Skip to main content

Advertisement

SpringerLink
Log in

Automatic Detection of Contracting Muscle Regions via the Deformation Field of Transverse Ultrasound Images: A Feasibility Study

  • Original Article
  • Published:
Annals of Biomedical Engineering Aims and scope Submit manuscript

Abstract

Accurate identification of contracting muscles can help us to understand the muscle function in both physiological and pathological conditions. Conventional electromyography (EMG) have limited access to deep muscles, crosstalk, or instability in the recordings. Accordingly, a novel framework was developed to detect contracting muscle regions based on the deformation field of transverse ultrasound images. We first estimated the muscle movements in a stepwise calculation, to derive the deformation field. We then calculated the divergence of the deformation field to locate the expanding or shrinking regions during muscle contractions. Two preliminary experiments were performed to evaluate the feasibility of the developed algorithm. Using concurrent intramuscular EMG recordings, Experiment I verified that the divergence map can capture the activity of superficial and deep muscles, when muscles were activated voluntarily or through electrical stimulation. Experiment II verified that the divergence map can only capture contracting muscles but not muscle shortening during passive movements. The results demonstrated that the divergence can individually capture the activity of muscles at different depths, and was not sensitive to muscle shortening during passive movements. The proposed framework can automatically detect the regions of contracting muscle, and could potentially serve as a tool to assess the functions of a group of muscles concurrently.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

Abbreviations

sEMG:

Surface electromyography

iEMG:

Intramuscular electromyography

US:

Ultrasound

DIP:

Distal interphalangeal

PIP:

Proximal interphalangeal

FDP:

Flexor digitorum profundus

FDS:

Flexor digitorum superficialis

RMS:

Root mean square

fps:

Frames per second

Def.:

Deformation

Div.:

Divergence

References

  1. Akhlaghi, N., C. A. Baker, M. Lahlou, H. Zafar, K. G. Murthy, H. S. Rangwala, J. Kosecka, W. M. Joiner, J. J. Pancrazio, and S. Sikdar. Real-time classification of hand motions using ultrasound imaging of forearm muscles. IEEE Trans. Biomed. Eng. 63:1687–1698, 2015.

    Article  Google Scholar 

  2. Biały, M., W. Adamczyk, R. Gnat, and T. Stranc. Tissue deformation index as a reliable measure of lateral abdominal muscle activation on m-mode sonography. J. Ultrasound Med. 36:1461–1467, 2017.

    Article  Google Scholar 

  3. Bogaerts, S., C. D. B. Carvalho, L. Scheys, K. Desloovere, J. D’hooge, F. Maes, P. Suetens, and K. Peers. Evaluation of tissue displacement and regional strain in the achilles tendon using quantitative high-frequency ultrasound. PLoS ONE 12:e0181364, 2017.

    Article  Google Scholar 

  4. Bunce, S., A. Moore, and A. Hough. M-mode ultrasound: a reliable measure of transversus abdominis thickness? Clin. Biomech. 17:315–317, 2002.

    Article  CAS  Google Scholar 

  5. Carpinella, I., J. Jonsdottir, and M. Ferrarin. Multi-finger coordination in healthy subjects and stroke patients: a mathematical modelling approach. J. Neuroeng. Rehabil. 8:19, 2011.

    Article  Google Scholar 

  6. Castellini, C., G. Passig, and E. Zarka. Using ultrasound images of the forearm to predict finger positions. IEEE Trans. Neural Syst. Rehab. Eng. 20:788–797, 2012.

    Article  Google Scholar 

  7. Chen, X., Y.-P. Zheng, J.-Y. Guo, Z. Zhu, S.-C. Chan, and Z. Zhang. Sonomyographic responses during voluntary isometric ramp contraction of the human rectus femoris muscle. Eur. J. Appl. Physiol. 112:2603–2614, 2012.

    Article  Google Scholar 

  8. Chleboun, G. S., A. R. France, M. T. Crill, H. K. Braddock, and J. N. Howell. In vivo measurement of fascicle length and pennation angle of the human biceps femoris muscle. Cells Tissues Organs 169:401–409, 2001.

    Article  CAS  Google Scholar 

  9. Debaere, F., D. Van Assche, C. Kiekens, S. Verschueren, and S. Swinnen. Coordination of upper and lower limb segments: deficits on the ipsilesional side after unilateral stroke. Exp. Brain Res. 141:519–529, 2001.

    Article  CAS  Google Scholar 

  10. Gijsbertse, K., R. Goselink, S. Lassche, M. Nillesen, A. Sprengers, N. Verdonschot, N. van Alfen, and C. De Korte. Ultrasound imaging of muscle contraction of the tibialis anterior in patients with facioscapulohumeral dystrophy. Ultrasound Med. Biol. 43:2537–2545, 2017.

    Article  Google Scholar 

  11. Gooch, C. L., T. J. Doherty, K. M. Chan, M. B. Bromberg, R. A. Lewis, D. W. Stashuk, M. J. Berger, M. T. Andary, and J. R. Daube. Motor unit number estimation: a technology and literature review. Muscle Nerve 50:884–893, 2014.

    Article  Google Scholar 

  12. Guo, J.-Y., Y.-P. Zheng, H.-B. Xie, and X. Chen. Continuous monitoring of electromyography (emg), mechanomyography (mmg), sonomyography (smg) and torque output during ramp and step isometric contractions. Med. Eng. Phys. 32:1032–1042, 2010.

    Article  Google Scholar 

  13. Kamavuako, E. N., D. Farina, K. Yoshida, and W. Jensen. Estimation of grasping force from features of intramuscular emg signals with mirrored bilateral training. Ann. Biomed. Eng. 40:648–656, 2012.

