WO2012041196A1 - 一种多普勒彩色血流成像方法和装置 - Google Patents

一种多普勒彩色血流成像方法和装置 Download PDF

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WO2012041196A1
WO2012041196A1 PCT/CN2011/080153 CN2011080153W WO2012041196A1 WO 2012041196 A1 WO2012041196 A1 WO 2012041196A1 CN 2011080153 W CN2011080153 W CN 2011080153W WO 2012041196 A1 WO2012041196 A1 WO 2012041196A1
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blood flow
signal
module
typical
color
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马忠伟
李雷
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Shenzhen Mindray Bio Medical Electronics Co Ltd
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Shenzhen Mindray Bio Medical Electronics Co Ltd
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/06—Measuring blood flow
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/08—Clinical applications
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/48—Diagnostic techniques
    • A61B8/488—Diagnostic techniques involving Doppler signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/52—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/5269—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving detection or reduction of artifacts
    • A61B8/5276—Devices using data or image processing specially adapted for diagnosis using ultrasonic, sonic or infrasonic waves involving detection or reduction of artifacts due to motion

Definitions

  • the present invention relates to the field of ultrasound imaging, and more particularly to a Doppler color blood flow imaging method and apparatus.
  • Ultrasound refers to mechanical waves with a frequency above 20 kHz and has a wide range of applications in medical diagnosis, ranging and flaw detection.
  • Medical ultrasound generally includes modules such as transducers, transmitting and receiving circuits, and digital signal processing.
  • the transducer converts the electrical signal into ultrasonic waves that are emitted into the body tissue.
  • Ultrasound is transmitted in human tissues, and absorption, reflection, refraction, and scattering occur. A portion of the scattered signal is returned to the transducer and converted back to an analog electrical signal.
  • the receiving circuit converts these received analog electrical signals into digital signals. Then, through the digital signal processing link, the useful information is extracted and then converted into an image signal that is easily recognized by the human eye.
  • the propagation characteristics of ultrasound in various organs are different, so there is a certain difference in the energy of the scattered echoes received by the transducer.
  • the transducer emits a pulse, and then according to the ultrasonic propagation speed, a series of echo amplitude data representing the depth can be acquired at different times.
  • the transducer emits multiple pulses at different locations within the same slice, and a set of two-dimensional data representing the difference in ultrasonic scattering echo energy at different locations within the slice can be obtained.
  • This energy difference can be mapped to different image gray levels to generate a two-dimensional black-and-white image (referred to as a two-dimensional image) that can describe the structure of the human tissue.
  • the velocity information obtained from the autocorrelation estimation may be mapped to pseudo color information, and the generated pseudo color image may be referred to as a Doppler color blood flow image (hereinafter referred to as a blood flow image).
  • the energy information obtained from the autocorrelation estimation can also be mapped to pseudo color information, and the generated pseudo color image can be referred to as an energy image.
  • the medium of motion that doctors care about is mainly blood, but not only blood movement can produce Doppler effect, tissue movement can also produce Doppler effect (such as breathing). Therefore, special techniques are needed to remove Doppler information generated by non-blood movements in order to make it easier for doctors to diagnose.
  • data from autocorrelation contains three mechanisms of production: normal noise, non-blood motion, and blood movement. Both normal and non-blood movements are not required by doctors when assessing blood movements.
  • the causes of ordinary noise are complex, such as sound wave attenuation and reflection, inaccurate focus, and circuit thermal noise. Ordinary noise is not generated by the Doppler effect, so the energy value is lower after wall filtering and can usually be identified by setting the energy threshold method.
  • Noise caused by non-blood motion is called motion artifacts and is usually caused by the Doppler effect, such as breathing, movement of the myocardial wall, tissue motion, and probe movement.
  • the signals produced by blood movements are called blood flow signals.
  • Typical blood flow signals and motion artifacts have their own characteristics and are easy to distinguish; but some blood flow signals and motion artifacts have similar characteristics (such as similar energy and Speed), so it is not easy to remove by traditional methods.
  • Blood flow identification technology plays an important role in medical ultrasound, so many methods are used to identify blood flow signals.
  • the most basic method is to set the energy threshold, which is recognized as a blood flow signal above the threshold; below the threshold is identified as noise. This method has a significant effect on conventional noise with lower energy, but the effect of suppressing motion artifacts with higher energy is not obvious.
  • the second method is to increase the cutoff frequency of the wall filter. This method has a certain inhibitory effect on motion artifacts. However, some motion artifacts have a high frequency, and simply increasing the wall filter cutoff frequency cannot be completely filtered out. In addition, choosing the right cutoff frequency is also a difficult problem to handle. Excessive cutoff frequencies will filter out some low-speed blood flow signals.
  • the third method is to perform two autocorrelation estimations on the quadrature demodulated signal, one wall filtering and the other without wall filtering. The results of the two estimates are then compared and motion artifacts are then identified.
  • This method can accurately identify a part of the motion artifacts, but in some cases, the blood flow signal is also related before and after the wall filtering. Therefore, this method can easily identify the blood flow signal as a motion artifact, resulting in an erroneous processing result.
  • a Doppler color blood flow imaging method comprising the following steps:
  • Weighting the tissue feature parameters to obtain a dynamic threshold performing feature estimation on the complex signal after wall filtering to obtain blood flow characteristic parameters
  • a typical blood flow signal is obtained by typical blood flow identification of blood flow characteristic parameters, and a typical signal is obtained by performing typical motion artifact recognition on atypical blood flow signals after typical blood flow identification;
  • a typical blood flow signal and a blood flow signal similar to the motion artifact are synthesized to obtain a complete blood flow signal, and the complete blood flow signal is mapped into a color blood flow image output.
