WO2022160899A1 - 一种三维通气图像产生方法、控制器及装置 - Google Patents

一种三维通气图像产生方法、控制器及装置 Download PDF

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WO2022160899A1
WO2022160899A1 PCT/CN2021/132763 CN2021132763W WO2022160899A1 WO 2022160899 A1 WO2022160899 A1 WO 2022160899A1 CN 2021132763 W CN2021132763 W CN 2021132763W WO 2022160899 A1 WO2022160899 A1 WO 2022160899A1
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image
ventilation
dimensional
electrical impedance
signal
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PCT/CN2021/132763
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English (en)
French (fr)
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张可
张昕
管明涛
王谊冰
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北京华睿博视医学影像技术有限公司
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Priority to JP2023544419A priority Critical patent/JP2024505852A/ja
Priority to US18/268,891 priority patent/US20240057887A1/en
Priority to EP21922489.6A priority patent/EP4285816A1/en
Publication of WO2022160899A1 publication Critical patent/WO2022160899A1/zh

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves 
    • A61B5/053—Measuring electrical impedance or conductance of a portion of the body
    • A61B5/0536—Impedance imaging, e.g. by tomography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/0809—Detecting, measuring or recording devices for evaluating the respiratory organs by impedance pneumography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A61B5/085—Measuring impedance of respiratory organs or lung elasticity
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7278—Artificial waveform generation or derivation, e.g. synthesising signals from measured signals
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/04—Arrangements of multiple sensors of the same type
    • A61B2562/046—Arrangements of multiple sensors of the same type in a matrix array
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2576/00—Medical imaging apparatus involving image processing or analysis
    • A61B2576/02—Medical imaging apparatus involving image processing or analysis specially adapted for a particular organ or body part
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/004—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow

Definitions

  • the present disclosure belongs to the technical field of electrical impedance imaging applications, and in particular relates to a method, a controller and a device for generating a three-dimensional ventilation image.
  • EIT Electrical Impedance Tomography, Electrical Impedance Tomography
  • EIT Electrical Impedance Tomography
  • Electrical Impedance Tomography is a non-invasive technology that reconstructs in vivo tissue images by targeting the resistivity distribution inside the human body or other organisms.
  • the human body is a large biological electrical conductor, and each tissue and organ has a certain impedance.
  • the impedance of the local part must be different from that of other parts. Therefore, the measurement of impedance can be used to detect the disease of human organs. Diagnose.
  • the prior art can only generate a two-dimensional ventilation image, and the two-dimensional image reflects the electrical impedance change in a certain section of the thoracic region of the human body to be measured due to the change in gas content.
  • the technical problem to be solved by the present disclosure is how to generate a three-dimensional ventilation image, so as to reflect the ventilation situation of the human chest cavity in each volume in the three-dimensional space.
  • the present disclosure provides a method, controller and device for generating a three-dimensional ventilation image.
  • the present disclosure provides a method for generating a three-dimensional ventilation image, including the following steps: generating a three-dimensional ventilation image according to an electrical impedance signal obtained by measuring an electrical impedance of a target area to be measured through a signal extraction algorithm and an image reconstruction algorithm, wherein , the electrical impedance measurement of the target area to be measured is realized by using an electrode array that is three-dimensionally distributed on the periphery of the target area to be measured.
  • the present disclosure provides a three-dimensional ventilation image generation controller, which includes a memory and a processor, the memory stores a computer program, and the computer program implements the steps of the above method when executed by the processor.
  • the present disclosure provides a three-dimensional ventilation image generating device, comprising: an electrode array distributed three-dimensionally on the periphery of a target area to be measured, which is used to measure the electrical impedance of the target area to be measured, and the measured resistance sent to a three-dimensional ventilation image generation controller; and the three-dimensional ventilation image generation controller described above.
