656–660

    Article  CAS  Google Scholar 

  9. Scholten RR, Pillen S, Verrips A, Zwarts MJ (2003) Quantitative ultrasonography of skeletal muscles in children: normal values. Muscle Nerve 27:693–698

    Article  CAS  Google Scholar 

  10. Pillen S, van Keimpema M, Nievelstein RA, Verrips A, Kruijsbergen-Raijmann W, Zwarts MJ (2006) Skeletal muscle ultrasonography: visual versus quantitative evaluation. Ultrasound Med Biol 32:1315–1321

    Article  Google Scholar 

  11. Arts IMP, Pillen S, Schelhaas HJ, Overeem S, Zwarts MJ (2010) Normal values for quantitative muscle ultrasonography in adults. Muscle Nerve 41:32–41

    Article  Google Scholar 

  12. Jensen BR, Bakke M (1998) Prolonged work with shoulder muscles and other small muscle groups: use, function, and pain. In: Capodaglio P, Narici MV (eds) Muscle atrophy: disuse and disease. Le Collane della Fondazione Salvatore Maugeri, Pavia, pp 149–161

    Google Scholar 

  13. Nielsen PK, Jensen BR, Darvann T et al (2006) Quantitative ultrasound tissue characterization in supraspinatus and thigh muscles—a new approach. BMC Musculoskel Disord 7:2–13

    Article  Google Scholar 

  14. Pillen S (2010) Skeletal muscle ultrasound. Eur J Transl Myol 20(4):145–156

    Article  Google Scholar 

  15. Rivers WE, Garrigues D, Graciosa J, Harden RN (2015) Signs and symptoms of myofascial pain: an international survey of pain management providers and proposed preliminary set of diagnostic criteria. Pain Med 16(9):1794–1805

    Article  Google Scholar 

  16. Gerwin RD (2014) Diagnosis of myofascial pain syndrome. Phys Med Rehabil Clin N Am 25(2):341–355

    Article  Google Scholar 

  17. Tough EA, White AR, Richards S, Campbell J (2007) Variability of criteria used to diagnose myofascial trigger point pain syndrome—evidence from a review of the literature. Clin J Pain 23:278–286

    Article  Google Scholar 

  18. Turo D, Otto P, Shah JP, Heimur J, Gebreab T, Zaazhoa M, Armstrong K, Gerber LH, Sikdar S (2013) Ultrasonic characterization of the upper trapezius muscle in patients with chronic neck pain. Ultrason Imaging 35:173–187

    Article  Google Scholar 

  19. Shah JP, Thaker N, Heimur J, Aredo JV, Sikdar S, Gerber L (2015) Myofascial trigger points then and now: a historical and scientific perspective. PM&R 7(7):746–761

    Article  Google Scholar 

  20. Simons DG, Travell JG, Simons PT (1999) Travell and Simon’s myofascial pain and dysfunction: the trigger point manual, vol 1, 2nd edn. Williams and Wilkins, Baltimore

    Google Scholar 

  21. McDermid J, Gross AR, Galea V, McLaughlin LM, Parkinson W, Woodhouse L (2009) Developing biologically based assessment tools for physical therapy management of neck pain. J Orthop Sports Phys Ther 39(5):388–399

    Article  Google Scholar 

  22. Nielsen PK, Jensen BR, Darvann T, Jørgensen K, Bakke M (2000) Quantitative ultrasound image analyses of the supraspinatus muscle. Clin Biomech 15(Suppl 1):S13–S16

    Article  Google Scholar 

  23. Lindeberg T (1991) Discrete scale-space theory and the scale-space primal sketch (Doctoral dissertation, KTH Royal Institute of Technology)

  24. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59

    Article  Google Scholar 

  25. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  26. Myburgh C, Lauridsen HH, Larsen AH, Hartvigsen J (2011) Standardized manual palpation of myofascial trigger points in relation to neck/shoulder pain; the influence of clinical experience on inter-examiner reproducibility. Man Ther 16(2):136–140

    Article  Google Scholar 

  27. Rathbone AT, Grosman-Rimon L, Kumbhare DA (2017) Interrater agreement of manual palpation for identification of myofascial trigger points. Clin J Pain 33(8):715–729

    Article  Google Scholar 

  28. Pillen S, Arts IM, Zwarts MJ (2008) Muscle ultrasound in neuromuscular disorders. Muscle Nerve 37(6):679–693

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Medicine, Division of Physical Medicine and Rehabilitation, Toronto Rehabilitation Institute, 550 University Ave, Suite 7-131, Toronto, ON, M5G 2A2, Canada

    Dinesh Kumbhare

  2. Department of Psychology, McMaster University, Hamilton, Canada

    Saurabh Shaw

  3. Department of Electrical and Computer Engineering, McMaster University, Hamilton, Canada

    Michael D. Noseworthy

  4. University of Toronto, Toronto, Canada

    Sara Ahmed

Authors
  1. Dinesh Kumbhare
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  2. Saurabh Shaw
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  3. Sara Ahmed
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  4. Michael D. Noseworthy
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Correspondence to Dinesh Kumbhare.

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The authors and author institutions have no conflict of interest to declare. This includes financial or personal relationships, dual commitments, competing interests or competing loyalties.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Kumbhare, D., Shaw, S., Ahmed, S. et al. Quantitative ultrasound of trapezius muscle involvement in myofascial pain: comparison of clinical and healthy population using texture analysis. J Ultrasound 23, 23–30 (2020). https://doi.org/10.1007/s40477-018-0330-5

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  • DOI: https://doi.org/10.1007/s40477-018-0330-5

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