  • a Doppler color blood flow imaging apparatus comprising:
  • a transmitting circuit for transmitting a pulse
  • a receiving circuit for amplifying and analog-to-digital conversion of an echo signal
  • a beamforming module for beam combining the received pulse echoes
  • a quadrature demodulation module for orthogonally demodulating a signal after beam combining
  • a wall filtering module for performing wall filtering processing on the quadrature demodulated complex signal
  • a first feature estimation module for performing feature estimation on the quadrature demodulated complex signal
  • a dynamic threshold calculation module for calculating a dynamic threshold
  • a second feature estimation module for performing feature estimation on the complex signal after the wall filtering process to obtain a blood flow characteristic parameter
  • a typical blood flow identification module for performing typical blood flow identification of blood flow characteristic parameters
  • a typical motion artifact recognition module for performing typical motion artifact recognition of blood flow characteristic parameters
  • a similar signal identification module for identifying blood flow signals in similar signals
  • a blood flow signal synthesis module for synthesizing a typical blood flow signal and a blood flow signal similar to motion artifacts
  • An image output module for mapping a B-type two-dimensional signal and a complete blood flow signal into an image
  • the transmitting circuit, the probe, the receiving circuit, the beam combining module, the quadrature demodulating module, the wall filtering module, the second feature estimating module, and the typical blood flow identifying module are sequentially connected; the typical blood flow identifying module is connected to the blood flow signal synthesis a module, the typical blood flow identification module is further connected to a typical motion artifact recognition module; the typical motion artifact recognition module is connected to a similar signal recognition module;
  • the quadrature demodulation module is further connected to the first feature estimation module and the dynamic threshold calculation module, wherein the dynamic threshold calculation module is connected to a similar signal recognition module; the similar signal recognition module is connected to the blood flow signal synthesis module;
  • the quadrature demodulation module is further connected to the envelope detection module and the image output module, and the blood flow signal synthesis module is connected to the image output module.
  • the present invention comprehensively evaluates features before and after wall filtering, and then separates blood flow signals from motion artifacts. Finally, the integrated blood flow signal is combined with the blood flow signal identified from the similar signal to finally obtain a filled blood flow signal.
  • FIG. 1 is a system block diagram of a Doppler color blood flow imaging system according to an embodiment of the present invention
  • FIG. 2 is a flowchart of a method for a Doppler color blood flow imaging method according to an embodiment of the present invention
  • 3A, 3B, 3C, 3D and 3E are two-dimensional diagrams of energy velocities of signals after autocorrelation
  • 4A and 4B are normalized spectral characteristics of a blood flow signal wall before and after filtering
  • 5A and 5B are normalized spectral feature diagrams before and after motion artifact wall filtering.
  • the present invention divides the information after autocorrelation into four types: ordinary noise, similar signals (blood flow signals and motion artifacts with similar characteristics), independent blood flow signals, and independent motion artifacts.
  • Ordinary noise has a lower energy and can be distinguished by an energy threshold.
  • independent blood flow signals and independent motion artifacts they are easy to extract separately because of their independent characteristics.
  • independent blood flow signals have the characteristics of high speed and moderate energy; while independent motion artifacts have the characteristics of low speed and high energy.
  • the recognition accuracy is high and should be given priority.
  • the integrated blood flow signal is combined with the blood flow signal identified from the similar signal to obtain the final filled blood flow signal.
  • FIG. 1 A block diagram of the present invention is shown in FIG.
  • the method comprises: a transmitting circuit for transmitting a pulse; a probe for converting the pulse into an ultrasonic wave and receiving the pulse echo signal; a receiving circuit for amplifying the echo signal, and analog-to-digital conversion; a beamforming module for beamforming, a quadrature demodulation module for quadrature demodulating a signal after beamforming, and an envelope detecting method for envelope detection of a quadrature demodulated signal a module; a wall filtering module for performing wall filtering on the quadrature demodulated signal; a first feature estimating module for performing feature estimation on the quadrature demodulated signal; and a dynamic threshold calculating for calculating a dynamic threshold a second feature estimation module for performing feature estimation on the wall filtered signal to obtain a blood flow characteristic parameter; a typical blood flow identification module for performing typical blood flow identification on a blood flow characteristic parameter; A typical motion artifact recognition module for performing typical motion artifact recognition by a stream feature parameter
  • the transmitting circuit, the probe, the receiving circuit, the beam combining module, the quadrature demodulating module, the wall filtering module, the second feature estimating module, and the typical blood flow identifying module are sequentially connected; the typical blood flow identifying module is connected to the blood flow signal synthesis The module; the typical blood flow identification module is further coupled to a typical motion artifact recognition module; the typical motion artifact recognition module is coupled to a similar signal recognition module.
  • the quadrature demodulation module is further connected to the first feature estimation module and the dynamic threshold calculation module, wherein the dynamic threshold calculation module is connected to a similar signal recognition module; and the similar signal recognition module is connected to the blood flow signal synthesis module.
  • the quadrature demodulation module is further connected to the envelope detection module and the image output module, and the blood flow signal synthesis module is connected to the image output module.
  • the transmitting circuit generates a transmit pulse that is transmitted to the transducer of the probe and converted into an ultrasonic wave.
  • Ultrasound occurs in a series of physical processes in human tissue, such as refraction, reflection, and scattering. A portion of the energy is returned to the transducer and converted into an analog electrical signal. The analog electrical signal is digitally converted after analog-to-digital conversion.