  • FIG. 1 shows a flowchart of a method for generating a three-dimensional ventilation image according to Embodiment 1 of the present disclosure
  • FIG. 2 shows another flowchart of the method for generating a three-dimensional ventilation image according to Embodiment 1 of the present disclosure
  • FIG. 3( a ) shows a flowchart of a method for generating a three-dimensional ventilation image according to Embodiment 2 of the present disclosure
  • Fig. 3(b) shows another flowchart of the method for generating a three-dimensional ventilation image according to the second embodiment of the present disclosure
  • FIG. 4( a ) shows a schematic diagram of time-domain signals of human chest cavity measurement data according to Embodiment 2 of the present disclosure
  • Figure 4(b) shows a schematic diagram of a frequency domain signal of the human chest cavity measurement data according to Embodiment 2 of the present disclosure
  • Fig. 5(a) shows a schematic diagram of a time-domain signal of a ventilation-related signal after filtering the human chest cavity measurement data according to Embodiment 2 of the present disclosure
  • Fig. 5(b) shows a schematic diagram of a frequency domain signal of a ventilation-related signal after filtering the human chest cavity measurement data according to Embodiment 2 of the present disclosure
  • FIG. 6 shows a schematic diagram of a three-dimensional ventilation image of the human thoracic cavity generated by the method for generating a three-dimensional ventilation image shown in FIG. 3( a ) according to the second embodiment of the present disclosure
  • FIG. 7 shows a schematic diagram of a three-dimensional differential image of the human thorax generated by the method for generating a three-dimensional ventilation image shown in FIG. 3( b ) according to the second embodiment of the present disclosure
  • Figure 8(a) shows a schematic diagram of the time domain signal of the example pixel point in Figure 7;
  • Figure 8(b) shows a schematic diagram of the frequency domain signal of the example pixel point in Figure 7;
  • Fig. 9 (a) shows the time domain signal schematic diagram of the ventilation-related signal after the example pixel point data filtering in Fig. 7;
  • Fig. 9(b) shows a schematic diagram of the frequency domain signal of the ventilation-related signal filtered by the example pixel point data in Fig. 7;
  • FIG. 10 shows a schematic diagram of a three-dimensional ventilation image of the human chest cavity generated by the method for generating a three-dimensional ventilation image shown in FIG. 3( b ) according to the second embodiment of the present disclosure.
  • an embodiment of the present disclosure provides a method for generating a three-dimensional ventilation image, wherein the method for generating a three-dimensional ventilation image in this embodiment is implemented in two ways, as shown in FIG. 1 and FIG. 2 . shown.
  • the method for generating a three-dimensional ventilation image in this embodiment includes the following steps.
  • the electrode array can use a plurality of impedance strips or an electrode vest in which the electrodes are distributed in three dimensions.
  • the electrical impedance signal includes a ventilation-related signal and a blood perfusion-related signal
  • the ventilation-related signal is extracted from the electrical impedance signal obtained by performing electrical impedance measurement on the target area to be measured by a signal extraction algorithm, including the following steps:
  • a low-pass filter is used to extract the ventilation-related signal from the electrical impedance signal obtained by measuring the electrical impedance of the target area to be measured, wherein the cut-off frequency of the low-pass filter is greater than the second harmonic frequency of the ventilation-related signal and less than the blood Fundamental frequency of perfusion-related signals.
  • the signal extraction algorithm is any one of frequency domain filtering method, principal component analysis method and neural network method.
  • the image reconstruction algorithm is a linear difference reconstruction algorithm or an image reconstruction algorithm based on a neural network.
  • the method for generating a three-dimensional ventilation image in this embodiment includes the following steps.
  • S220 List the time series of each pixel in the three-dimensional image according to the three-dimensional image data at multiple times, wherein the time series of each pixel is composed of values of each pixel at different times.
  • step S230 extracting the time series of ventilation-related pixels from the time series of each pixel in the three-dimensional image is implemented by any one of frequency domain filtering method, principal component analysis method and neural network method.
  • the image reconstruction algorithm is a linear difference reconstruction algorithm or an image reconstruction algorithm based on a neural network.
  • the embodiments of the present disclosure provide a method for generating a 3D ventilation image applied to a human thorax based on Embodiment 1, wherein the method for generating a 3D ventilation image in this embodiment uses two methods. Implementation, as shown in Figure 3(a) and Figure 3(b).
  • the method for generating a three-dimensional ventilation image in this embodiment includes the following steps: first, measuring the electrical impedance of the chest region of the human body to be measured; then, extracting a ventilation-related signal from the measurement signal; finally, reconstructing a three-dimensional ventilation image.
  • the specific process is as follows.
  • the first step is to measure the electrical impedance of the thoracic region of the human body to be tested.
  • an electrode array needs to be fixed around the thoracic cavity of the human body to be measured.
  • the electrode array includes several electrodes distributed in three-dimensional space. Then, the thoracic cavity of the human body to be tested is excited through the electrode array and the resulting response is measured, that is, current excitation is applied to the electrodes in turn, and the resulting voltage signals are measured on other electrodes in turn.
