Advertisement

Imaging the Mechanical Properties of Porous Biological Tissue

  • John J. PitreJr.
  • Joseph L. Bull
Reference work entry

Abstract

Of the roughly 42 l of water contained in a normal 70 kg person, approximately a quarter makes up what is known as the interstitial fluid. This fluid permeates a dense porous network of proteins called the extracellular matrix. The nonlinear mechanical behavior of many tissues is, at least partly, a result of this porous structure. That is, many tissues are poroelastic. Abnormalities in the poroelastic properties of tissue are often indicative of disease states ranging from renal failure to traumatic brain injury to cancer. As such, it is of broad clinical interest to develop methods for measuring these abnormalities to help guide diagnosis and clinical decision-making. This chapter describes methods for imaging the poroelastic properties of biological tissue using ultrasound and magnetic resonance (MR). These poroelastography methods are derived from earlier work which focused on imaging linearly elastic properties of tissue, ignoring the porous nature of the tissue structure. Incorporating poroelastic tissue models into elasticity imaging makes it possible to image not only tissue stiffness but also fluid content, permeability, and internal pressure. This chapter will introduce the theory of poroelasticity, as described by both the Biot and KLM biphasic models, and discuss its role in the development of both quasi-static ultrasound poroelastography and time-harmonic MR poroelastography. For each method, the current state-of-the-art from both a technical perspective and a clinical perspective will be reviewed, offering insight into the continuing development of both technologies. The authors hope to leave the reader with a better understanding of the challenges faced by these methods as well as the role that advances in porous media science can play in improving medical imaging.