  • the beam synthesis module effectively integrates the signals received by the multiple elements on the transducer and outputs a well-focused ultrasonic echo amplitude signal.
  • the echo amplitude signal is converted to a complex signal by a quadrature demodulation module.
  • the B-type two-dimensional image data is obtained by extracting the envelope from the complex signal.
  • the feature signal is directly estimated by the feature signal to obtain the tissue feature parameters.
  • the feature estimation can be performed by autocorrelation estimation or by using fast Fourier transform, Fourier transform, autoregressive model, and the like.
  • Another processing method for the complex signal is to perform wall estimation on the complex signal before performing feature estimation to obtain blood flow characteristic parameters, wherein the feature estimation may adopt autocorrelation estimation or may use fast Fourier transform and Fourier transform. , autoregressive models, etc. for feature estimation.
  • a similar signal is obtained by performing typical blood flow identification and typical motion artifact recognition on blood flow characteristic parameters, and the similar signal includes both blood flow signal data and motion artifact data. Typical blood flow signal data is also available for typical blood flow identification.
  • a set of dynamic thresholds is obtained by weighting the tissue feature parameters to identify blood flow signals from similar signals. Finally, the typical blood flow signal data and the blood flow signal data recognized from the similar signals are combined to obtain complete blood flow image data, and the B-type two-dimensional image and the blood flow image can be simultaneously displayed through the image output module.
  • a low-pass filter can be added before the complex signal is directly estimated, so that the tissue components can be preserved and the blood flow signal interference can be eliminated.
  • a method flow of a Doppler color blood flow imaging method includes the following steps:
  • Step S100 transmitting a pulse.
  • Step S200 receiving a pulse echo.
  • Step S300 Perform beamforming on the received pulse echo.
  • Step S400 performing quadrature demodulation on the signal after beam combining to obtain a complex signal.
  • Step S500 performing feature estimation on the complex signal to obtain an organization feature parameter; and performing wall filtering on the complex signal to filter out low-speed tissue information.
  • Step S600 performing weighting processing on the tissue feature parameters obtained after the feature estimation to obtain a dynamic threshold value; performing feature estimation on the complex signal after the wall filtering process to obtain a blood flow characteristic parameter.
  • Step S700 performing typical blood flow identification on the blood flow characteristic parameter to obtain a typical blood flow signal, and performing typical motion artifact recognition on the atypical blood flow signal after the typical blood flow identification to obtain a similar signal.
  • Step S800 performing a recognition process on the similar signal in combination with the dynamic threshold to obtain a blood flow signal similar to the motion artifact.
  • Step S900 synthesize a typical blood flow signal and a blood flow signal similar to the motion artifact to obtain a complete blood flow signal, and map the complete blood flow signal into a color blood flow image output.
  • the above method may further comprise:
  • Step S510 performing envelope detection processing on the quadrature demodulated complex signal to obtain a B-type two-dimensional signal, and mapping the B-type two-dimensional signal into a B-type two-dimensional image;
  • Step S1000 Displaying the B-type two-dimensional image and the color blood flow image simultaneously.
  • Autocorrelation is a common Doppler effect feature estimation method. Autocorrelation estimation of complex signals can generally obtain three parameters of velocity, variance and energy.
  • feature estimation can be performed on the Doppler effect using fast Fourier transform, Fourier transform, autoregressive model, and the like.
  • a normal noise filtering process is performed on the signal by setting an energy threshold and filtering out the signal below the energy threshold. The normal noise processing can also be set before the blood flow image is formed after the complete blood flow signal is synthesized.
  • a low-pass filtering operation can be added before the feature estimation of the complex signal to obtain more accurate tissue characteristic parameters.
  • tissue characteristic parameters including tissue velocity, tissue variance, and tissue energy
  • tissue characteristic parameters including tissue velocity, tissue variance, and tissue energy
  • wall filtering is high-pass filtering or band-pass filtering to filter out low-speed tissue information
  • feature estimation another set of characteristic parameters, generally called blood flow, can be obtained.
  • Characteristic parameters including blood flow velocity, blood flow variance, and blood flow energy.
  • the invention divides the information obtained by high-pass filtering and feature estimation into four kinds, as shown in FIG. 3A, which are: ordinary noise, similar signals (blood flow signals and motion artifacts with similar characteristics), independent blood flow. Signal and independent motion artifacts.
  • the ordinary noise energy is low and can be distinguished by an energy threshold, which can be set in any step after the autocorrelation, or after the blood flow signal synthesis is completed.
  • an energy threshold can be set in any step after the autocorrelation, or after the blood flow signal synthesis is completed.
  • independent blood flow signals and independent motion artifacts they are easy to extract separately because of their independent characteristics.
  • the independent motion artifact has the characteristics of low speed and high energy.
  • FIG. 3B it can be identified by setting the speed threshold and the energy threshold; and the independent blood flow signal has the characteristics of high speed and moderate energy, as shown in FIG.
  • the recognition accuracy is high and should be given priority.
  • the recognition accuracy is high and should be given priority.
  • it can be decomposed into the motion artifacts shown in Figure 3D and the blood flow signals shown in Figure 3E. Both have similar speed and energy ranges. Therefore, it is necessary to use the characteristics before and after wall filtering for comprehensive evaluation.
  • the blood flow signal is separated from the motion artifacts.
  • variance features can also be used to distinguish between various signals.
  • the independent blood flow signal and the independent motion artifact have smaller variance (such as the normalized value is less than 0.5), and the ordinary noise has a larger variance (such as the normalized value is greater than 0.5).
  • the variance characteristics of similar signals are more complicated and not easy to measure directly.