  • the ventilation-related signal is extracted from the electrical impedance signal measured in the previous step.
  • a ventilation related signal is extracted from the measured electrical impedance signal using a filter.
  • the filter can use a finite impulse response filter or an infinite impulse response filter, etc.
  • the following is an example of measuring the human thorax.
  • Figure 4(a) shows the time domain signal of the measurement data. The curve in the figure represents the voltage signal measured on the specific electrode when the specific electrode is excited. Similar data were obtained for other stimulus-measurement situations. It should be noted that the ordinate in the figure is the value directly read from the digital voltmeter, which has not yet been converted into a voltage value.
  • Figure 4(b) shows the frequency domain signal of the measurement data. The signal shown in Fig.
  • FIG. 4(b) is obtained by Fourier transform of the signal shown in Fig. 4(a). From Figure 4(b), the ventilation-related signals and blood perfusion-related signals can be distinguished.
  • a low-pass filter which can be a finite impulse response low-pass digital filter, is designed.
  • the cutoff frequency of the filter is greater than the second harmonic frequency of the ventilation-related signal, and less than the fundamental wave of the blood perfusion-related signal. frequency.
  • the filtered signal graphs are shown in Figure 5(a) and Figure 5(b), wherein Figure 5(a) is the time domain signal graph, and Figure 5(b) is the frequency domain signal graph.
  • a method based on PCA Principal Component Analysis, principal component analysis
  • the measurement signal is u. Its size is N t ⁇ N c , where N t is the number of sampling points, and N c is the number of features (here, the number of measurement channels).
  • the first several principal components are used as templates to perform template matching filtering on the signal u to obtain a ventilation-related signal u V .
  • a neural network based method is used to extract ventilation related signals.
  • the neural network-based method is divided into two stages: training and prediction.
  • training phase a ventilation-related signal extraction network is trained by supervised or unsupervised methods using the training data; in the prediction phase, the trained ventilation-related signal extraction network is used to extract the ventilation-related signals in the electrical impedance measurement signal.
  • a three-dimensional ventilation image is reconstructed by an image reconstruction algorithm using the ventilation-related signals extracted in the second step.
  • the three-dimensional ventilation image reflects changes in electrical impedance in the area of the human body to be measured due to breathing.
  • the image reconstruction algorithm is a linear difference reconstruction algorithm. The following is an example of reconstructing a three-dimensional ventilation image by a linear difference reconstruction algorithm.
  • the EIT differential reconstruction can be formulated as the following least squares problem:
  • J is the Jacobian matrix
  • ⁇ is the electrical conductivity change in the human body due to ventilation at the above two times
  • R is the regularization matrix
  • ⁇ is the regularization parameter.
  • the reference time t ref can either be set to be fixed in the entire image reconstruction process, or can be set to be dynamically updated as the image reconstruction process progresses.
  • ⁇ is defined in a discretized 3D model, such as a tetrahedral grid or a voxel grid.
  • ⁇ * is the calculated three-dimensional ventilation image.
  • FIG. 6 shows a schematic diagram of a three-dimensional ventilation image of the human thorax produced by the above method.
  • the image reconstruction algorithm is a method based on machine learning.
  • EIT differential imaging can be expressed as:
  • ⁇ u is the change of the measured data at different times
  • ⁇ is the change of the conductivity at the corresponding time.
  • the machine learning-based method is divided into two stages: training and prediction.
  • training phase Given the training data ⁇ u i , ⁇ i ⁇ , a machine learning model can be trained to approximate the operator
  • prediction stage given the differential measurement signal ⁇ u, it can be obtained by to predict the corresponding conductivity change:
  • the method for generating a three-dimensional ventilation image in this embodiment includes the following steps: first, measure the electrical impedance of the chest region of the human body to be measured, then reconstruct a three-dimensional differential image, and finally extract a three-dimensional ventilation from the three-dimensional differential image image.
  • the specific process is as follows.
  • the first step is to measure the electrical impedance of the thoracic region of the human body to be tested.
  • a three-dimensional differential image is reconstructed by an image reconstruction algorithm using the electrical impedance signal measured in the previous step.
  • the three-dimensional differential image reflects changes in electrical impedance in the thoracic cavity of the human body to be measured, and the changes in electrical impedance may be caused by human ventilation or blood perfusion.
  • the image reconstruction algorithm may adopt the above-mentioned image reconstruction algorithm.
  • Figure 7 shows a three-dimensional difference image generated using the data shown in Figure 4 and a linear difference reconstruction algorithm.
