ArticleIEEE transactions on medical imaging2013
Comparison of Kasai autocorrelation and maximum likelihood estimators for Doppler optical coherence tomography.
Article in IEEE transactions on medical imaging, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Analysis of sensitivity in quantitative micro-elastography.Biomedical optics express · 2021Article
- Computationally effective 2D and 3D fast phase unwrapping algorithms and their applications to Doppler optical coherence tomography.Biomedical optics express · 2019Article
- Clinical feasibility of optical coherence micro-elastography for imaging tumor margins in breast-conserving surgery.Biomedical optics express · 2018Article
- Doppler OCT clutter rejection using variance minimization and offset extrapolation.Biomedical optics express · 2018Article
- Wide-field quantitative micro-elastography of human breast tissue.Biomedical optics express · 2018Article
- Comparison of amplitude-decorrelation, speckle-variance and phase-variance OCT angiography methods for imaging the human retina and choroid.Biomedical optics express · 2016Article
- Simultaneous estimation of bidirectional particle flow and relative flux using MUSIC-OCT: phantom studies.Physics in medicine and biology · 2014Article
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Abstract
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.
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