Comparison of Kasai autocorrelation and maximum likelihood estimators for doppler optical coherence tomography

Aaron C. Chan, Edmund Y. Lam, Vivek Srinivasan

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


In optical coherence tomography (OCT) and ultrasound, unbiased Doppler frequency estimators with low variance are desirable for blood velocity estimation. Hardware improvements in OCT mean that ever higher acquisition rates are possible, which should also, in principle, improve estimation performance. Paradoxically, however, the widely used Kasai autocorrelation estimator's performance worsens with increasing acquisition rate. We propose that parametric estimators based on accurate models of noise statistics can offer better performance. We derive a maximum likelihood estimator (MLE) based on a simple additive white Gaussian noise model, and show that it can outperform the Kasai autocorrelation estimator. In addition, we also derive the Cramer Rao lower bound (CRLB), and show that the variance of the MLE approaches the CRLB for moderate data lengths and noise levels. We note that the MLE performance improves with longer acquisition time, and remains constant or improves with higher acquisition rates. These qualities may make it a preferred technique as OCT imaging speed continues to improve. Finally, our work motivates the development of more general parametric estimators based on statistical models of decorrelation noise.

Original languageEnglish (US)
Article number6468104
Pages (from-to)1033-1042
Number of pages10
JournalIEEE Transactions on Medical Imaging
Issue number6
StatePublished - Jun 10 2013
Externally publishedYes


  • Cramer-Rao bound (CRB)
  • Doppler optical coherence tomography
  • Doppler ultrasound
  • frequency estimation
  • maximum likelihood estimation (MLE)

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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