全数字超声多普勒血流测量系统研究

传统超声多普勒血流测量仪器使用模拟电路来完成信号采集与解调处理,此类系统容易受外界电磁环境、温度变化等因素的干扰,也难以运用现代信号处理技术来实现更高级的检测功能,例如多深度检测、功率M型多普勒血流成像以及编码激励成像等。本研究设计了一种全数字的超声多普勒血流信号采集处理系统,结合经典的自相关技术,实现了多深度检测和功率M型多普勒血流成像,并采用2周期基础码调制的13位Barker码编码激励方法,得到远高于传统方法的轴向空间分辨率。为验证该系统的有效性,首先利用该系统对多普勒体模进行检测实验研究,证明该系统能有效提高轴向空间分辨率;然后通过对大脑中动脉、前动脉血流检测,表明该系统可以显示出超声波发射方向上整个深度的血流信息,有利于血管的定位和识别。     

彩色多普勒在门静脉癌栓检测中的应用

目的 应用彩色多普勒血流显像(CDFI)及脉冲多普勒(PD)检测门静脉癌栓的血流状况。方法 运用CDFI和PD检测门静脉癌栓及其周围有无血流信号,在血流信号明显处取样,获得PD血流频谱。结果 31例癌栓内见稀疏星点状血流信号,无血流频谱,5例癌栓内可探及动脉血流频谱。结论 CDFI和PD具有无创、可重复动态观察的优点,是临床诊断及治疗方案选择不可缺少的重要手段和方法。     

搭桥冠脉内超声多普勒血流信号的仿真分析

为研究搭桥冠脉内血流状况与移植管结构因素的关系,采用基于计算流体动力学模型的信号仿真方法合成不同缝合角度、不同缝合距离的搭桥冠脉内的超声多普勒血流信号,并分析其频谱随两个结构因素变化的规律.结果 表明:缝合角度越小,缝合前端及下游仿真信号的频谱宽度越小,说明搭桥手术的效果越好;缝合位置距离狭窄越近,缝合后端附近血流信号的频谱宽度越大,缝合前端及下游的频谱宽度则越小,说明手术应折中选取缝合位置.结论 与血液动力学研究结果一致,可以为手术方案的设计和术后效果的监测提供参考.     

多普勒测定孕妇左心室舒张功能与年龄、心率及胎龄变化的关系

目的运用多普勒及组织多普勒技术测定正常孕妇左心室舒张功能变化,探讨孕妇年龄、胎龄、心率与心功能的关系,为孕产妇早期心功能判断提供帮助。方法选取2009年4月至2010年4月产前检查704例孕妇,年龄19~41岁,平均年龄26.43岁。对所有孕妇进行超声心动图检查,常规检测各项数据,然后分别测定二尖瓣血流频谱及二尖瓣环部组织多普勒频谱。分析孕妇不同年龄、心率及胎龄与左心室舒张功能不全的关系。结果出现血流多普勒异常43例(6.1%),组织多普勒异常152例(21.6%)。随年龄增长、胎龄增长及心率增快,组织多普勒和血流多普勒异常也增多,并且组织多普勒改变比血流多普勒更加明显。结论孕妇怀孕的生理过程相当于心脏功能的负荷试验,使正常衰老过程或病理状态下才出现的舒张功能改变提早出现。并且与孕妇的年龄、胎龄、心率增加有关,这种负荷试验出现的心脏舒张功能异常改变,对孕妇分娩时心功能估计及产后心脏情况评价有积极意义。     

基于小波变换估计的超声多普勒血流频谱中频带展宽及修正

利用脉冲多普勒技术检测血流时，信号的频谱带宽是诊断疾病的重要依据之一。与传统的短时傅立叶变换比较，小波变换使用了不同宽度的函数窗，具有多分辨能力，对多普勒血流信号频谱的估计可以获得更精确的时频分辨率。但是，基于小波变换的频谱带宽中仍然包含有分析窗口和信号自身非平稳性引入的误差。本研究针对小波变换中窗口宽度变化的多分辨特点，给出了窗口效应和信号非平稳性频谱均方根带宽的表达式，提出了两种修正频谱带宽中窗口和非平稳展宽误差的方法。通过对模拟多普勒信号的修正实验，两种方法都取得了很好的修正效果。     

彩色双功能多普勒超声评价睾丸的血流特点及其意义

目的:应用高频率二维及彩色多普勒超声,探讨正常成人睾丸及其血流的彩色多普勒超声特点.方法:经阴囊超声检测120例正常成年男性的睾丸及其血流的彩色多普勒频谱,测睾丸动脉(TA)、包膜动脉(CA,即睾丸动脉主支)、睾丸内动脉(ITA,包括向心动脉和中央动脉)的收缩期峰值血流速度(PSV)、舒张末期血流速度(EDV)、时间平均峰值血流速度(TAMX)、阻力指数(RI)和搏动指数(PI);分别计算左右侧睾丸大小和血流参数的均值,并作t检验进行分析.结果:120例正常成人左、右侧睾丸大小及其血管的PSV、EDV、TAMX、RI和PI测值无显著性差异.CA和ITA多普勒频谱波型变化较小,为低阻力型血流频谱;TA的波型变化较大,RI、PI值变异较大.结论:高频率彩色双功能多普勒超声敏感性高,可准确检测睾丸的形态结构及其血管的血流动力学参数,且左、右侧睾丸测值差异无显著意义(P>0.05).     