  • the ventilation image is extracted from the three-dimensional differential image obtained in the previous step.
  • Fig. 8(a) and its corresponding spectrogram 8(b) show the time series of example pixel points of the three-dimensional difference image of the human thorax in Fig. 7 .
  • the ventilation-related signals and blood perfusion-related signals can be distinguished.
  • a low-pass filter which can be a finite impulse response low-pass digital filter, is designed.
  • the cutoff frequency of the filter is greater than the second harmonic frequency of the ventilation-related signal and less than the fundamental wave of the blood perfusion-related signal. frequency.
  • Fig. 9(a) and spectrogram 9(b) show the time domain signal after filtering the example pixel point in Fig. 7 .
  • a three-dimensional ventilation image can be obtained, as shown in FIG. 10 .
  • ventilation images can be extracted using principal component analysis based and neural network based methods.
  • FIG.8(a), FIG.8(b), FIG.9(a), and FIG.9(b) is an arbitrary unit.
  • the method for generating a 3D ventilation image applied to a human thoracic cavity in this embodiment provides a 3D ventilation image that can reflect changes in electrical impedance of the human thoracic cavity caused by human ventilation, thereby reflecting the ventilation conditions of each volume of the human thoracic cavity in a three-dimensional space.
  • an embodiment of the present disclosure provides a three-dimensional ventilation image generation controller.
  • the three-dimensional ventilation image generation controller of this embodiment includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the steps of the methods described in the first and second embodiments are implemented.
  • an embodiment of the present disclosure further provides a three-dimensional ventilation image generating device.
  • the device for generating a three-dimensional ventilation image in this embodiment includes: an electrode array distributed three-dimensionally on the periphery of the target area to be measured, which is used to measure the electrical impedance of the target area to be measured, and send the measured electrical impedance to the three-dimensional ventilation image generating controller; and the three-dimensional ventilation image generating controller described in the third embodiment.
  • the three-dimensional ventilation image generating device of this embodiment further includes: an image display device configured to generate and display the three-dimensional ventilation image generated by the controller on the three-dimensional ventilation image.
