Comparison of Kasai autocorrelation and maximum likelihood estimators for Doppler optical coherence tomography.
Comparison of Kasai autocorrelation and maximum likelihood estimators for Doppler optical coherence tomography.
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DOI：
10.1109/tmi.2013.2248163
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发表时间：
2013-06
影响因子：
10.6
通讯作者：
Srinivasan VJ
中科院分区：
文献类型：
--
作者：
Chan AC;Lam EY;Srinivasan VJ
在光学相干断层扫描（OCT）和超声中，具有低方差的无偏多普勒频率估计器对于血流速度估计是期望的。OCT的硬件改进意味着更高的采集速率是可能的，这也应该在原则上提高估计性能。然而，奇怪的是，广泛使用的加塞自相关估计器的性能随着捕获速率的增加而增加。我们建议，基于精确的噪声统计模型的参数估计可以提供更好的性能。基于简单的加性白色高斯噪声模型，我们推导了一个最大似然估计（MLE），并证明了它可以优于加塞自相关估计。此外，我们还推导出Cramer Rao下界（CRLB），并表明，中等数据长度和噪声水平的极大似然估计的方差接近CRLB。我们注意到，MLE性能随着更长的采集时间而提高，并且保持恒定或随着更高的采集速率而提高。随着OCT成像速度的不断提高，这些品质可能使其成为首选技术。最后，我们的工作激励更一般的参数估计的基础上去相关噪声的统计模型的发展。
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.