功率M模式经颅多普勒超声测量系统的设计

经颅多普勒（TCD）是研究颅内大血管中血流动力学的常用技术,但传统TCD中依然存在着对大脑血管定位困难和对微栓子检测不确定等缺点。为了克服这些弊端,本研究设计了一种功率M模式TCD测量系统。该系统通过FPGA设计、USB固件驱动以及PC数据后处理等模块共同实现。数字电路模块以FPGA为核心进行状态机设计,并采用高速AD采样和双FIFO乒乓工作模式来确保良好的实时性;硬件电路部分和PC之间的数据传输依靠高速USB实现;软件模块结合功率M模式多普勒成像技术、短时傅里叶变换和伪彩色图像处理,对超声波束发射方向上整体深度的血流状态进行监测,方便定位血管深度,并为临床医生提供选通深度的声谱图信息。     

基于正弦调频连续波的超声多普勒血流速度的测量

为了解决基于脉冲波和普通连续波测量方法的不足,我们采用了基于正弦调频连续波的多普勒血流速度测量方法。利用正弦调频连续波作为发射信号,通过延迟信号与回波信号相乘解调,然后进行频谱分析,得到多普勒频移信息。本研究通过原理分析和仿真实验,验证了基于正弦调频连续波方法的有效性,并探讨了调频参数对测量结果的影响。结果表明,该方法拥有基于连续波方法的一般特点,并能够在测量速度的同时获得距离信息。     

彩色多普勒超声在急性下肢深静脉血栓诊断中的显像研究

目的应用彩色多普勒超声(CDFI)观察急性下肢深静脉血栓(DVT)的图像特点,研究其血流动力学变化规律。方法检测126例158条拟诊为急性下肢深静脉血栓的患者,观察下肢深静脉管腔内径变化、管壁回声、管腔内回声、血栓形成的范围、血流动力学改变等情况。结果 123条急性血栓患者初诊时下肢深静脉管腔内径患侧较健侧明显增宽(p<0.01),血栓呈低回声。完全阻塞者官腔内无彩色血流信号,彩色血流出现突然中断现象;不完全阻塞者管腔内可见彩色血流信号充盈缺损,脉冲多普勒为连续性低速血流频谱,多普勒频谱随呼吸变化不明显或无变化。结论急性下肢深静脉血栓的声像图及血流动力学改变,可为临床早期诊断提供可靠信息。     

多普勒血流信号仿真模型的建立及谱估计方法的研究

本文建立了多普勒血流信号的仿真模型，在计算机上完成了血流信号谱估计方法的仿真研究。最后，通过对人体颈动脉血流信号的研究，进一步分析了各种谱估计方法的有效性。所得结论对多普勒血流信号谱分析器的设计有重要的指导意义。     

Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network

Doppler ultrasound is a noninvasive technique that allows the examination of the direction, velocity, and volume of blood flow. In this study, ophthalmic artery Doppler signals were obtained from 105 subjects, 48 of whom had suffered from ophthalmic artery stenosis. A least-mean squares backpropagation neural network was used to detect the presence or absence of ophthalmic artery stenosis. Spectral analysis of ophthalmic artery Doppler signals was done by the Welch method for determining the neural network inputs. The network was trained, cross validated and tested with subject records from the database. Performance indicators and statistical measures were used for evaluating the neural network. Ophthalmic artery Doppler signals were classified with the accuracy varying from 88.9% to 90.6%.     

Wavelet-based neural network analysis of ophthalmic artery Doppler signals

In this study, ophthalmic artery Doppler signals were recorded from 115 subjects, 52 of whom had ophthalmic artery stenosis while the rest were healthy controls. Results were classified using a wavelet-based neural network. The wavelet-based neural network model, employing the multilayer perceptron, was used for analysis of ophthalmic artery Doppler signals. A multilayer perceptron neural network (MLPNN) trained with the Levenberg-Marquardt algorithm was used to detect stenosis in ophthalmic arteries. In order to determine the MLPNN inputs, spectral analysis of ophthalmic artery Doppler signals was performed using wavelet transform. The MLPNN was trained, cross validated, and tested with training, cross validation, and testing sets, respectively. All data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ophthalmic artery stenosis. The correct classification rate was 97.22% for healthy subjects, and 96.77% for subjects having ophthalmic artery stenosis. The classification results showed that the MLPNN trained with the Levenberg-Marquardt algorithm was effective to detect ophthalmic artery stenosis.     

