Skip to main content
SpringerLink
Log in

Elastography mapped by deep convolutional neural networks

  • Article
  • Published:
Science China Technological Sciences Aims and scope Submit manuscript

Abstract

Elastography emerges as a medical modality to map stiffness distribution of tissues and is expected to help identify malignant tumors. To this end, tissues are externally stimulated with dynamic waves, and thereafter mechanical responses are internally measured. However, internal measurements limit the resolution and accuracy due to wave scattering and frequency-dependence. Although models have been reported only with need for acquiring transmitted responses, the computational processes are time-consuming in the inverse analysis. Here we develop an architecture of deep learning-based convolutional neural networks (CNNs) to image elastography based on sound transmission. The proposed CNNs contain three branches, one of which considers the contribution of original features in input data. By comparison, the developed architecture not only maps elastography accurately, but also is more efficient than traditional CNNs in sequence.

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.

Institutional subscriptions

Similar content being viewed by others

References

  1. Lerner R M, Huang S R, Parker K J. “Sonoelasticity” images derived from ultrasound signals in mechanically vibrated tissues. Ultrasound Med Biol, 1990, 16: 231–239

    Article  Google Scholar 

  2. Ophir J, Céspedes I, Ponnekanti H, et al. Elastography: A quantitative method for imaging the elasticity of biological tissues. Ultrason Imag, 1991, 13: 111–134

    Article  Google Scholar 

  3. Muthupillai R, Lomas D J, Rossman P J, et al. Magnetic resonance elastography by direct visualization of propagating acoustic strain waves. Science, 1995, 269: 1854–1857

    Article  Google Scholar 

  4. Manduca A, Oliphant T E, Dresner M A, et al. Magnetic resonance elastography: Non-invasive mapping of tissue elasticity. Med Image Anal, 2001, 5: 237–254

    Article  Google Scholar 

  5. Y. Liu, G. Wang, L. Z. Sun. Anisotropic elastography for local passive properties and active contractility of myocardium from dynamic heart imaging sequence.Int J Biomed Imaging, 2006, 2006: 45957

    Article  Google Scholar 

  6. Liu D, Hu Z, Wang G, et al. Sound transmission-based elastography imaging. IEEE Access, 2019, 7: 74383–74392

    Article  Google Scholar 

  7. Liu D, Sun L. Elastography mapped by untangling compressional and shear deformation. Extreme Mech Lett, 2020, 36: 100669

    Article  Google Scholar 

  8. Mariappan Y K, Glaser K J, Ehman R L. Magnetic resonance elastography: A review. Clin Anat, 2010, 23: 497–511

    Article  Google Scholar 

  9. Duck F A. Physical Properties of Tissue: A Comprehensive Reference Book. New York: Academic Press, 1990

    Google Scholar 

  10. Garra B S. Elastography: History, principles, and technique comparison. Abdom Imag, 2015, 40: 680–697

    Article  Google Scholar 

  11. Parker K J, Doyley M M, Rubens D J. Corrigendum: Imaging the elastic properties of tissue: The 20 year perspective. Phys Med Biol, 2012, 57: 5359–5360

    Article  Google Scholar 

  12. Dewall R J. Ultrasound elastography: Principles, techniques, and clinical applications. Crit Rev Biomed Eng, 2013, 41: 1–19

    Article  Google Scholar 

  13. Zaleska-Dorobisz U, Kaczorowski K, Pawluś A, et al. Ultrasound elastography—Review of techniques and its clinical applications. Adv Clin Exp Med, 2014, 23: 645–655

    Article  Google Scholar 

  14. Sigrist R M S, Liau J, Kaffas A E, et al. Ultrasound elastography: Review of Techniques and clinical applications. Theranostics, 2017, 7: 1303–1329

    Article  Google Scholar 

  15. Hamilton T J, Bailat C, Gehring S, et al. X-ray elastography: Modification of X-ray phase contrast images using ultrasonic radiation pressure. J Appl Phys, 2009, 105: 102001

    Article  Google Scholar 

  16. Parker K J, Ormachea J, Zvietcovich F, et al. Reverberant shear wave fields and estimation of tissue properties. Phys Med Biol, 2017, 62: 1046–1061

    Article  Google Scholar 

  17. Racedo J, Urban M W. Evaluation of reconstruction parameters for 2-D comb-push ultrasound shear wave elastography. IEEE Trans Ultrason Ferroelect Freq Contr, 2019, 66: 254–263

    Article  Google Scholar 

  18. Doyley M M. Model-based elastography: A survey of approaches to the inverse elasticity problem. Phys Med Biol, 2012, 57: R35–R73

    Article  Google Scholar 

  19. Graff K F. Wave Motion in Elastic Solids. New York: Dover Publications, 1991

    MATH  Google Scholar 

  20. Sarvazyan A P, Rudenko O V, Swanson S D, et al. Shear wave elasticity imaging: A new ultrasonic technology of medical diagnostics. Ultrasound Med Biol, 1998, 24: 1419–1435

    Article  Google Scholar 

  21. Tapper E B, Loomba R. Noninvasive imaging biomarker assessment of liver fibrosis by elastography in NAFLD. Nat Rev Gastroenterol Hepatol, 2018, 15: 274–282

