Abstract
The authors present and explain coherently the two main algorithms which are currently used in commercial colour flow imaging systems, namely the frequency domain based autocorrelation algorithm and the time domain based cross correlation algorithm. The autocorrelation algorithm is a robust optimum mean frequency baseband estimator, for both high and low signal to noise ratio (SNR). Its main disadvantage is its aliasing problem. However the cross correlation algorithm does not suffer from this problem, and this has been one of the main reasons for its development. The mathematics behind these two algorithms and the general data processing steps used for their implementation are also presented. The subject of clutter removal filtering is discussed and valuable points are made. A practical relevant approach on how to discriminate between wanted blood flow related estimates and unwanted false estimates due to noise will be presented. On the aliasing problem of the autocorrelation algorithm, a promising solution known as spatial tracking of estimated mean frequency does exist, which will be also introduced in detail.