Advertisement

Spatio-Temporal Directional Analysis of Real-Time Three Dimensional Cardiac Ultrasound

  • Elsa Angelini
  • Andrew Laine
Part of the Computational Imaging and Vision book series (CIVI, volume 19)

Abstract

Among screening modalities, echocardiography is the fastest, least expensive, and least invasive method for imaging the heart. Recently a new generation of technology has been developed for real-time 3D (RT3D) transducers, where an entire cardiac volume may be acquired instantaneously. Such transducers no longer require volumetric reconstruction, as with rotational and freehand 3D ultrasound acquisition systems. While, the wealth of information acquired by RT3D transducers is by far greater than any other echocardiography screening modality, on the other hand, echoes acquired with RT3D systems have low spatial resolution in the short axis plane and a high level of speckle noise embedded in the signal. We have developed a multidimensional spatio-temporal denoising/enhancement tool using brushlet basis functions to characterize ultrasound data in terms of oriented texture components, which decorrelate non-coherent speckle noise in the frequency domain. A redundant brushlet expansion exploits spatial and temporal coherence to identify persistent cardiac structures while removing uncorrelated speckle noise components. Denoising performance is evaluated quantitatively on phantom volumes and qualitatively on clinical RT3D patient data. We show in this study that brushlet analysis is well adapted to the intrinsic nature of RT3D ultrasound data and outperforms traditional denoising methods. We also show that by incorporating the time dimension directly in the analysis, we can bring into play temporal coherence between successive frames to improve denoising performance and enhance cardiac boundaries and structure.