  • Figure 4A shows the characteristics of a blood flow signal without motion artifacts, with normal noise predominating before wall filtering. However, after wall filtering, as shown in FIG. 4B, the blood flow signal is dominant.
  • Figure 5A shows the characteristics of a motion artifact without blood flow. Before wall filtering, common noise and motion artifacts jointly affect the frequency offset estimation; but after wall filtering, as shown in Figure 5B, only there is no filtering. Clean motion artifacts affect parameter estimation.
  • the velocity value at a certain point after wall filtering is high (such as the normalized velocity value is greater than 0.3), and the energy satisfies the blood flow signal range, it can be considered as an independent blood flow signal, and the blood flow signal range needs to be based on the actual system.
  • Customization such as normalized blood flow, has an energy value between 0.3 and 0.8. If the energy of a certain point after wall filtering is larger than the energy of the blood flow signal (such as the normalized energy value is greater than 0.8) and the speed value is lower (such as the normalized speed value is less than 0.3), Can be considered as an independent motion artifact.
  • the wall-filtered motion artifact energy is similar to the blood flow signal, and the velocity is also in the blood flow velocity interval (eg, the normalized velocity value is greater than 0.3), it is considered a similar signal.
  • a dynamic threshold method is needed to distinguish (see below).
  • the dynamic velocity threshold can be calculated using the estimated tissue velocity values before wall filtering to distinguish between motion artifacts and blood flow signals.
  • the dynamic threshold of the tissue can be calculated using the energy and variance of the tissue, and the energy and variance of the blood flow can be judged to analyze whether it is a blood flow signal.
  • the dynamic velocity threshold can be calculated based on the estimated tissue velocity value before wall filtering to identify the blood flow signal in the similar signal. According to the above description, it is set that if the wall-filtered speed value is greater than the corresponding dynamic speed threshold, it is considered to be a blood flow signal, and vice versa, it is considered as a motion artifact, and the motion artifact needs to be eliminated. Only the blood flow signal is finally retained.
  • tissue velocity, tissue variance, and tissue energy need to be weighted to correspond to blood flow velocity, blood flow variance, and blood flow energy, ie, numerically corresponding.
  • Weighting can be done using a method such as aX+b, X can be expressed as velocity, energy or variance, and a and b are weighting parameters.