    Article  Google Scholar 

  14. Kim, C.-Y., J.-D. Choi, S.-Y. Kim, D.-W. Oh, J.-K. Kim, and J.-W. Park. Comparison between muscle activation measured by electromyography and muscle thickness measured using ultrasonography for effective muscle assessment. J. Electromyogr. Kinesiol. 24:614–620, 2014.

    Article  Google Scholar 

  15. Mademli, L., and A. Arampatzis. Behaviour of the human gastrocnemius muscle architecture during submaximal isometric fatigue. Eur. J. Appl. Physiol. 94:611–617, 2005.

    Article  Google Scholar 

  16. McMeeken, J., I. Beith, D. Newham, P. Milligan, and D. Critchley. The relationship between emg and change in thickness of transversus abdominis. Clin. Biomech. 19:337–342, 2004.

    Article  CAS  Google Scholar 

  17. Mogk, J. P., and P. J. Keir. Crosstalk in surface electromyography of the proximal forearm during gripping tasks. J. Electromyogr. Kinesiol. 13:63–71, 2003.

    Article  Google Scholar 

  18. Nelson, C. M., W. M. Murray, and J. P. A. Dewald. Motor impairment-related alterations in biceps and triceps brachii fascicle lengths in chronic hemiparetic stroke. Neurorehabil. Neural Repair 32:799–809, 2018.

    Article  Google Scholar 

  19. Perry, J., C. S. Easterday, and D. J. Antonelli. Surface versus intramuscular electrodes for electromyography of superficial and deep muscles. Phys. Ther. 61:7–15, 1981.

    Article  CAS  Google Scholar 

  20. Revell, J., M. Mirmehdi, and D. McNally. Musculoskeletal motion flow fields using hierarchical variable-sized block matching in ultrasonographic video sequences. J. Biomech. 37:511–522, 2004.

    Article  CAS  Google Scholar 

  21. Shi, J., J.-Y. Guo, S.-X. Hu, and Y.-P. Zheng. Recognition of finger flexion motion from ultrasound image: a feasibility study. Ultrasound Med. Biol. 38:1695–1704, 2012.

    Article  Google Scholar 

  22. Shin, H., Y. Zheng, and X. Hu. Variation of finger activation patterns post-stroke through non-invasive nerve stimulation. Front. Neurol. 9:1101, 2018.

    Article  Google Scholar 

  23. Simon, N. G. Dynamic muscle ultrasound-another extension of the clinical examination. Clinical neurophysiology: official journal of the International Federation of Clinical Neurophysiology 126:1466, 2015.

    Article  Google Scholar 

  24. Simon, N. G., Y.-I. Noto, and C. M. Zaidman. Skeletal muscle imaging in neuromuscular disease. J. Clin. Neurosci. 33:1–10, 2016.

    Article  Google Scholar 

  25. Smith, L. H., T. A. Kuiken, and L. J. Hargrove. Real-time simultaneous and proportional myoelectric control using intramuscular emg. J. Neural Eng. 11:066013, 2014.

    Article  Google Scholar 

  26. Solomonow, M., R. Baratta, M. Bernardi, B. Zhou, Y. Lu, M. Zhu, and S. Acierno. Surface and wire emg crosstalk in neighbouring muscles. J. Electromyogr. Kinesiol. 4:131–142, 1994.

    Article  CAS  Google Scholar 

  27. Stokes, I. A., S. M. Henry, and R. M. Single. Surface emg electrodes do not accurately record from lumbar multifidus muscles. Clin. Biomech. 18:9–13, 2003.

    Article  Google Scholar 

  28. Thirion, J.-P. Image matching as a diffusion process: an analogy with maxwell’s demons. Med. Image Anal. 2:243–260, 1998.

    Article  CAS  Google Scholar 

  29. van der Werff, R., S. O’Leary, G. Jull, M. Peolsson, J. Trygg, and A. Peolsson. A speckle tracking application of ultrasound to evaluate activity of multilayered cervical muscles. J. Rehabil. Med. 46:662–667, 2014.

    Article  Google Scholar 

  30. Vercauteren, T., X. Pennec, A. Perchant, and N. Ayache. Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 45:S61–S72, 2009.

    Article  Google Scholar 

  31. Zheng, Y., and X. Hu. Reduced muscle fatigue using kilohertz-frequency subthreshold stimulation of the proximal nerve. J. Neural Eng. 15:066010, 2018.

    Article  Google Scholar 

  32. Zheng, Y., and X. Hu. Elicited finger and wrist extension through transcutaneous radial nerve stimulation. IEEE Trans. Neural Syst. Rehab. Eng. 27:1875–1882, 2019.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

This study was supported in part by the National Science Foundation (CBET-1847319).

Conflict of interest

The authors have no financial relationships that may cause a conflict of interest.

Author information

Author notes

  1. Yang Zheng and Henry Shin have contributed equally to the study.

Authors and Affiliations

  1. Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, 116 Manning Drive, 10206B Mary Ellen Jones Bldg, Chapel Hill, NC, 27599-7575, USA

    Yang Zheng, Henry Shin, Derek G. Kamper & Xiaogang Hu

Authors
  1. Yang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Henry Shin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Derek G. Kamper
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaogang Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaogang Hu.

Additional information

Associate Editor Joel Stitzel oversaw the review of this article.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 261 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, Y., Shin, H., Kamper, D.G. et al. Automatic Detection of Contracting Muscle Regions via the Deformation Field of Transverse Ultrasound Images: A Feasibility Study. Ann Biomed Eng 49, 354–366 (2021). https://doi.org/10.1007/s10439-020-02557-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10439-020-02557-2

Keywords

Search

Navigation