References

  1. Alam SK, Ophir J (1997) Reduction of signal decorrelation from mechanical compression of tissues by temporal stretching: applications to elastography. Ultrasound Med Biol 23(1):95–105CrossRefGoogle Scholar
  2. Alam SK, Ophir J, Konofagou EE (1998) An adaptive strain estimator for elastography. IEEE T Ultrason Ferroelectr 45(2):461–472CrossRefGoogle Scholar
  3. Armstrong CG, Lai WM, Mow VC (1984) An analysis of the unconfined compression of articular cartilage. J Biomech Eng 106(2):165–173CrossRefGoogle Scholar
  4. Barbone PE, Bamber JC (2002) Quantitative elasticity imaging: what can and cannot be inferred from strain images. Phys Med Biol 47(12):2147–2164CrossRefGoogle Scholar
  5. Bates D, Levick J, Mortimer P (1994) Quantification of rate and depth of pitting in human edema using an electric tonometer. Lymphology 27(4):159–172Google Scholar
  6. Belmont B, Dodde RE, Shih AJ (2013) Impedance of tissue-mimicking phantom material under compression. J Electr Bioimpedance 4(1):2–12Google Scholar
  7. Berry GP, Bamber JC, Armstrong CG, Miller NR, Barbone PE (2006a) Towards an acoustic model-based poroelastic imaging method: I. Theoretical foundation. Ultrasound Med Biol 32(4):547–567CrossRefGoogle Scholar
  8. Berry GP, Bamber JC, Miller NR, Barbone PE, Bush NL, Armstrong CG (2006b) Towards an acoustic model-based poroelastic imaging method: II. experimental investigation. Ultrasound Med Biol 32(12):1869–1885CrossRefGoogle Scholar
  9. Berry GP, Bamber JC, Mortimer PS, Bush NL, Miller NR, Barbone PE (2008) The spatio-temporal strain response of oedematous and nonoedematous tissue to sustained compression in vivo. Ultrasound Med Biol 34(4):617–629CrossRefGoogle Scholar
  10. Biot MA (1941) General theory of three-dimensional consolidation. J Appl Phys 12(2):155–164CrossRefGoogle Scholar
  11. Detournay E, Cheng AH-D (1993) Fundamentals of poroelasticity, Chapter 5. In: Fairhurst C (ed) Comprehensive rock engineering: principles, practice and projects, vol. II, analysis and design method. Pergamon Press, Oxford, pp 113–171Google Scholar
  12. Doyley MM, Meaney PM, Bamber JC (2000) Evaluation of an iterative reconstruction method for quantitative elastography. Phys Med Biol 45(6):1521–1540CrossRefGoogle Scholar
  13. Doyley MM, Srinivasan S, Pendergrass SA, Wu Z, Ophir J (2005) Comparative evaluation of strain-based and model-based modulus elastography. Ultrasound Med Biol 31(6):787–802CrossRefGoogle Scholar
  14. Field-II [Software]. J. A. Jensen, Technical University of Denmark. Available from http://field-ii.dk. Accessed 28 Oct 2016
  15. FOCUS: Fast Object-Oriented C++ Ultrasound Simulator [Software]. Michigan State University. Available from http://www.egr.msu.edu/~fultras-web/. Accessed 28 Oct 2016
  16. Goenezen S, Barbone PE, Oberai AA (2011) Solution of the nonlinear elasticity imaging inverse problem: the incompressible case. Comput Methods Appl M 200(13–16):1406–1420MathSciNetCrossRefGoogle Scholar
  17. Goenezen S, Dord J-F, Sink Z, Barbone PE, Jiang J, Hall TJ, Oberai AA (2012) Linear and nonlinear elastic modulus imaging: an application to breast cancer diagnosis. IEEE T Med Imaging 31(8):1628–1637CrossRefGoogle Scholar
  18. Hirsch S, Klatt D, Freimann F, Scheel M, Braun J, Sack I (2013a) In vivo measurement of volumetric strain in the human brain induced by arterial pulsation and harmonic waves. Magn Reson Med 70(3):671–682CrossRefGoogle Scholar
  19. Hirsch S, Beyer F, Guo J, Papazoglou S, Tzschaetzsch H, Braun J, Sack I (2013b) Compression-sensitive magnetic resonance elastography. Phys Med Biol 58(15):5287–5299CrossRefGoogle Scholar
  20. Hirsch S, Guo J, Reiter R, Schott E, Büning C, Somasundaram R, Braun J, Sack I, Kroencke TJ (2014) Towards compression-sensitive magnetic resonance elastography of the liver: Sensitivity of harmonic volumetric strain to portal hypertension. J Magn Reson Im 39(2):298–306CrossRefGoogle Scholar
  21. Hoskins P, Martin K, Thrush A (2010) Diagnostic ultrasound: physics and equipment, 2nd edn. Cambridge University Press, New York, p 147CrossRefGoogle Scholar
  22. Kallel F, Bertrand M (1996) Tissue elasticity reconstruction using linear perturbation method. IEEE T Med Imaging 15(3):299–313CrossRefGoogle Scholar
  23. Konofagou EE, Ophir J (1998) A new elastographic method for estimation and imaging of lateral displacements, lateral strains, corrected axial strains, and Poisson’s ratios in tissues. Ultrasound Med Biol 24(8):1183–1199CrossRefGoogle Scholar
  24. Konofagou EE, Harrigan TP, Ophir J, Krouskop TA (2001) Poroelasticity: imaging the poroelastic properties of tissues. Ultrasound Med Biol 27(10):1387–1397CrossRefGoogle Scholar
  25. Kuei SC, Lai WM, Mow VC (1978) A biphasic rheological model of articular cartilage. In: Eberhardt RC, Burstein AH (eds) Advances in bioengineering. American Society of Mechanical Engineers, New YorkGoogle Scholar
  26. Lindahl O (1995) The evaluation of a biexponential model for description of intercompartmental fluid shifts in compressed oedematous tissue. Physiol Meas 16:17–28CrossRefGoogle Scholar
  27. Lubinski MA, Emilianov SY, O’Donnell M (1999) Speckle tracking methods for ultrasonic elasticity imaging using short-time correlation. IEEE T Ultrason Ferroelectr 46(1):82–96CrossRefGoogle Scholar