  • non-linear methods can be used for weighting, such as aX2+bX+c, threshold method, etc., to obtain dynamic threshold parameters.
  • the three physical parameters of the tissue velocity, the tissue variance and the tissue energy are weighted to obtain three dynamic thresholds: a dynamic velocity threshold, a dynamic variance threshold, and a dynamic energy threshold.
  • the velocity of the tissue before the wall filtering is the autocorrelation frequency offset of 0.075.
  • the velocity dynamic threshold is 0.15
  • the velocity is 0.29, which is greater than the velocity dynamic threshold of 0.15. Therefore, this part of the information is a blood flow signal.
  • the velocity of the tissue before the wall filtering that is, the autocorrelation frequency offset is 0.06
  • the velocity dynamic threshold is 0.12
  • the velocity is 0.11, which is smaller than the velocity dynamic threshold of 0.12. So this part of the information is a motion artifact.
  • the integrated blood flow signal is combined with the blood flow signal identified from the similar signal to obtain a filled blood flow signal.
  • the filled blood flow signal can be mapped to a pseudo color image using the velocity feature, or the energy feature can be mapped to a pseudo color image to finally obtain a Doppler color blood flow image.
  • the Doppler color blood flow image and the two-dimensional image are superimposed to generate a final display image.

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Description

一种多普勒彩色血流成像方法和装置 技术领域
本发明涉及超声成像领域，尤其涉及的是一种多普勒彩色血流成像方法和装置。
 背景技术
超声波是指频率在20KHz以上的机械波，在医学诊断、测距和探伤等方面具有广泛的用途。医学超声一般包含有换能器、发射接收电路和数字信号处理等模块。在发射电路的驱动下，换能器把电信号转化为超声波发射到人体组织中。超声波在人体组织中传输，会出现吸收、反射、折射和散射等现象。其中散射的信号会有一部分返回到换能器中，并被再次转化为模拟电信号。接收电路会把这些接收到的模拟电信号转化为数字信号。然后通过数字信号处理环节，把有用的信息提取出来，然后转化为人眼容易识别的图像信号。
在人体组织中，超声波在各个器官中的传播特性是不相同的，因此就会造成换能器接收到的散射回波能量有一定差异。换能器发射一个脉冲，然后根据超声波传播速度，可以在不同时刻采集到一系列表示深度的回波幅度数据。换能器在同一切面内不同位置发射多个脉冲，就可以得到一组表示此切面内不同位置超声波散射回波能量差异的二维数据。这种能量差异可以被映射为不同的图像灰度，从而生成一幅二维黑白图像（简称二维图像），可以描述人体组织的结构。
超声波传播过程中，介质如果发生移动，会产生多普勒现象。如果介质面向换能器运动，散射回波频率会升高；如果介质背向换能器运动，散射回波频率会降低。在检测多普勒效应时，换能器会向组织内同一位置发出一系列相同的超声波脉冲，脉冲重复频率决定最大可检测到的介质移动速度。由于血流不断运动，因此相邻脉冲回波的相位可能会发生变化。自相关估计是一种常见相位差分析算法，可以把多普勒效应产生的特征估计为速度、能量和方差三个物理量。自相关估计得到的速度信息可以映射为伪彩信息，生成的伪彩图像可以被称为多普勒彩色血流图像（以下简称血流图像）。自相关估计得到的能量信息也可以映射为伪彩信息，生成的伪彩图像可以被称为能量图像。
在医学超声诊断中，医生关心的运动介质主要为血液，但是并不是只有血液运动才会产生多普勒效应，组织运动同样可以产生多普勒效应（比如呼吸）。因此需要使用特殊的技术去除非血液运动产生的多普勒信息，才能使医生更容易的进行诊断。一般情况下，自相关得到的数据含三种产生机制：普通噪声、非血液运动和血液运动。在评估血液运动时，普通噪声和非血液运动都是医生不需要的。普通噪声产生的原因很复杂，常见的原因比如声波的衰减和反射、聚焦不准和电路热噪声等。普通噪声不是由多普勒效应产生，因此在壁滤波后能量值较低，通常可以通过设置能量阈值方法识别。非血液运动引起的噪声被称为运动伪像，通常由多普勒效应产生，比如呼吸、心肌壁的运动、组织运动和探头移动等。血液运动产生的信号被称为血流信号，典型的血流信号和运动伪像都有自己的特征，容易区分；但是一部分血流信号和运动伪像有着相似的特征（比如有类似的能量和速度），因此不易用传统的方法去除。