  • one or more embodiments of the above solutions may have the following advantages or beneficial effects: applying the three-dimensional ventilation image generation method of the present disclosure, through the signal extraction algorithm and the image reconstruction algorithm, according to the target area to be measured.
  • the electrical impedance signal obtained by the electrical impedance measurement generates a three-dimensional ventilation image, in which the electrical impedance measurement of the target area to be measured is realized by an electrode array that is three-dimensionally distributed on the periphery of the target area to be measured, and a three-dimensional ventilation image can be provided to reflect the human thoracic cavity. Ventilation within each volume in three-dimensional space.

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Abstract

一种三维通气图像产生方法、控制器及装置，方法包括：通过信号提取算法和图像重建算法，根据对待测目标区域进行电阻抗测量得到的电阻抗信号，生成三维通气图像，其中，对待测目标区域进行电阻抗测量利用在待测目标区域的外围呈三维分布的电极阵列实现。

Description

一种三维通气图像产生方法、控制器及装置
相关申请的交叉引用
本公开要求享有2021年01月26日提交的名称为“一种三维通气图像产生方法、控制器及装置”的中国专利申请CN202110111098.8的优先权，其全部内容通过引用并入本公开中。
技术领域
本公开属于电阻抗成像应用技术领域，具体涉及一种三维通气图像产生方法、控制器及装置。
背景技术
EIT(Electrical Impedance Tomography，电阻抗成像)技术是一种无创的、以人体或其他生物体内部的电阻率分布为目标的重建体内组织图像的技术。人体是一个大的生物电导体，各组织、器官均有一定的阻抗，当人体的局部器官发生病变时，局部部位的阻抗必然与其他部位不同，因而可以通过阻抗的测量来对人体器官的病变进行诊断。
现有技术只能生成二维的通气图像，这个二维的图像反映的是待测人体胸腔区域某个断面内由于气体含量变化引起的电阻抗变化。然而，二维的图像难以反映人体胸腔在三维空间中某个体积内的通气情况。
现在亟须一种三维通气图像产生方法、控制器及装置。
发明内容
本公开所要解决的技术问题是如何生成三维通气图像，从而反映人体胸腔在三维空间中各个体积内的通气情况。
针对上述问题，本公开提供了一种三维通气图像产生方法、控制器及装置。
第一方面，本公开提供了一种三维通气图像产生方法，包括以下步骤：通过信号提取算法和图像重建算法，根据对待测目标区域进行电阻抗测量得到的电阻抗信号，生成三维通气图像，其中，对待测目标区域进行电阻抗测量利用在待测目标区域的外围呈三维分布的电极阵列实现。
第二方面，本公开提供了一种三维通气图像产生控制器，其包括存储器和处理器，该存储器上存储有计算机程序，该计算机程序被处理器执行时实现上述方法的步骤。
第三方面，本公开提供了一种三维通气图像产生装置，包括：在待测目标区域的外围呈三维分布的电极阵列，其用于对待测目标区域进行电阻抗测量，并将测量得到的电阻抗 发送至三维通气图像产生控制器；和上述三维通气图像产生控制器。
本公开的其它特征和优点将在随后的说明书中阐述，并且部分地从说明书中变得显而易见，或者通过实施本公开而了解。本公开的目的和其他优点可通过在说明书、权利要求书以及附图中所特别指出的结构来实现和获得。
附图说明
附图用来提供对本公开的进一步理解，并且构成说明书的一部分，与本公开的实施例共同用于解释本公开，并不构成对本公开的限制。在附图中：
图1示出了本公开实施例一的三维通气图像产生方法的流程图；
图2示出了本公开实施例一的三维通气图像产生方法的另一流程图；
图3(a)示出了本公开实施例二的三维通气图像产生方法的流程图；
图3(b)示出了本公开实施例二的三维通气图像产生方法的另一流程图；
图4(a)示出了本公开实施例二的人体胸腔测量数据的时域信号示意图；
图4(b)示出了本公开实施例二的人体胸腔测量数据的频域信号示意图；
图5(a)示出了本公开实施例二的人体胸腔测量数据滤波后的通气相关信号的时域信号示意图；
图5(b)示出了本公开实施例二的人体胸腔测量数据滤波后的通气相关信号的频域信号示意图；
图6示出了本公开实施例二利用图3(a)中示出的三维通气图像产生方法生成的人体胸腔三维通气图像示意图；
图7示出了本公开实施例二利用图3(b)中示出的三维通气图像产生方法生成的人体胸腔三维差分图像示意图；
图8(a)示出了图7中示例像素点的时域信号示意图；
图8(b)示出了图7中示例像素点的频域信号示意图；
图9(a)示出了图7中示例像素点数据滤波后的通气相关信号的时域信号示意图；
图9(b)示出了图7中示例像素点数据滤波后的通气相关信号的频域信号示意图；
图10示出了本公开实施例二利用图3(b)中示出的三维通气图像产生方法生成的人体胸腔三维通气图像示意图。