Keywords

Wavelet Packet Fourier Domain Speckle Noise Soft Thresholding Hard Thresholding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abbott, J., and Thurstone, F. (1979). Acoustic speckle: Theory and experimental analysis. Ultrasonic Imaging, vol. 1, pp. 303–324.CrossRefGoogle Scholar
  2. Angelini, E., Laine, A., Takuma, S., and Homma, S. (1999). Directional representations of 4D echocardiography for temporal quantification of LV volumes. Proc. of Medical Imaging and ComputerAssisted Intervention - MICCAI’99, Cambridge, England, pp. 430–440.Google Scholar
  3. Angelini, E., Takuma, S., Laine, A., and Homma, S. (2000a). Spatio-temporal directional analysis of 4D echocardiography. Proceedings of SPIE 45th Annual Meeting, San Diego, pp. 605–614.Google Scholar
  4. Angelini, E., Takuma, S., Laine, A., and Homma, S. (2000b). Quantification of LV volumes with 4D real-time echocardiography. In: Proceedings of the World Congress on Medical Physics and Biomedical Engineering, Chicago, 2000.Google Scholar
  5. Ausher, P., Weiss, G., and Wickerhauser, M. (1992). Local sine and cosine bases of Coifman and Meyer and the construction of smooth wavelets. In: Wavelets- A tutorial in Theory and Applications, vol. 2, Wavelet Analysis and its Applications, C. K. Chui, Ed., San Diego, Academic Press, pp. 237–256.Google Scholar
  6. Coifman, R., and Donoho, D. (1995). Translation invariant denoising. In: Wavelet and Statistics, Springer-Verlag, Lecture Notes.Google Scholar
  7. Donoho, D., and Johnstone, I. (1992). Ideal spatial adaptation by wavelet shrinkage. Technical Report, Statistics Department, Stanford University.Google Scholar
  8. Donoho, D., and Johnstone, I. (1994a). Ideal denoising in an orthonormal basis chosen from a library of bases. Technical Report, Statistics Department, Stanford University.Google Scholar
  9. Donoho, D., and Johnstone, I. (1994b). Threshold selection for wavelet shrinkage of noisy data. Proceedings of 16th Annual Int. Conf of the IEEE Engineering in Medicine and Biology Society, pp. A24–25.Google Scholar
  10. Donoho, D. (1995).De-noising by soft-thresholding. IEEE Transactions on Information Theory, vol. 41, pp. 613–627.MathSciNetzbMATHCrossRefGoogle Scholar
  11. Dutt, V. (1995). Statistical Analysis of Ultrasound Echo Envelope. In: Ultrasound Research Laboratory. Rochester, MN: Mayo Foundation, pp. 181.Google Scholar
  12. Geiser, E., Wilson, D., Gibby, G., Billet, J., and Conetta, D. (1991). A method for evaluation of enhancement operations in two-dimensional echocardiographic images. Journal of the American Society of Echocardiography, vol. 4, pp. 235- 246 .Google Scholar
  13. Gopal, A., Schnellbaecher, M., Shen, Z., Sciacca, R., Keller, A., Sapin, P., and King, D. (1996). Serial assessment of left ventricular mass regression by 3D echocardiography requires three-fold fewer subjects compared to conventional 1D and 2D echocardiography. Journal of American College of Cardiology, vol. 27, pp. 150A.CrossRefGoogle Scholar
  14. Hao, X., Gao, S., and Gao, X. (1999). A novel multiscale nonlinear thresholding method for ultrasonic speckle suppressing. IEEE Transactions on Medical Imaging, vol. 18, pp. 787–794.CrossRefGoogle Scholar
  15. Herlin, I., and Ayache, N. (1992). Feature extraction and analysis methods for sequences of ultrasound images. Proceedings of ECCV, pp. 43–57.Google Scholar
  16. Jiang, L., Siu, S., Handshumacher, M., Guererro, J., Prada, J., King, M., Picard, M., Weyman, A., and Levine, R. (1994). Threedimensional echocardiography: In vivo validation for right ventricular volume and function. Circulation, vol. 2342–2350, pp 2342–2350.CrossRefGoogle Scholar
  17. Lester, M., Brenner, J., and Selles, W. (1980). Local transforms for biomedical image analysis. Computer Graphics, Image Processing, vol. 13, pp. 17–30.CrossRefGoogle Scholar
  18. Loupas, T., Mcdicken, W., and Allan, P. (1989). An adaptive weighted median filtering for speckle suppression in medical ultrasonic images. IEEE Transactions on Circuits and Systems, vol. 36, pp. 129–135.CrossRefGoogle Scholar
  19. Malvar, H. (1991). Signal Processing with Lapped Transforms. Boston, London: Artech House.Google Scholar
  20. Meyer, F., and Coifman, R. (1997). Brushlets: A tool for directional image analysis and image compression. Applied and Computational Harmonic Analysis, vol. 4, pp. 147–187.MathSciNetzbMATHCrossRefGoogle Scholar
  21. Mulet-Parada, M., and Noble, J. (1998). 2D+T acoustic boundary detection in echocardiography. Proceedings of Medical Image Computing and Computer-Assisted Intervention-MICC AI’ 98, Cambridge, MA, pp. 806–813.Google Scholar
  22. Ota, T., Fleishman, C., Ohazama, C., Stetten, G., Lewis, C., Glower, D., Li, J., Ryan, T., Kisslo, J., and Ramm, O. (1990). Measurement of left ventricular volume by real-time, three-dimensional echocardiography in dogs. Circulation, vol. 94, pp. 379.Google Scholar
  23. Porat, M., Zeevi, Y. (1988). The generalized Gabor scheme for image representation in biological and machine vision. IEEE Transactions on Pattern Analysis and Machine Intell1gence, vol. 10, pp. 452–468.zbMATHCrossRefGoogle Scholar
  24. Sapin, P., Schroeder, K., Smith, M., DeMaria, A., and King, D. (1993). Three-dimensional echocardiographic measurement of left ventricular volume in vitro: Comparison with two-dimensional echocardiography and cineventriculography. Journal of American College of Cardiology, vol. 22, pp 1530–1537.CrossRefGoogle Scholar
  25. Siu, S., Levine, R., Rivera, J., Xie, S., Lethor, J., Handschumacher, M., Wayman, A., and Picard, M. (1995). Three-dimensional echocardiography improves noninvasive assessment of left ventricular volume and performance. American Heart Journal, vol. 130, pp. 812–822.CrossRefGoogle Scholar
  26. Stetten, G., Ota, T., Ohazama, C., Fleishman, C., Castelucci, J., Oxaal, J., Ryan, T., Kisslo, J., and Ramm, O. (1998). Real-time 3D ultrasound: A new look at the heart. Journal of Cardiovascular Diagnosis Procedures, vol. 15, pp. 73–84.Google Scholar
  27. Takuma, S., Cabreriza, S., Zwas, D., Fard, A., Chaudhry, H., Tullio, M., and Homma, S. (1998). Real-time 3D echocardiography for in-vitro assessment of left ventricular mass in dog. Circulation, vol. 98, pp. 701.Google Scholar
  28. Zong, X., Laine, A., and Geiser, E. (1998). Speckle reduction and contrast enhancement of echocardiograms via multiscale nonlinear processing. IEEE Transactions on Medical Imaging, vol. 17, pp. 532–540.CrossRefGoogle Scholar
  29. Wickerhauser, M. (1993). Smooth localized orthonormal bases. Comptes Rendus de l’Academie des Sciences, Paris I, pp. 423–427.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Elsa Angelini
    • 1
  • Andrew Laine
    • 1
  1. 1.Department of Biomedical EngineeringColumbia UniversityNew YorkUSA

Personalised recommendations