Blood Flow Signal Processing Method Based On Ultrasound Doppler Effect

Doppler ultrasound has been used extensively in clinical applications, like obtaining hemodynamic information and detecting fetal rhythm of the heart and so on. Its major use remains the detection and quantification of flow in the heart, arteries and veins. The ultrasonic Doppler blood flow signal results from the backscattering of the ultrasound beam by moving red blood cells and the diagnostic information may be extracted from the signal. Doppler ultrasound technology without breaking was worshiped. But the extraction of accurate frequency shift was disturbed by the direction of the ultrasonic transducer, the signals absorbed and attenuated in the sound field, some signals with high amplitude from the arterial wall or the surrounding tissues and the noise in the process of transmission. This paper introduced wavelet transform method to denoise and process the ultrasonic Doppler signals, this method not only improve the accuracy and get the velocity of the blood fluid , but also get the direction of the blood fluid, therefore this research is very significant.Conventional spectral analysis of the Doppler signal is performed using the short-time Fourier transform, in which the signal is divided into small sequential or overlapping segments and the fast Fourier transform is applied to each one. Taking the fast Fourier transform of a short segment of the Doppler signal leads to a broadening of the spectral estimate and the leakage of signal energy into spurious side lobes. Using longer data segments reduces the distortion and leakage of the spectral estimates but may violate the nonstationarity assumption. Obviously, there are window induced broadening and nonstationarity broadening errors in the spectral estimates.On the contrary to the short-time Fourier transform, the Wavelet transform incorporates the concept of scale into the transform, which gives better time-frequency resolution: a compressed wavelet for analyzing high frequency details and a dilated wavelet for detecting lower frequency underlying trend. This method is particularly suitable for analyzing signals having a changing rapidly with time. In recently years, the Wavelet transform was used by many people for extraction of Doppler blood flow signals and analyzing its correlation coefficients.This article mainly develops the following several aspects:1. We first discuss the properties and characters of Doppler blood flow signal carefully, then built an academic model of the Doppler signal and simulate a Doppler echo signal on the computer according to the basic theory of the Doppler ultrasound technology. This experiment shows that the simulation method can largely facilitate studies of the blood flow in the next step of our work.2. Since the existence of noise in Doppler ultrasound signals may reduce the estimation precision of the maximum frequency, the mean frequency and the spectral width, which further do harm to the diagnostic precision, a Doppler ultrasound signal denoising algorithm based on the wavelet frame was introduced. The experiment result indicates the new method can filter away noise signal simultaneously reserving the entire cardiac cycle's effective Doppler ultrasound signals, such turn out to be a very good denoising method for the Doppler ultrasound signals.3. Then we use Short-time Fourier Transform and Wavelet Transform respectively to analyze the Doppler echo signal. During the process of analysis, we get the good display of the results. By comparing the results of the Wavelet transform with those of the short-time Fourier transform, we conclude that Wavelet transform is a better method for analyzing Doppler blood flow signal than the short-time Fourier transform: It has partial characteristic in the time domain and the frequency range, and has still the multi-resolution characteristic. So it adapts to analyzing the Doppler blood flow signal which simultaneously have low frequency and high frequency.4. In order to improve the precision of the blood flow rate and the maximum velocity which are two important indices in Doppler ultrasound diagnose, we try to extract the maximum frequency cure, the mean frequency cure and the spectral width cure: a method based on the wavelet analysis was proposed to estimate the maximum frequency cure in spectrograms. To verify the method, the maximum frequency curve of simulated signals is extracted and compared with the given one; finally we extract the mean frequency cure and the spectral width cure in spectrograms and discuss their extraction precision.