Data Weighting for Principal Component Noise Reduction in Contrast Enhanced Ultrasound

  • Gord Lueck
  • Peter N. Burns
  • Anne L. Martel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


Pulse inversion ultrasound is a mechanism for preferentially displaying contrast agent in blood vessels while suppressing signal from tissue. We seek a method for identifying and segmenting areas of the liver with similar statistically significant time intensity curves. As a first step in this process, a method of weighting Rayleigh distributed ultrasound image data before principal components analysis is presented. Simulation studies show that relative mean squared error can be reduced by 14% when the correct number of dimensions in selected. Our method is tested on an in vitro ultrasound phantom showing slightly increased error suppression, and is demonstrated on a clinical liver scan, showing decreased correlation between signals in the low intensity range.


Pulse Inversion Time Intensity Curve Significant Time Intensity Curve Weighted Principal Component Analysis Contrast Ultrasound Image 
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.


  1. 1.
    Burns, P., Wilson, S., Simpson, D.: Pulse inversion imaging of liver blood flow: Improved method for characterizing focal masses with microbubble contrast. Investigative radiology 35, 58 (2000)CrossRefGoogle Scholar
  2. 2.
    Wilson, S., Burns, P.: Liver mass evaluation with ultrasound: The impact of microbubble contrast agents and pulse inversion imaging. Seminars in liver disease 21, 147 (2001)Google Scholar
  3. 3.
    Williams, Q.: Tissue perfusion diagnostic classification using a spatio-temporal analysis of contrast ultrasound image sequences. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Martel, A.: Extracting parametric images from dynamic contrast-enhanced mri studies of the brain using factor analysis. Medical image analysis 5, 29 (2001)Google Scholar
  5. 5.
    Benali, H.: A statistical model for the determination of the optimal metric in factor-analysis of medical image sequences (famis). Physics in medicine biology 38, 1065 (1993)CrossRefGoogle Scholar
  6. 6.
    Cochran, R.N., Horne, F.H.: Statistically weighted principal component analysis of rapid scanning wavelength kinetics experiments. Analytical Chem. 49, 846–853 (1977)CrossRefGoogle Scholar
  7. 7.
    S̆ámal, M.: Experimental comparison of data transformation procedures for analysis of principal components. Phys. Med. Biol. 44, 2821 (1999)Google Scholar
  8. 8.
    Keenan, M.R., Kotula, P.G.: Accounting for Poisson noise in the multivariate analysis of ToF-SIMS spectrum images. Surf. Interface Analysis 36, 203–212 (2004)CrossRefGoogle Scholar
  9. 9.
    Wagner, R.F., Smith, S.W., Sandrick, J.M., Lopez, H.: Statistics of speckle in ultrasound b-scans. IEEE Transactions on Sonics and Ultrasonics 30, 156–163 (1983)CrossRefGoogle Scholar
  10. 10.
    Goodman, J.W.: Some fundamental properties of speckle. Optical Society of America, Journal 66, 1145–1150 (1976)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gord Lueck
    • 1
    • 2
  • Peter N. Burns
    • 1
    • 2
  • Anne L. Martel
    • 1
    • 2
  1. 1.Sunnybrook Health Sciences CentreTorontoCanada
  2. 2.Department of Medical BiophysicsUniversity of TorontoCanada

Personalised recommendations