具体实施方式
以下将结合附图及实施例来详细说明本公开的实施方式，借此对本公开如何应用技术手段来解决技术问题，并达成技术效果的实现过程能充分理解并据以实施。需要说明的是， 只要不构成冲突，本公开中的各个实施例以及各实施例中的各个特征可以相互结合，所形成的技术方案均在本公开的保护范围之内。
实施例一
为解决现有技术中存在的上述技术问题，本公开实施例提供了一种三维通气图像产生方法，其中，本实施例的三维通气图像产生方法通过两种方式实现，具体如图1和图2所示。
参照图1，本实施例的三维通气图像产生方法，包括以下步骤。
S110，通过信号提取算法从对待测目标区域进行电阻抗测量得到的电阻抗信号中提取通气相关信号，其中，对待测目标区域进行电阻抗测量利用在待测目标区域的外围呈三维分布的电极阵列实现，电极阵列可以采用多条阻抗带或者是采用电极呈三维分布的电极背心。
S120，通过图像重建算法根据所述通气相关信号重建三维通气图像。
在一种实施方式中，所述电阻抗信号包括通气相关信号和血液灌注相关信号，通过信号提取算法从对待测目标区域进行电阻抗测量得到的电阻抗信号中提取通气相关信号，包括以下步骤：利用低通滤波器从对待测目标区域进行电阻抗测量得到的电阻抗信号中提取通气相关信号，其中，所述低通滤波器的截止频率大于通气相关信号的二次谐波频率，且小于血液灌注相关信号的基波频率。
在步骤S110中，所述信号提取算法为频域滤波法、主成分分析法和神经网络法中的任意一种算法。
在步骤S120中，所述图像重建算法为线性差分重建算法或基于神经网络的图像重建算法。
参照图2，本实施例的三维通气图像产生方法，包括以下步骤。
S210，通过图像重建算法根据对待测目标区域进行电阻抗测量得到的电阻抗信号重建三维图像，其中，对待测目标区域进行电阻抗测量利用在待测目标区域的外围呈三维分布的电极阵列实现。
S220，根据多个时刻下的三维图像数据列出三维图像中每个像素的时间序列，其中，每个像素的时间序列由每个像素在不同时刻下的值组成。
S230，从三维图像中每个像素的时间序列中提取通气相关像素的时间序列。
S240，根据通气相关像素的时间序列构建三维通气图像。
在步骤S230中，从三维图像中每个像素的时间序列中提取通气相关像素的时间序列通过频域滤波法、主成分分析法和神经网络法中的任意一种算法实现。
在步骤S210中，所述图像重建算法为线性差分重建算法或基于神经网络的图像重建算 法。
实施例二
为解决现有技术中存在的上述技术问题，本公开实施例基于实施例一提供了一种应用于人体胸腔的三维通气图像产生方法，其中，本实施例的三维通气图像产生方法通过两种方式实现，具体如图3(a)和图3(b)所示。
如图3(a)所示，本实施例的三维通气图像产生方法包括以下步骤：首先，对待测人体胸腔区域进行电阻抗测量；然后，从测量信号中提取通气相关信号；最后，重建三维通气图像。具体过程如下。
第一步，对待测人体胸腔区域进行电阻抗测量。在所述电阻抗测量中，首先，需要在待测人体胸腔周围固定电极阵列。所述电极阵列包含若干个分布在三维空间内的电极。然后，通过电极阵列对待测人体胸腔进行激励并测量由此产生的响应，即：轮流对电极施加电流激励，并依次在其他电极上测量由此产生的电压信号。
第二步，从上一步测量得到的电阻抗信号中提取通气相关信号。在本步骤的一个实施例中，利用滤波器从所测量的电阻抗信号中提取通气相关信号。滤波器可以使用有限脉冲响应滤波器或无限脉冲响应滤波器等。下面是对人体胸腔进行测量的实施例。图4(a)示出了测量数据的时域信号。图中曲线代表特定电极激励时，在特定电极上测量得到的电压信号。其他激励-测量情况得到的数据与之类似。需要说明的是，图中纵坐标为从数字电压表直接读取的数值，仍未将其转换为电压值。图4(b)示出了测量数据的频域信号。图4(b)所示信号由图4(a)所示信号经过傅里叶变换得到。由图4(b)可以分辨出通气相关信号和血液灌注相关信号。为了提取通气相关信号，设计一个低通滤波器，可以是有限脉冲响应低通数字滤波器，该滤波器的截止频率大于通气相关信号的二次谐波频率，且小于血液灌注相关信号的基波频率。滤波后的信号图形如图5(a)和图5(b)所示，其中，图5(a)为时域信号图形，图5(b)为频域信号图形。
在本步骤的另一个实施例中，使用基于PCA(Principle Component Analysis，主成分分析)的方法提取通气相关信号。具体地，假设测量信号为u。其尺寸为N t×N c，其中，N t为采样点数，N c为特征数(在这里为测量通道数)。利用主成分分析得到信号的主成分
Figure PCTCN2021132763-appb-000001
其中，p i(i＝1,2,…,N c)的尺寸为N t×1，且其对应的特征值依次减小。将前若干个主成分(如p 1，p 2)作为模板对信号u进行模板匹配滤波，得到通气相关信号u V。
在本步骤的另一个实施例中，使用基于神经网络的方法提取通气相关信号。具体地，所述基于神经网络的方法分为训练和预测两个阶段。在训练阶段，利用训练数据通过有监督或无监督的方法训练一个通气相关信号提取网络；在预测阶段，利用训练好的通气相关 信号提取网络来提取电阻抗测量信号中的通气相关信号。
第三步，利用第二步所提取的通气相关信号，通过图像重建算法重建三维通气图像。所述三维通气图像反映由于呼吸引起的待测人体区域内的电阻抗变化。在本步骤的一个实施例中，所述图像重建算法是线性差分重建算法。下面是线性差分重建算法进行重建三维通气图像的实施例。
假设第二步所提取出的通气相关信号的时域形式为u(t)，其中，t为时间变量。EIT差分重建可以表述为如下最小二乘问题：
min δσ‖J·δσ-δu‖ 2+α‖R·δσ‖ 2，
其中，J为雅可比矩阵，δu＝u(t)-u(t ref)为信号在时刻t相对于参考时刻t ref的变化，δσ为上述两个时刻人体内由于通气引起的电导率变化，R为正则化矩阵，α为正则化参数。参考时刻t ref既可以设置为在整个图像重建过程中固定不变，也可以设置为随着图像重建过程的进行而动态地更新。δσ定义在离散化的三维模型中，如四面体网格或体素网格。上述问题的解为
δσ *＝(J T·J+αR T·R) -1· T·δu.