血流识别技术在医学超声中具有重要作用，因此许多方法被用来识别血流信号。最基本的方法是设置能量阈值，高于阈值的被识别为血流信号；低于阈值的被识别为噪声。此种方法对能量较低的常规噪声效果显著，但是对能量较大的运动伪像抑制效果不明显。第二种方法是提高壁滤波的截止频率，此种方法对运动伪像有一定抑制作用。但是有些运动伪像频率较高，单纯提高壁滤波截止频率无法完全滤除。此外，选择恰当的截止频率也是较难处理的问题，过高的截止频率会过滤掉一些低速的血流信号。第三种方法是对正交解调后的信号进行两次自相关估计，一次经过壁滤波，另一次未经过壁滤波。然后对两次估计的结果进行比较，然后识别出运动伪像。此种方法可以准确识别一部分运动伪像，但是某些情况下，血流信号在壁滤波前后也有一定联系，因此此种方法容易把血流信号误识别为运动伪像，导致错误的处理结果。
因此，现有血流识别技术还有待于改进和发展。
 技术问题
本发明的目的在于提供一种多普勒彩色血流成像方法和装置，旨在解决现有的多普勒彩色血流成像中难以将类似血流信号的运动伪像去除的问题。
 技术解决方案
本发明的技术方案如下：
一种多普勒彩色血流成像方法，其包括以下步骤：
发射脉冲；
接收脉冲回波；
对接收到的脉冲回波进行波束合成；
对波束合成后的信号进行正交解调得到复信号；
对复信号进行特征估计得到组织特征参数；并且对复信号进行壁滤波处理以过滤掉低速的组织信息；
对组织特征参数进行加权处理得到动态阈值；对壁滤波处理后的复信号进行特征估计得到血流特征参数；
对血流特征参数进行典型血流识别得到典型的血流信号，同时对典型血流识别后的非典型的血流信号进行典型运动伪像识别得到类似信号；
结合动态阈值对类似信号进行识别处理得到与运动伪像相似的血流信号；
对典型血流信号和与运动伪像相似的血流信号进行合成得到完整的血流信号，并将完整的血流信号映射成彩色血流图像输出。
一种多普勒彩色血流成像装置，其包括：
用于发射脉冲的发射电路；
用于将脉冲转换为超声波发射出去并接收脉冲回波信号的探头；
用于对回波信号进行放大，模数转换的接收电路；
用于对接收到的脉冲回波进行波束合成的波束合成模块；
用于对波束合成后的信号进行正交解调的正交解调模块；
用于对正交解调后的复信号进行壁滤波处理的壁滤波模块；
用于对正交解调后的复信号进行特征估计的第一特征估计模块；
用于计算动态阈值的动态阈值计算模块；
用于对壁滤波处理后的复信号做特征估计得到血流特征参数的第二特征估计模块；
用于对血流特征参数进行典型血流识别的典型血流识别模块；
用于通过对血流特征参数进行典型运动伪像识别的典型运动伪像识别模块；
用于识别类似信号中血流信号的类似信号识别模块；
用于对典型血流信号和与运动伪像相似的血流信号进行合成的血流信号合成模块；
用于将B型二维信号和完整的血流信号映射成图像的图像输出模块,
其中，发射电路、探头、接收电路、波束合成模块、正交解调模块、壁滤波模块、第二特征估计模块、典型血流识别模块依次连接；所述典型血流识别模块连接血流信号合成模块，所述典型血流识别模块还连接典型运动伪像识别模块；所述典型运动伪像识别模块连接类似信号识别模块；
所述正交解调模块还依次连接第一特征估计模块和动态阈值计算模块，所述动态阈值计算模块连接类似信号识别模块；所述类似信号识别模块连接血流信号合成模块；
所述正交解调模块还依次连接包络检测模块和图像输出模块，所述血流信号合成模块连接图像输出模块。
 有益效果
本发明的有益效果：本发明通过对壁滤波前后的特征进行综合评估，然后把血流信号和运动伪像分离开。最后，综合独立血流信号与从类似信号中识别出来的血流信号，最终得到充盈的血流信号。
 附图说明
图1是本发明实施例提供的多普勒彩色血流成像系统的系统框图；
图2是本发明实施例提供的多普勒彩色血流成像方法的方法流程图；
图3A、3B、3C、3D和3E 是自相关后各信号的能量速度二维图；
图4A和4B是一种血流信号壁滤波前后的归一化频谱特征图；
图5A和5B是一种运动伪像壁滤波前后的归一化频谱特征图。
 本发明的实施方式
本发明将自相关后的信息分为四种：普通噪声、类似信号（具有相似特征的血流信号和运动伪像）、独立血流信号和独立运动伪像。普通噪声的能量较低，可通过能量阈值区分。对于独立血流信号和独立运动伪像，由于其具有独立的特征，很容易单独提取。比如独立血流信号具有速度高和能量适中的特征；而独立运动伪像则具有速度低和能量大的特征。对于具有独立特征的信号，其识别正确率很高，应当优先保证。而对于类似信号，需要使用壁滤波前后的特征进行综合评估，然后把血流信号和运动伪像分离开。最后，综合独立血流信号与从类似信号中识别出来的血流信号，得到最终充盈的血流信号。
为使本发明的目的、技术方案及优点更加清楚、明确，以下参照附图并举实施例对本发明进一步详细说明。
本发明的框图如图1所示。其包括：用于发射脉冲的发射电路；用于将脉冲转换为超声波发射出去并接收脉冲回波信号的探头；用于对回波信号进行放大，模数转换的接收电路；用于对接收到的脉冲回波进行波束合成的波束合成模块；用于对波束合成后的信号进行正交解调的正交解调模块；用于对正交解调后的信号进行包络检测的包络检测模块；用于对正交解调后的信号进行壁滤波处理的壁滤波模块；用于对正交解调后的信号进行特征估计的第一特征估计模块；用于计算动态阈值的动态阈值计算模块；用于对壁滤波处理后的信号做特征估计得到血流特征参数的第二特征估计模块；用于对血流特征参数进行典型血流识别的典型血流识别模块；用于通过对血流特征参数进行典型运动伪像识别的典型运动伪像识别模块；用于识别类似信号中血流信号的类似信号识别模块；用于对典型血流信号和与运动伪像相似的血流信号进行合成的血流信号合成模块；用于将B型二维信号和完整的血流信号还原成图形输出的图像输出模块。
其中，发射电路、探头、接收电路、波束合成模块、正交解调模块、壁滤波模块、第二特征估计模块、典型血流识别模块依次连接；所述典型血流识别模块连接血流信号合成模块；所述典型血流识别模块还连接典型运动伪像识别模块；所述典型运动伪像识别模块连接类似信号识别模块。
所述正交解调模块还依次连接第一特征估计模块和动态阈值计算模块，所述动态阈值计算模块连接类似信号识别模块；所述类似信号识别模块连接血流信号合成模块。
所述正交解调模块还依次连接包络检测模块和图像输出模块，所述血流信号合成模块连接图像输出模块。
发射电路产生发射脉冲，传递到探头的换能器上转化为超声波。超声波在人体组织中发生一系列物理过程，如折射、反射和散射等，一部分能量返回到换能器中并被转化为模拟电信号。模拟电信号经过模数转换后成为数字信号。通过波束合成模块将换能器上多阵元接收到的信号进行有效整合，并输出聚焦良好的超声回波幅度信号。通过正交解调模块将回波幅度信号转化为复信号。对复信号求取包络就得到了B型二维图像数据。