  28. Mow VC, Kuei SC (1980) Biphasic creep and stress relaxation of articular cartilage in compression: theory and experiments. J Biomech Eng 102(Feb):73–84CrossRefGoogle Scholar
  29. Mridha M, Odman S (1986) Noninvasive method for the assessment of subcutaneous oedema. Med Biol Eng Comput 24(4):393–398CrossRefGoogle Scholar
  30. Oberai AA, Gokhale NH, Feijóo GR (2003) Solution of inverse problems in elasticity imaging using the adjoint method. Inverse Probl 19(2):297–313MathSciNetCrossRefGoogle Scholar
  31. Oberai AA, Gokhale NH, Doyley MM, Bamber JC (2004) Evaluation of the adjoint equation based algorithm for elasticity imaging. Phys Med Biol 49(13):2955–2974CrossRefGoogle Scholar
  32. O’Donnell M, Skovoroda AR, Shapo BM, Emilianov SY (1994) Internal displacement and strain imaging using ultrasonic speckle tracking. IEEE T Ultrason Ferroelectr 41(3):314–325CrossRefGoogle Scholar
  33. Ophir J, Cespedes I, Ponnekanti H, Yazdi Y, Li X (1991) Elastography: a quantitative method for imaging the elasticity of biological tissues. Ultrason Imaging 134:111–134CrossRefGoogle Scholar
  34. Perriñez PR, Kennedy FE, Van Houten EEW, Weaver JB, Paulsen KD (2009) Modeling of soft poroelastic tissue in time-harmonic MR elastography. IEEE T Biomed Eng 56(3):598–608CrossRefGoogle Scholar
  35. Perriñez PR, Kennedy FE, Van Houten EEW, Weaver JB, Paulsen KD (2010a) Magnetic resonance poroelastography: an algorithm for estimating the mechanical properties of fluid-saturated soft tissues. IEEE T Med Imaging 29(3):746–755CrossRefGoogle Scholar
  36. Perriñez PR, Pattison AJ, Kennedy FE, Weaver JB, Paulsen KD (2010b) Contrast detection in fluid-saturated media with magnetic resonance poroelastography. Med Phys 37(7):3518–3526CrossRefGoogle Scholar
  37. Petrank Y, Lingyun H, O’Donnell M (2009) Reduced peak-hopping artifacts in ultrasonic strain estimation using the Viterbi algorithm. IEEE T Ultrason Ferroelectr 56(7):1359–1367CrossRefGoogle Scholar
  38. Pitre JJ Jr, Koziol L, Kruger GH, Vollmer A, Ophir J, Ammann J, Weitzel WF, Bull JL (2016) Design and testing of a single-element ultrasound viscoelastography system for point-of-care edema quantification. Ultrasound Med Biol 42(9):2209–2219CrossRefGoogle Scholar
  39. Righetti R, Ophir J, Srinivasan S, Krouskop TA (2004) The feasibility of using elastography for imaging the Poisson’s ratio in porous media. Ultrasound Med Biol 30(2):215–228CrossRefGoogle Scholar
  40. Righetti R, Ophir J, Krouskop TA (2005a) A method for generating permeability elastograms and Poisson’s ratio time-constant elastograms. Ultrasound Med Biol 31(6):803–816CrossRefGoogle Scholar
  41. Righetti R, Ophir J, Garra BS, Chandrasekhar RM, Krouskop TA (2005b) A new method for generating poroelastograms in noisy environments. Ultrason Imaging 27(4):201–220CrossRefGoogle Scholar
  42. Righetti R, Righetti M, Ophir J, Krouskop TA (2007a) The feasibility of estimating and imaging the mechanical behavior of poroelastic materials using axial strain elastography. Phys Med Biol 52(11):3241–3259CrossRefGoogle Scholar
  43. Righetti R, Barra GS, Mobbs LM, Kraemer-Chant CM, Ophir J, Krouskop TA (2007b) The feasibility of using poroelastographic techniques for distinguishing between normal and lymphedematous tissues in vivo. Phys Med Biol 52(21):6525–6541CrossRefGoogle Scholar
  44. Samani A, Plewes D (2004) A method to measure the hyperelastic parameters of ex vivo breast tissue samples. Phys Med Biol 49(18):4395–4405CrossRefGoogle Scholar
  45. Simon BR (1992) Multiphase poroelastic finite element models for soft tissue structures. Appl Mech Rev 45(6):191–218MathSciNetCrossRefGoogle Scholar
  46. Skovoroda AR, Emilianov SY, Lubinski MA, Sarvazyan AP, O’Donnell M (1994) Theoretical analysis and verification of ultrasound displacement and strain imaging. IEEE T Ultrason Ferroelectr 41(3):303–313Google Scholar
  47. Skovoroda AR, Emilianov SY, O’Donnell M (1995) Tissue elasticity reconstruction based on ultrasonic displacement and strain images. IEEE T Ultrason Ferroelectr 42(4):747–765CrossRefGoogle Scholar
  48. Skovoroda AR, Lubinski MA, Emilianov SY, O’Donnell M (1999) Reconstructive elasticity imaging for large deformations. IEEE T Ultrason Ferroelectr 46(3):523–535CrossRefGoogle Scholar
  49. Terzaghi K (1923) Die berechnung der durchlassigkeitsziffer des tones aus dem verlauf der hydrodynamischen spannungserscheinungen. Mathematish-Naturwissenschaftiliche Klasse 132:125–138Google Scholar
  50. Varghese T, Ophir J (1997a) A theoretical framework for performance characterization of elastography: the strain filter. IEEE T Ultrason Ferroelectr 44(1):164–172CrossRefGoogle Scholar
  51. Varghese T, Ophir J (1997b) Enhancement of echo-signal correlation in elastography using temporal stretching. IEEE T Ultrason Ferroelectr 44(1):173–180CrossRefGoogle Scholar
  52. Varghese T, Ophir J, Cespedes I (1996) Noise reduction in elastograms using temporal stretching with multicompression averaging. Ultrasound Med Biol 22(8):1043–1052CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Biomedical Engineering DepartmentTulane UniversityNew OrleansUSA

Personalised recommendations