令D＝(J T·J+αR T·R) -1·J T，则上述公式可以表示为：
δσ *＝D·δu.
上述δσ *即为计算所得的三维通气图像。
图6示出了利用上述方法所产生的人体胸腔的三维通气图像的一个示意图。
在本步骤的另一个实施例中，所述图像重建算法为基于机器学习的方法。EIT差分成像可以表示为：
Figure PCTCN2021132763-appb-000002
其中，
Figure PCTCN2021132763-appb-000003
为重建算子，δu为不同时刻测量数据的变化，δσ为相应时刻电导率的变化。所述基于机器学习的方法分为训练和预测两个阶段。首先，在训练阶段，给定训练数据{δu i,δσ i}，可以训练一个机器学习模型
Figure PCTCN2021132763-appb-000004
来近似算子
Figure PCTCN2021132763-appb-000005
在预测阶段，给定差分测量信号δu，可以通过
Figure PCTCN2021132763-appb-000006
来预测相应的电导率变化：
Figure PCTCN2021132763-appb-000007
除了上述实施例中的图像重建算法外，本步骤还可以使用各种线性的或非线性的、迭代的或非迭代的、随机的或确定性的图像重建算法。
如图3(b)所示，本实施例的三维通气图像产生方法包括以下步骤：首先，通过对待测人体胸腔区域进行电阻抗测量，然后重建三维差分图像，最后从三维差分图像中提取三维通气图像。具体过程如下。
第一步，对待测人体胸腔区域进行电阻抗测量。
第二步，利用上一步测量得到的电阻抗信号，通过图像重建算法重建三维差分图像。所述三维差分图像反映了待测人体胸腔内的电阻抗变化，该电阻抗变化可能是由人体通气或血液灌注引起的。所述图像重建算法可以采用上述图像重建算法。图7示出了利用图4所示数据和线性差分重建算法生成的三维差分图像。
第三步，从上一步得到的三维差分图像中提取通气图像。在本步骤的一个实施例中，利用滤波器从三维差分图像中提取通气图像。假设N个时刻的三维差分图像可排列为一个矩阵A＝{a 1,a 2,…,a M} T，其中，a i(i＝1,2,…,M)为像素i在N个时刻的值所组成的列向量，M为三维图像中像素的总数。对每个像素i的时间序列a i(i＝1,2,…,M)进行低通滤波可以得到通气图像上对应像素的时间序列。具体地，假设滤波函数为f(·)，则通气图像为A V＝{f(a 1),f(a 2),…,f(a M)} T。
图8(a)和其对应的频谱图8(b)示出了图7中人体胸腔三维差分图像示例像素点的时间序列。由图8(b)可以分辨出通气相关信号和血液灌注相关信号。为了提取通气相关信号，设计一个低通滤波器，可以是有限脉冲响应低通数字滤波器，该滤波器的截止频率大于通气相关信号的二次谐波频率，且小于血液灌注相关信号的基波频率。
图9(a)和频谱图9(b)示出了对图7中示例像素点进行滤波后的时域信号。对三维差分图像中的每个像素进行上述低通滤波后，即可得到三维通气图像，如图10所示。在本步骤的另外两个实施例中，可以使用基于主成分分析和基于神经网络的方法提取通气图像。
需要说明的是，图8(a)、图8(b)、图9(a)和图9(b)中的A.U.为任意单位。
本实施例的应用于人体胸腔的三维通气图像产生方法提供了能够反映人体胸腔内由于人体通气引起的电阻抗变化的三维通气图像，从而反映人体胸腔在三维空间中各个体积内的通气情况。
实施例三
为解决现有技术中存在的上述技术问题，本公开实施例提供了一种三维通气图像产生控制器。
本实施例的三维通气图像产生控制器，其包括存储器和处理器，该存储器上存储有计算机程序，该计算机程序被处理器执行时实现实施例一和实施例二所述的方法的步骤。
实施例四
为解决现有技术中存在的上述技术问题，本公开实施例还提供了一种三维通气图像产生装置。
本实施例的三维通气图像产生装置，包括：在待测目标区域的外围呈三维分布的电极 阵列，其用于对待测目标区域进行电阻抗测量，并将测量得到的电阻抗发送至三维通气图像产生控制器；以及实施例三所述的三维通气图像产生控制器。