然后对复信号直接进行特征估计得到组织特征参数，其中特征估计可以采用自相关估计也可以是使用快速傅里叶变换、傅里叶变换、自回归模型等进行特征估计。对复信号的另一处理方法是对复信号先进行壁滤波处理后再进行特征估计得到血流特征参数，其中特征估计可以采用自相关估计也可以是使用快速傅里叶变换、傅里叶变换、自回归模型等进行特征估计。通过对血流特征参数进行典型血流识别和典型运动伪像识别得到一种类似信号，该类似信号中既包括有血流信号的数据也包括运动伪像的数据。在进行典型血流识别时还可得到典型的血流信号数据。需要通过对组织特征参数进行加权处理得到一组动态阈值，来将血流信号从类似信号中识别出来。最终将典型的血流信号数据和从类似信号中识别出来的血流信号数据合成得到完整的血流图像数据，通过图像输出模块可以将B型二维图像和血流图像同时显示出来。
为了能够更好的得到组织特征参数，可以在对复信号直接进行特征估计前加入一个低通滤波器，从而可以保留组织成分，消除血流信号干扰。
参见图2，本发明实施例提供的多普勒彩色血流成像方法的方法流程，其包括以下步骤：
步骤S100：发射脉冲。
步骤S200：接收脉冲回波。
步骤S300：对接收到的脉冲回波进行波束合成。
步骤S400：对波束合成后的信号进行正交解调得到复信号。
步骤S500：对复信号进行特征估计得到组织特征参数；另外对复信号进行壁滤波处理过滤掉低速的组织信息。
步骤S600：对进行特征估计后得到的组织特征参数进行加权处理得到动态阈值；对壁滤波处理后的复信号进行特征估计得到血流特征参数。
步骤S700：对血流特征参数进行典型血流识别得到典型血流信号，同时对典型血流识别后的非典型血流信号进行典型运动伪像识别得到类似信号。
步骤S800：结合动态阈值对类似信号进行识别处理得到与运动伪像相似的血流信号。
步骤S900：对典型血流信号和与运动伪像相似的血流信号进行合成得到完整的血流信号，并将完整的血流信号映射成彩色血流图像输出。
上述方法还可以进一步包括：
步骤S510：对正交解调后的复信号进行包络检测处理得到B型二维信号，并将B型二维信号映射成B型二维图像；
步骤S1000：将B型二维图像与彩色血流图像同时显示。
其中，步骤S500中对复信号进行特征估计得到组织特征参数的方法有多种。自相关是一种常见的多普勒效应特征估计方法，对复信号进行自相关估计，一般可以得到速度、方差和能量三种参数信息。此外使用快速傅里叶变换、傅里叶变换、自回归模型等也可以对多普勒效应进行特征估计。在对复信号进行特征估计之后要对信号进行一个普通噪声过滤处理，采用的方法是设置一个能量阈值，将低于能量阈值的信号过滤掉。所述普通噪声处理也可设置在得到完整的血流信号合成之后，血流图像形成之前。
另外，在对复信号进行特征估计前还可加入一个低通滤波操作，用于得到更准确的组织特征参数。
一般情况下，组织特征占主导成分，如果直接进行特征估计，可以得到组织特征参数，包括组织速度、组织方差和组织能量。如果对复信号先进行壁滤波（所述壁滤波为高通滤波或带通滤波，用于过滤掉低速的组织信息），然后再进行特征估计，可以得到另一组特征参数，一般称为血流特征参数，包括血流速度、血流方差和血流能量。
本发明将经过高通滤波并进行特征估计后得到的信息分为四种，如图3A所示，分别是：普通噪声、类似信号（具有相似特征的血流信号和运动伪像）、独立血流信号和独立运动伪像。普通噪声能量较低，可通过能量阈值区分，所述能量阈值可以设置在自相关之后任何步骤中，也可以设置在血流信号合成完成后。对于独立血流信号和独立运动伪像，由于其具有独立的特征，很容易单独提取。比如独立运动伪像则具有速度低和能量大的特征，如图3B所示，可以通过设定速度阈值和能量阈值进行识别；而独立血流信号具有速度高和能量适中的特征，如图3C所示，同样可以通过设定速度阈值和能量阈值进行识别（具体请参见后文）。对于具有独立特征的信号，其识别正确率很高，应当优先保证。而对于类似信号，可分解为图3D所示的运动伪像和图3E所示的血流信号，两者具有相似的速度和能量范围，因此需要使用壁滤波前后的特征进行综合评估，才能把血流信号和运动伪像分离开。此外，方差特征也可以被用来区分各种信号。通常独立血流信号和独立运动伪像均具有较小的方差（如归一化后数值小于0.5），普通噪声具有较大方差（如归一化后数值大于0.5）。而类似信号的方差特征比较复杂，不容易直接衡量。
要将运动伪像和类似的血流信号正确识别，首先了解类似信号中血流信号和运动伪像的特征差异。图4A所示的是一种无运动伪像的血流信号的特征，在壁滤波前，普通噪声占主导成分。但是在壁滤波后，如图4B所示，血流信号占主导成分。图5A所示的是一种无血液流动的运动伪像的特征，在壁滤波前，普通噪声和运动伪像共同影响频偏估计；但在壁滤波后，如图5B所示，只有没有过滤干净的运动伪像影响参数估计。
如果壁滤波后某一点的速度值较高（如归一化后速度值大于0.3），同时能量满足血流信号范围，则可以认为是独立的血流信号，该血流信号范围需要根据实际系统定制，例如归一化后血流的能量值在0.3到0.8之间。如果壁滤波后的某一个点的能量较大，显著大于血流信号的能量（如归一化后能量值大于0.8），且速度值较低（如归一化后速度值小于0.3），就可以认为是独立运动伪像。如果壁滤波后的运动伪像能量和血流信号类似，且速度也在血流速度区间（如归一化后速度值大于0.3），则认为是类似信号。对于这类信号，需要用动态阈值方法进行区分（具体参见下文）。比如在图4A和4B 中，壁滤波前后估计的速度值相差较大；而在图5A和5B中，壁滤波前后估计的速度值相差较小。这样即可使用壁滤波前估计出的组织速度值计算出动态速度阈值，区分运动伪像和血流信号。此外，也可以使用组织的能量和方差计算动态阈值，对血流的能量和方差进行判断，分析是否为血流信号。
基于类似信号中血流信号和运动伪像的上述特征差异，在本实施例中，可以根据壁滤波前估计出的组织速度值计算出动态速度阈值来识别类似信号中的血流信号。根据上面的描述设定为如果壁滤波后的速度值大于对应的动态速度阈值则认为是血流信号，反之则认为是运动伪像，运动伪像需要剔除。最终保留下来的只有血流信号。
由于壁滤波的作用，组织速度、组织方差和组织能量需要进行一定的加权才能和血流速度、血流方差和血流能量相对应，即数值大小上相对应。可以使用如aX+b的方法进行加权，X可以表示为速度、能量或方差，a和b为加权参数。除此之外，还可以采用非线性的方法进行加权，比如aX2+bX+c、阈值法等，得到动态阈值参数。