    Article  Google Scholar 

  22. Van Houten E E W, Miga M I, Weaver J B, et al. Three-dimensional subzone-based reconstruction algorithm for MR elastography. Magn Reson Med, 2001, 45: 827–837

    Article  Google Scholar 

  23. Cao Y, Zheng Y, Li G Y, et al. Elastodiagnosis of diseases: A review. Extreme Mech Lett, 2019, 27: 102–123

    Article  Google Scholar 

  24. Weaver J B, van Houten E E W, Miga M I, et al. Magnetic resonance elastography using 3D gradient echo measurements of steady-state motion. Med Phys, 2001, 28: 1620–1628

    Article  Google Scholar 

  25. Zorgani A, Souchon R, Dinh A H, et al. Brain palpation from physiological vibrations using MRI. Proc Natl Acad Sci USA, 2015, 112: 12917–12921

    Article  Google Scholar 

  26. Ji L, McLaughlin J R, Renzi D, et al. Interior elastodynamics inverse problems: Shear wave speed reconstruction in transient elastography. Inverse Problems, 2003, 19: S1–S29

    Article  MathSciNet  MATH  Google Scholar 

  27. Malandrino A, Mak M, Kamm R D, et al. Complex mechanics of the heterogeneous extracellular matrix in cancer. Extreme Mech Lett, 2018, 21: 25–34

    Article  Google Scholar 

  28. Deffieux T, Gennisson J L, Bercoff J, et al. On the effects of reflected waves in transient shear wave elastography. IEEE Trans Ultrason Ferroelect Freq Contr, 2011, 58: 2032–2035

    Article  Google Scholar 

  29. Manduca A, Lake D S, Kruse S A, et al. Spatio-temporal directional filtering for improved inversion of MR elastography images. Med Image Anal, 2003, 7: 465–473

    Article  Google Scholar 

  30. Montgomery D C. Design and Analysis of Experiments. 5th ed. New York: John Wiley & Sons, Inc., 2000

    Google Scholar 

  31. Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems. 2012, 1097–1105

  32. Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks, 2015, 61: 85–117

    Article  Google Scholar 

  33. Erickson B J, Korfiatis P, Akkus Z, et al. Machine learning for medical imaging. RadioGraphics, 2017, 37: 505–515

    Article  Google Scholar 

  34. Yoo J, Wahab A, Ye J C. A mathematical framework for deep learning in elastic source imaging. SIAM J Appl Math, 2018, 78: 2791–2818

    Article  MathSciNet  MATH  Google Scholar 

  35. Pelt D M, Sethian J A. A mixed-scale dense convolutional neural network for image analysis. Proc Natl Acad Sci USA, 2018, 115: 254–259

    Article  MathSciNet  Google Scholar 

  36. Ameri A, Akhaee M A, Scheme E, et al. Regression convolutional neural network for improved simultaneous EMG control. J Neural Eng, 2019, 16: 036015

    Article  Google Scholar 

  37. Fu X, Zhang C, Peng X, et al. Towards end-to-end pulsed eddy current classification and regression with CNN. In: Proceedings of the 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). Auckland, 2019. 1–5

  38. Goodfellow I, Bengio Y, Courvile A. Deep Learning. Cambridge: MIT Press, 2016

    MATH  Google Scholar 

  39. Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives. IEEE Trans Pattern Anal Mach Intell, 2013, 35: 1798–1828

    Article  Google Scholar 

  40. Murphy M C, Manduca A, Trzasko J D, et al. Artificial neural networks for stiffness estimation in magnetic resonance elastography. Magn Reson Med, 2018, 80: 351–360

    Article  Google Scholar 

  41. Raissi M, Perdikaris P, Karniadakis G E. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys, 2019, 378: 686–707

    Article  MathSciNet  MATH  Google Scholar 

  42. Liu T, Chen M, Zhou M, et al. Towards understanding the importance of shortcut connections in residual networks. arXiv: 1909.04653

  43. Budday S, Sommer G, Birkl C, et al. Mechanical characterization of human brain tissue. Acta Biomater, 2017, 48: 319–340

    Article  Google Scholar 

  44. Kingma D P, Ba J. Adam: A method for stochastic optimization. arXiv: 1412.6980

Download references

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of California, Irvine, CA, 92697, USA

    DongXu Liu & LiZhi Sun

  2. Department of Biomedical Engineering, University of California, Irvine, CA, 92697, USA

    Frithjof Kruggel

Authors
  1. DongXu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frithjof Kruggel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. LiZhi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to LiZhi Sun.

Additional information

This work was supported by the US National Science Foundation (Grant No. CMMI-1229405), and the University of California, Irvine-Initiative to End Family Violence (UCI-IEFL) Program on Modeling Brain Trauma in Children.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, D., Kruggel, F. & Sun, L. Elastography mapped by deep convolutional neural networks. Sci. China Technol. Sci. 64, 1567–1574 (2021). https://doi.org/10.1007/s11431-020-1726-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11431-020-1726-5

Keywords

Search

Navigation