Advertisement

Journal of Medical Ultrasonics

, Volume 29, Issue 3, pp 119–128 | Cite as

Real time tissue elasticity imaging using the combined autocorrelation method

  • Tsuyoshi Shiina
  • Naotaka Nitta
  • Ei Ueno
  • Jeffrey C. Bamber
Translation of Original Contribution

Abstract

The elastic properties of tissues are expected to provide novel information for use in diagnosing pathologic changes in tissues and discriminating between malignant and benign tumors. Because it is hard to directly estimate the elastic modulus distribution from echo signals, methods for imaging the distribution of tissue strain under static compression are being widely investigated. Imaging the distribution of strain has proven to be useful for detecting disease tissues on the basis of their differences in elastic properties, although it is more qualitative than elastic modulus distribution. Many approaches to obtaining strain images from echo signals have been proposed. Most of these approaches use the spatial correlation technique, a method of detecting tissue displacement that provides maximum correlation between the echo signal obtained before and the one obtained after compression. Those methods are not suited for real-time processing, however, because of the amount of computation time they require. An alternative approach is a phase-tracking method, which is analogous to Doppler blood flowmetry. Although it can realize the rapid detection of displacement, the aliasing effect prevents its application to the large displacements that are necessary to improve the S/N ratio of the strain image. We therefore developed a more useful technique for imaging tissue elasticity. This approach, which we call the combined autocorrelation (CA) method, has the advantages of producing strain images of high quality with real-time processing and being applicable to large displacements.

Numeric simulation and phantom experimentation have demonstrated that this method's capability to reconstruct images of tissue strain distribution under practical conditions is superior to that of the conventional spatial correlation method. In simulation and phantom experimentation, moreover, the image of elastic modulus distribution was also obtained by estimating stress distribution using a three-dimensional tissue model. When the proposed CA method was used to measure breast tumor specimens, the obtained strain images clearly revealed harder tumor lesions that were only vaguely resolved in B-mode images. Moreover, the results indicated the possibility of extracting the pathological characteristics of a tumor, making it useful for determining tumor type. These advantages justify the clinical use of the CA method.

Keywords

combined autocorrelation method real-time processing strain mapping tissue elasticity imaging tumor discrimination based on tissue elasticity 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1).
    E. Ueno, E. Tohno and S. Soeda: Dynamic Tests in Real-Time Breast Echography,Ultrasound Med Biol 1988;14 (Suppl): 53–57.PubMedCrossRefGoogle Scholar
  2. 2).
    J. B. Fowlkes, S. Yemelyanov, J. G. Pipe, et al: Possibility of cancer detection by means of measurement of elastic properties,Radiology 1992;185 (P): 206–207.Google Scholar
  3. 3).
    J. Ophir, I. Cespedes, H. Ponnekanti, et al: Elastography: A quantitative method for imaging the elasticity of biological tissues,Ultrason Imaging 1991;13: 111–134.PubMedCrossRefGoogle Scholar
  4. 4).
    L. N. Bohs and G. E. Trathy: A Novel Method for Angle Independent Ultrasonic Imaging of Blood Flow and Tissue Motion,IEEE Trans BME 1991;38: 280–286.Google Scholar
  5. 5).
    M. O'Donnell, A. R. Skovoroda and B. M. Shapo: Measurement of arterial wall motion using Fourier based speckle tracking algorithms,Proc IEEE Ultrason Symp 1991; 1101–1104.Google Scholar
  6. 6).
    A. R. Skovoroda, S. Y. Emelianov, M. A. Lubinski, et al: Theoretical Analysis and Verification of Ultrasound Displacement and Strain Imaging,IEEE Trans UFFC 1994;41: 302–313.Google Scholar
  7. 7).
    M. O'Donnell, A. R. Skovoroda, B. M. Shapo, et al: Internal Displacement and Strain Imaging using Ultrasonic Speckle Tracking,IEEE Trans UFFC 1994;41: 314–325.Google Scholar
  8. 8).
    S. Yagi and K. Nakayama: Local displacement analysis of inhomogeneous soft tissue by spatial correlation of rf echo signals,Proc of 1988 World Federation for Ultrasound in Medicine Biology, Washington DC. Oct., 1988; 113.Google Scholar
  9. 9).
    Y. Yamashita and M. Kubota: Ultrasonic characterization of tissue hardness in the in vivo human liver.Proc IEEE Ultrason Symp 1994; 1449–1453.Google Scholar
  10. 10).
    C. Sumi, A. Suzuki and K. Nakayama: Phantom experiment on estimation of shear modulus distribution in soft tissue from ultrasonic measurement of displacement vector field,IEICE Trans. Fundamentals, 1997; E78-A;12: 1655–1664.Google Scholar
  11. 11).
    S. Y. Emelianov, A. R. Skovoroda, M. A. Lubinski, et al: Reconstructive elasticity imaging.Acoustical Imaging 1995;21: 241–252.Google Scholar
  12. 12).
    A. J. Romano, J. J. Shirron and J. A. Bucaro: On the Noninvasive Determination of Material Parameters from a Knowledge of Elastic Displacements: Theory and Numerical Simulation.IEEE Trans UFFC 1998;45: 751–759.Google Scholar
  13. 13).
    N. Nitta, T. Shiina: Ultrasonic Tissue Elasticity Imaging and its Application to Tumor Detection,Proc of the 1998 spring meeting of the acoustical society of Japan, 1998; 925–926. [in Japanese].Google Scholar
  14. 14).
    R. M. Lerner, S. R. Huang and K. J. Parker: ‘Sonoelasticity’ Images Derived from Ultrasound Signals in Mechanically Vibrated Tissues.Ultrasound Med Biol 1990;16: 231–239.PubMedCrossRefGoogle Scholar
  15. 15).
    Y. Yamakoshi, J. Sato and T. Sato: Ultrasonic Imaging of Internal Vibration of Soft Tissue under Forced Vibration.IEEE Trans UFFC 1990;37: 45–53.Google Scholar
  16. 16).
    T. Shiina, M. M. Doyley and J. C. Bamber: Strain imaging using combined RF and envelope autocorrelation processing.Proc IEEE Ultrason Symp 1996; 1331–1334.Google Scholar
  17. 17).
    T. Shiina: Imaging of Tissue Strain Distribution by Combined Autocorrelation Processing,J. Med Ultrasonics 1997;24(3): 343. [in Japanese]Google Scholar
  18. 18).
    C. Kasai, K. Namekawa, A. Koyano, et al: Real-Time Two-Dimensional Blood Flow Imaging Using Autocorrelation Technique,IEEE Trans Sonics Ultrason 1985; SU-32: 458–464.Google Scholar
  19. 19).
    M. Yamakawa, T. Shiina: Evaluation of a Method for Ultrasonic Elasticity Imaging using 3-D Tissue Model,Proc. of the 1998 spring meeting of the acoustical society of Japan, 1998; 923–924. [in Japanese]Google Scholar

Copyright information

© The Japan Society of Ultrasonics in Medicine 2002

Authors and Affiliations

  • Tsuyoshi Shiina
    • 1
  • Naotaka Nitta
    • 1
  • Ei Ueno
    • 2
  • Jeffrey C. Bamber
    • 3
  1. 1.Institute of Information Sciences and ElectronicsUniversity of TsukubaIbarakiJapan
  2. 2.Institute of Clinical MedicineUniversity of TsukubaIbarakiJapan
  3. 3.The Institute of Cancer ResearchRoyal Marsden NHS TrustSuttonUK

Personalised recommendations