这样，在运动伪像判断时，对组织速度、组织方差和组织能量3个物理参数进行加权得到3个动态阈值：动态速度阈值、动态方差阈值和动态能量阈值。最后通过这3个动态阈值，可以将运动伪像和类似的血流信号正确识别。
以图4A和4B为例，在壁滤波前组织的速度即自相关频偏为0.075，经过2X的加权后得到速度动态阈值0.15，而壁滤波后其速度为0.29，大于速度动态阈值0.15。所以该部分信息为血流信号。
以图5A和5B为例，在壁滤波前组织的速度即自相关频偏为0.06，经过2X的加权后得到速度动态阈值0.12，而壁滤波后其速度为0.11，小于速度动态阈值0.12。所以该部分信息为运动伪像。
最后，综合独立血流信号与从类似信号中识别出来的血流信号，得到充盈的血流信号。充盈的血流信号可利用速度特征映射为伪彩图像，也可利用能量特征映射为伪彩图像，最终得到多普勒彩色血流图像。所述多普勒彩色血流图像和二维图像叠加在一起生成最终的显示图像。
应当理解的是，本发明的应用不限于上述的举例，对本领域普通技术人员来说，可以根据上述说明加以改进或变换，所有这些改进和变换都应属于本发明所附权利要求的保护范围。

Claims (17)

  1. 一种多普勒彩色血流成像方法，其特征在于，包括以下步骤：
    发射脉冲；
    接收脉冲回波；
    对接收到的脉冲回波进行波束合成；
    对波束合成后的信号进行正交解调得到复信号；
    对复信号进行特征估计得到组织特征参数；并且对复信号进行壁滤波处理以过滤掉低速的组织信息；
    对组织特征参数进行加权处理得到动态阈值；对壁滤波处理后的复信号进行特征估计得到血流特征参数；
    对血流特征参数进行典型血流识别得到典型的血流信号，同时对典型血流识别后的非典型的血流信号进行典型运动伪像识别得到类似信号；
    结合动态阈值对类似信号进行识别处理得到与运动伪像相似的血流信号；
    对典型血流信号和与运动伪像相似的血流信号进行合成得到完整的血流信号，并将完整的血流信号映射成彩色血流图像输出。
  2. 根据权利要求1所述的多普勒彩色血流成像方法，其特征在于，在完整的血流信号映射成彩色血流图像输出之前，通过设置能量阈值将普通噪声过滤掉。
  3. 根据权利要求2所述的多普勒彩色血流成像方法，其特征在于，所述方法还包括以下步骤：
    对正交解调后得到的复信号进行包络检测处理得到B型二维信号，并将B型二维信号映射成B型二维图像；
    将B型二维图像与彩色血流图像同时显示。
  4. 根据权利要求1所述的多普勒彩色血流成像方法，其特征在于，所述特征估计采用自相关估计方法。
  5. 根据权利要求1所述的多普勒彩色血流成像方法，其特征在于，所述特征估计采用快速傅里叶变换估计方法、傅里叶变换估计方法或者自回归模型估计方法。
  6. 根据权利要求4或5所述的多普勒彩色血流成像方法，其特征在于，所述完整的血流信号使用速度特征映射成伪彩，生成彩色多普勒血流图像。
  7. 根据权利要求4或5所述的多普勒彩色血流成像方法，其特征在于，所述完整的血流信号使用能量特征映射成伪彩，生成彩色多普勒血流图像。
  8. 根据权利要求1所述的多普勒彩色血流成像方法，其特征在于，在对复信号进行特征估计得到组织特征参数前，对复信号进行低通滤波处理将血流信号过滤掉。
  9. 根据权利要求8所述的多普勒彩色血流成像方法，其特征在于，所述特征估计采用自相关估计方法。
  10. 根据权利要求8所述的多普勒彩色血流成像方法，其特征在于，所述特征估计采用快速傅里叶变换估计方法或自回归模型。
  11. 根据权利要求9或10所述的多普勒彩色血流成像方法，其特征在于，所述完整的血流信号使用速度特征映射成伪彩，生成彩色多普勒血流图像。
  12. 根据权利要求9或10所述的多普勒彩色血流成像方法，其特征在于，所述完整的血流信号使用能量特征映射成伪彩，生成彩色多普勒血流图像。
  13. 根据权利要求1或8所述的多普勒彩色血流成像方法，其特征在于，所述壁滤波为高通滤波。
  14. 一种多普勒彩色血流成像装置，其特征在于，包括：
    用于发射脉冲的发射电路；
    用于将脉冲转换为超声波发射出去并接收脉冲回波信号的探头；
    用于对回波信号进行放大，模数转换的接收电路；
    用于对接收到的脉冲回波进行波束合成的波束合成模块；
    用于对波束合成后的信号进行正交解调的正交解调模块；
    用于对正交解调后的复信号进行壁滤波处理的壁滤波模块；
    用于对正交解调后的复信号进行特征估计的第一特征估计模块；
    用于计算动态阈值的动态阈值计算模块；
    用于对壁滤波处理后的复信号做特征估计得到血流特征参数的第二特征估计模块；
    用于对血流特征参数进行典型血流识别的典型血流识别模块；
    用于通过对血流特征参数进行典型运动伪像识别的典型运动伪像识别模块；
    用于识别类似信号中血流信号的类似信号识别模块；
    用于对典型血流信号和与运动伪像相似的血流信号进行合成的血流信号合成模块；
    用于将B型二维信号和完整的血流信号映射成图像的图像输出模块,
    其中，发射电路、探头、接收电路、波束合成模块、正交解调模块、壁滤波模块、第二特征估计模块、典型血流识别模块依次连接；所述典型血流识别模块连接血流信号合成模块，所述典型血流识别模块还连接典型运动伪像识别模块；所述典型运动伪像识别模块连接类似信号识别模块；
    所述正交解调模块还依次连接第一特征估计模块和动态阈值计算模块，所述动态阈值计算模块连接类似信号识别模块；所述类似信号识别模块连接血流信号合成模块；
    所述正交解调模块还依次连接包络检测模块和图像输出模块，所述血流信号合成模块连接图像输出模块。
  15. 根据权利要求14所述的多普勒彩色血流成像装置，其特征在于，还包括：
    用于对正交解调后的信号进行包络检测得到B型二维信号的包络检测模块。
  16. 根据权利要求14所述的多普勒彩色血流成像装置，其特征在于，还包括：
    设置在第一特征估计模块前，用于对正交解调后的复信号进行低通滤波的低通滤波器。
  17. 根据权利要求14所述的多普勒彩色血流成像装置，其特征在于，所述壁滤波模块为一高通滤波器。
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