本文将音频多普勒信号的几种特征提取方法结合起来，用多元分析的方法对血流状况进行多特征的分类决策，所用的特征提取方法包括：传统的声谱参数法，音频信号的零极点模型法、分形特征分析法及Teager能量法。将这种多特征分类决策法用于71例胎儿脐血流音频多普勒信号的分析，结果表明：该方法对胎儿宫内异常等疾病具有敏感性，可望为医学临床提供辅助诊断手段。

为了更准确全面地评估冠状动脉的动力学特性，本文采用血管内超声技术和血压测量设备获取冠状动脉内的超声图像、血流多普勒信号和血压曲线。使用动态轮廓模型从血管内的超声图像测量出管腔的截面积曲线，使用形态学和动态规划法提取多普勒信号中的流速信息，在心电同步下计算出血流量随心动周期的变化。结合血管内的血压曲线，通过傅里叶级数计算出冠状动脉等效阻抗。通过对人工控制下猪心肌微循环障碍实验的数据分析，观察到本方法估计出的血管阻抗可以反映冠状动脉的供血能力、扩张能力随微循环功能衰弱的变化。这一技术的实现，有望为医学临床提供一种新的辅助诊断参数。

通过数值仿真的方法分析狭窄血管内定常流多普勒血流信号的频谱特性，可为利用超声多普勒技术临床诊断血管狭窄提供依据。利用涡度－流函数方法计算狭窄管内的流场分布，估计出血管内不同位置的多普勒信号的功率谱密度函数，从中计算最大频率、平均频率和频谱宽度等参数，并分析它们在不同流速和狭窄程度下所表现出来的特征。

Embolic detection is very important to the early diagnosis of vessel disease. The Doppler ultrasound technique is one of the common methods to detect the emboli non-invasively. When the emboli pass through the sample volume of the Doppler ultrasound instrument, there exist high intensity transient Doppler signals. Thus the emboli can be detected directly from the variation of Doppler signal amplitude. Since there may be some disturbance in the system, this general detection method has great limitation. To improve the accuracy of emboli auto-detection, several novel methods are studied to obtain the sensitive characteristic of the emboli signals using the new signal processing theories.

A Doppler ultrasound analysis method based on wavelet package transform was proposed for embolic detection. The embolic Doppler signal was firstly decomposed using the wavelet packet. Then the sensitive characteristics were calculated from each sub-band signal and used in the emboli classification. This method was applied to analyze 300 cases simulated and 163 cases clinical Doppler signals. The error ratio of embolic detection using this method was 13 percents lower than that using the traditional spectrogram analysis method. It was shown that this method overcame the limit between time and frequency resolution in the short time Fourier transform, improved the accuracy of embolic detection greatly and extracted more reliable parameters for the clinical diagnosis.

A modified optimization-based contour detection method was presented to compute the lumen area of the coronary artery from intra-vascular ultrasound (IVUS) video images. Firstly, the search range for the artery inner wall was determined based on the continuity of IVUS video frames. Then the internal and external energy were calculated to describe the smoothness of the artery wall and the grayscale variation of ultrasound images respectively. Here a novel form of the external energy, which combined the gradient and variance of the intensity of image in the radial direction, was used. Finally, the minimal energy path, which was the optimum contour of the artery wall, was obtained using a circular dynamic programming (DP) method. By the comparison with the typical DP procedure using the traditional external energy form, which only counted the image gradient, the reliability of this modified method is considerably improved in the measurement of the lumen area of the coronary artery.

The low frequency and high amplitude clutter signal constitutes a great disturbance for the discrimination of low velocity blood flow signals in a Doppler ultrasound measurement system. In this letter, a novel method for clutter rejection is proposed, incorporating irregular sampling and iterative reconstruction of cubic spline. Experimental results indicate this approach outperforms the traditional way of high-pass filtering with its effective elimination of clutter and preservation of low velocity blood component in the overlapped frequency region.

A novel approach for blood and wall signal separation in Doppler ultrasound is proposed that removes 'wall thump' clutter in the wavelet time-scale domain. The blood and wall Doppler signals from a normal femoral artery were simulated and used in the experiments with different blood-to-clutter ratios. The method was compared with the conventional high-pass filtering method and signal separation in the spectrogram time-frequency domain. The experimental results show that the new method delivers improved performance for 'wall thump' removal with minimal loss of low-flow signal.

In Doppler ultrasound systems, a high-pass filter is usually employed to remove the wall component from the blood flow signal. However, this will lead to the loss of information from the low velocity flow. In this paper, an algorithm based on the principle component analysis (PCA) is proposed, in which singular value decomposition (SVD) is used to extract the main component from the mixed signals. Furthermore, the recursive process is incorporated into the PCA method to improve the performance of wall signal removal. This approach and the traditional high-pass filtering one are respectively applied to analyze the computer simulated, in vitro and in vivo Doppler ultrasound signals. With the proposed method, the wall signal can be removed while a large portion of low velocity blood signal remains. Comparison experiments show that this novel approach can satisfy the requirements of Doppler ultrasound system and is practicable under a broad range of measurement conditions. Since this algorithm is based on real data, it is currently applied to unidirectional signals.