A computer simulation model for Doppler ultrasound signals from pulsatile blood flow in stenosed vessels

A computer simulation model based on an analytic flow velocity distribution is proposed to generate Doppler ultrasound signals from pulsatile blood flow in the vessels with various stenosis degrees. The model takes into account the velocity field from pulsatile blood flow in the stenosed vessels, sample volume shape and acoustic factors that affect the Doppler signals. By analytically solving the Navier-Stokes equations, the velocity distributions of pulsatile blood flow in the vessels with various stenosis degrees are firstly calculated according to the velocity at the axis of the circular tube. Secondly, power spectral density (PSD) of the Doppler signals is estimated by summing the contribution of all scatterers passing through the sample volume grouped into elemental volumes. Finally, Doppler signals are generated using cosine-superposed components that are modulated by the PSD functions that vary over the cardiac cycle. The results show that the model generates Doppler blood flow signals with characteristics similar to those found in practice. It could be concluded that the proposed approach offers the advantages of computational simplicity and practicality for simulating Doppler ultrasound signals from pulsatile blood flow in stenosed vessels.     

Spectral broadening of ophthalmic arterial Doppler signals using STFT and wavelet transform

In this study, short-time Fourier transform (STFT) and wavelet transform (WT) were used for spectral analysis of ophthalmic arterial Doppler signals. Using these spectral analysis methods, the variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of spectral broadening in the presence of ophthalmic artery stenosis. A qualitative improvement in the appearance of the sonograms obtained using the WT over the STFT was noticeable. Despite the qualitative improvement in the individual sonograms, no quantitative advantage in using the WT over the STFT for the determination of spectral broadening index was obtained due to the poorer variance of the wavelet transform-based spectral broadening index and the additional computational requirements of the wavelet transform.     

A mixture of experts network structure for modelling Doppler ultrasound blood flow signals

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. Expectation-Maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.     

Correction for broadening in Doppler blood flow spectrum estimated using wavelet transform

The conventionally used spectral estimation technique for Doppler blood flow signal analysis is short-time Fourier transform (STFT). But this method requires stationarity of the signal during the window interval. Wavelet transform (WT), which has a flexible time-frequency window, is particularly suitable for nonstationary signals. In recently years, the WT has been used to investigate its advantages and limitations for the analysis of Doppler blood flow signals. In these studies, the estimated spectral width of Doppler blood flow signals using the WT might include significant window and nonstationarity broadening errors. These broadening errors of the time-varying spectrum were clearly undesirable since it would tend to mask the effect of flow disturbance on the spectra width. In this paper, a closed form expression for window and nonstationary root-mean-squared (rms) spectral width is given when using the WT to estimate the Doppler blood flow spectrum. The increases in the rms spectral width can be calculated and then the spectral width estimation based on the WT can be corrected.     

OPTHALMIC ARTERY DOPPLER FLOW VELOCITY STUDY IN DIABETIC

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Determining variability of ophthalmic arterial Doppler signals using Lyapunov exponents

The new method presented in this study was directly based on the consideration that ophthalmic arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architecture was formulated and used as a basis for determining variabilities such as stenosis, ocular Behcet disease, and uveitis disease in the physical state of ophthalmic arterial Doppler signals. The computed Lyapunov exponents of the ophthalmic arterial Doppler signals were used as inputs of the MLPNN. Receiver operating characteristic (ROC) curve was used to assess the performance of the detection process. The ophthalmic arterial Doppler signals were classified with the accuracy varying from 93.75% to 97.06%. The results confirmed that the proposed MLPNN trained with Levenberg-Marquardt algorithm has potential in detecting stenosis, Behcet disease and uveitis disease in ophthalmic arteries.     

Application of classical and model-based spectral methods to ophthalmic arterial Doppler signals with uveitis disease

In this study, Doppler signals recorded from ophthalmic artery of 75 subjects were processed by PC-computer using classical and model-based methods. The classical method (fast Fourier transform) and three model-based methods (Burg autoregressive, moving average, least-squares modified Yule–Walker autoregressive moving average methods) were selected for processing ophthalmic arterial Doppler signals with uveitis disease. Doppler power spectra of ophthalmic arterial Doppler signals were obtained by using these spectrum analysis techniques. The variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of uveitis disease.     

Detection of ophthalmic arterial doppler signals with Behcet disease using multilayer perceptron neural network

Doppler ultrasound is known as a reliable technique, which demonstrates the flow characteristics and resistance of ophthalmic arteries. In this study, ophthalmic arterial Doppler signals were obtained from 106 subjects, 54 of whom suffered from ocular Behcet disease while the rest were healthy subjects. Multilayer perceptron neural network (MLPNN) employing delta-bar-delta training algorithm was used to detect the presence of ocular Behcet disease. Spectral analysis of the ophthalmic arterial Doppler signals was performed by least squares (LS) autoregressive (AR) method for determining the MLPNN inputs. The MLPNN was trained with training set, cross validated with cross validation set and tested with testing set. All these data sets were obtained from ophthalmic arteries of healthy subjects and subjects suffering from ocular Behcet disease. Performance indicators and statistical measures were used for evaluating the MLPNN. The correct classification rate was 96.43% for healthy subjects and 93.75% for unhealthy subjects suffering from ocular Behcet disease. The classification results showed that the MLPNN employing delta-bar-delta training algorithm was effective to detect the ophthalmic arterial Doppler signals with Behcet disease.