Performance of time-frequency representation techniques to measure blood flow turbulence with pulsed-wave Doppler ultrasound

The current processing performed by commercial instruments to obtain the ti me-frequency representation (TFR) of pulsed-wave Doppler signals may not be adequate to characterize turbulent Bow motions. The assessment of the inte nsity of turbulence is of high clinical importance and measuring high-frequ ency (small-scale) flow motions, using Doppler ultrasound (US), is a diffic ult problem that has been studied very little. The objective was to optimiz e the performance of the spectrogram (SPEC), autoregressive modeling (AR), Choi-Williams distribution (CWD), Choi-Williams reduced interference distri bution (CW-RID), Bessel distribution (BD), and matching pursuit method (MP) for mean velocity waveform estimation and turbulence detection. The intens ity of turbulence was measured from the fluctuations of the Doppler mean ve locity obtained from a simulation model under pulsatile flow. The Kolmogoro v spectrum, which is used to determine the frequency of the fluctuations an d, thus, the scale of the turbulent motions, was also computed for each met hod. The best set of parameters for each TFR method was determined by minim izing the error of the absolute frequency fluctuations and Kolmogorov spect ral bandwidth measured from the simulated and computed Doppler spectra. The results showed that different parameters must be used for each method to m inimize the velocity variance of the estimator, to optimize the detection o f the turbulent frequency fluctuations, and to estimate the Kolmogorov spec trum. To minimize the variance and to measure the absolute turbulent freque ncy fluctuations, four methods provided similar results: SPEC (10-ms sine-c osine windows), AR (10-ms rectangular windows, model order = 8), CWD (w(N) and w(M) = 10-ms rectangular windows, sigma = 0.01), and BD (w(N) = 10-ms r ectangular windows, alpha = 16). The velocity variance in the absence of tu rbulence was on the order of 0.04 m/s (coefficient of variation ranging fro m 8.0% to 14.5%, depending on the method). With these spectral techniques, the peak of the turbulence intensity was adequately estimated (velocity bia s < 0.01 m/s). To track the frequency of turbulence, the best method was BD (w(N) = 2-ms rectangular windows, <alpha> = 2). The bias in the estimate o f the -10 db bandwidth of the Kolmogorov spectrum was 354 +/- 51 Hz in the absence of turbulence (the true bandwidth should be 0 Hz), and - 193 +/- 37 1 Hz with turbulence (the simulated - 10 dB bandwidth was estimated at 1256 Hz instead of 1449 Hz). In conclusion, several TFR methods can be used to measure the magnitude of the turbulent fluctuations. To track eddies rangin g from large vortex to small turbulent fluctuations (wide Kolmogorov spectr um), the Bessel distribution with appropriate set of parameters is recommen ded. (C) 2001 World Federation for Ultrasound in Medicine & Biology.