本实施例的三维通气图像产生装置，还包括：图像显示装置，其用于对所述三维通气图像产生控制器产生的三维通气图像并进行显示。
与现有技术相比，上述方案中的一个或多个实施例可以具有如下优点或有益效果：应用本公开的三维通气图像产生方法，通过信号提取算法和图像重建算法，根据对待测目标区域进行电阻抗测量得到的电阻抗信号，生成三维通气图像，其中，对待测目标区域进行电阻抗测量利用在待测目标区域的外围呈三维分布的电极阵列实现，能够提供三维通气图像，从而反映人体胸腔在三维空间中各个体积内的通气情况。
虽然本公开所公开的实施方式如上，但所述的内容只是为了便于理解本公开而采用的实施方式，并非用以限定本公开。任何本公开所属技术领域内的技术人员，在不脱离本公开所公开的精神和范围的前提下，可以在实施的形式上及细节上作任何的修改与变化，但本公开的保护范围，仍须以所附的权利要求书所界定的范围为准。

Claims (10)

  1. 一种三维通气图像产生方法，包括以下步骤：
    通过信号提取算法和图像重建算法，根据对待测目标区域进行电阻抗测量得到的电阻抗信号，生成三维通气图像，其中，对待测目标区域进行电阻抗测量利用在待测目标区域的外围呈三维分布的电极阵列实现。
  2. 根据权利要求1所述的方法，其中，通过信号提取算法和图像重建算法，根据对待测目标区域进行电阻抗测量得到的电阻抗信号，生成三维通气图像，包括以下步骤：
    通过信号提取算法从对待测目标区域进行电阻抗测量得到的电阻抗信号中提取通气相关信号；
    通过图像重建算法根据所述通气相关信号重建三维通气图像。
  3. 根据权利要求2所述的方法，其中，所述电阻抗信号包括通气相关信号和血液灌注相关信号，通过信号提取算法从对待测目标区域进行电阻抗测量得到的电阻抗信号中提取通气相关信号，包括以下步骤：
    利用低通滤波器从对待测目标区域进行电阻抗测量得到的电阻抗信号中提取通气相关信号，其中，所述低通滤波器的截止频率大于通气相关信号的二次谐波频率，且小于血液灌注相关信号的基波频率。
  4. 根据权利要求1所述的方法，其中，通过信号提取算法和图像重建算法，根据对待测目标区域进行电阻抗测量得到的电阻抗信号，生成三维通气图像，包括以下步骤：
    通过图像重建算法根据对待测目标区域进行电阻抗测量得到的电阻抗信号重建三维图像；
    通过信号提取算法从所述三维图像中提取三维通气图像。
  5. 根据权利要求4所述的方法，其中，通过信号提取算法从所述三维图像中提取三维通气图像，包括以下步骤：
    根据多个时刻下的三维图像数据列出三维图像中每个像素的时间序列，其中，每个像素的时间序列由每个像素在不同时刻下的值组成；
    从三维图像中每个像素的时间序列中提取通气相关像素的时间序列；
    根据通气相关像素的时间序列构建三维通气图像。
  6. 根据权利要求5所述的方法，其中，从三维图像中每个像素的时间序列中提取通气相关像素的时间序列通过频域滤波法、主成分分析法和神经网络法中的任意一种算法实现。
  7. 根据权利要求2或4所述的方法，其中，所述信号提取算法为频域滤波法、主成分分析法和神经网络法中的任意一种算法。
  8. 根据权利要求2或4所述的方法，其中，所述图像重建算法为线性差分重建算法或基于神经网络的图像重建算法。
  9. 一种三维通气图像产生控制器，其包括存储器和处理器，其中，该存储器上存储有计算机程序，该计算机程序被处理器执行时实现如权利要求1至8中任一项所述方法的步骤。
  10. 一种三维通气图像产生装置，其中，包括：
    在待测目标区域的外围呈三维分布的电极阵列，其用于对待测目标区域进行电阻抗测量，并将测量得到的电阻抗发送至三维通气图像产生控制器；和
    根据权利要求9所述的三维通气图像产生控制器。
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