阅读说明：本技术 一种三维通气图像产生方法、控制器及装置 (Three-dimensional ventilation image generation method, controller and device ) 是由 张可 张昕 管明涛 王谊冰 于 2021-01-26 设计创作，主要内容包括：本发明公开了一种三维通气图像产生方法、控制器及装置,所述方法包括：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。本发明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。(The invention discloses a three-dimensional ventilation image generation method, a controller and a device, wherein the method comprises the following steps: and generating a three-dimensional ventilation image according to an electrical impedance signal obtained by carrying out electrical impedance measurement on the target area to be measured by a signal extraction algorithm and an image reconstruction algorithm, wherein the electrical impedance measurement on the target area to be measured is realized by using an electrode array which is three-dimensionally distributed on the periphery of the target area to be measured. The invention provides a three-dimensional ventilation image which can reflect the electrical impedance change in the human thorax caused by the ventilation of the human body, thereby reflecting the ventilation condition of the human thorax in each volume in a three-dimensional space.)

1. A method of generating a three-dimensional ventilation image, comprising the steps of:

and generating a three-dimensional ventilation image according to an electrical impedance signal obtained by carrying out electrical impedance measurement on the target area to be measured by a signal extraction algorithm and an image reconstruction algorithm, wherein the electrical impedance measurement on the target area to be measured is realized by using an electrode array which is three-dimensionally distributed on the periphery of the target area to be measured.

2. The method according to claim 1, wherein the generation of the three-dimensional ventilation image from the electrical impedance signals obtained by electrical impedance measurement of the target region to be measured by means of a signal extraction algorithm and an image reconstruction algorithm comprises the following steps:

extracting ventilation related signals from electrical impedance signals obtained by carrying out electrical impedance measurement on a target region to be measured by a signal extraction algorithm;

and reconstructing a three-dimensional ventilation image according to the ventilation related signal through an image reconstruction algorithm.

3. The method of claim 2, wherein the electrical impedance signals comprise ventilation-related signals and blood perfusion-related signals, and wherein the ventilation-related signals are extracted from the electrical impedance signals obtained by electrical impedance measurements of the target region to be measured by a signal extraction algorithm, comprising the steps of:

and extracting a ventilation related signal from an electrical impedance signal obtained by measuring the electrical impedance of the target region to be measured by using a low-pass filter, 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 fundamental frequency of the blood perfusion related signal.

4. The method according to claim 1, wherein the generation of the three-dimensional ventilation image from the electrical impedance signals obtained by electrical impedance measurement of the target region to be measured by means of a signal extraction algorithm and an image reconstruction algorithm comprises the following steps:

reconstructing a three-dimensional image according to an electrical impedance signal obtained by performing electrical impedance measurement on a target region to be measured by an image reconstruction algorithm;

and extracting a three-dimensional ventilation image from the three-dimensional image through a signal extraction algorithm.

5. The method according to claim 4, wherein extracting a three-dimensional ventilation image from the three-dimensional image by a signal extraction algorithm comprises the steps of:

listing a time sequence of each pixel in the three-dimensional image according to the three-dimensional image data at a plurality of moments, wherein the time sequence of each pixel consists of the values of each pixel at different moments;

extracting a time series of ventilation-related pixels from the time series of each pixel in the three-dimensional image;

a three-dimensional ventilation image is constructed from the time series of ventilation-related pixels.

6. The method of claim 5, wherein the extracting the time series of ventilation-related pixels from the time series of each pixel in the three-dimensional image is performed by any one of a frequency domain filtering method, a principal component analysis method, and a neural network method.

7. The method according to claim 2 or 4, wherein the signal extraction algorithm is any one of a frequency domain filtering method, a principal component analysis method, and a neural network method.

8. The method according to claim 2 or 4, wherein the image reconstruction algorithm is a linear differential reconstruction algorithm or a neural network based image reconstruction algorithm.

9. A three-dimensional ventilation image generation controller comprising a memory and a processor, characterized in that the memory has stored thereon a computer program which, when executed by the processor, carries out the steps of the method according to any one of claims 1 to 8.

10. A three-dimensional ventilation image generation apparatus, comprising:

the electrode array is distributed in three dimensions on the periphery of the target area to be measured and is used for carrying out electrical impedance measurement on the target area to be measured and sending the measured electrical impedance to the three-dimensional ventilation image generation controller; and

the three-dimensional ventilation image generation controller of claim 9.

Technical Field

The invention belongs to the technical field of electrical impedance imaging application, and particularly relates to a three-dimensional ventilation image generation method, a controller and a device.

Background

EIT (Electrical Impedance Tomography) is a technique for reconstructing an image of tissue in a body without wound, targeting the Electrical resistivity distribution inside the human body or other living bodies. The human body is a large biological electric conductor, each tissue and organ has certain impedance, and when the local organ of the human body is diseased, the impedance of the local organ is different from that of other parts, so that the disease of the organ of the human body can be diagnosed by measuring the impedance.

In the prior art, only two-dimensional ventilation images can be generated, and the two-dimensional images reflect the electrical impedance change caused by the change of gas content in a certain section of the chest region of a human body to be detected. However, it is difficult to reflect the ventilation of the human thorax in a certain volume in three-dimensional space.

There is a need for a method, controller and apparatus for generating a three-dimensional ventilation image.

Disclosure of Invention

The invention aims to solve the technical problem of how to generate a three-dimensional ventilation image so as to reflect the ventilation condition of the human thorax in each volume in a three-dimensional space.

In view of the above problems, the present invention provides a three-dimensional ventilation image generation method, a controller and an apparatus.

In a first aspect, the present invention provides a method for generating a three-dimensional ventilation image, comprising the steps of:

and generating a three-dimensional ventilation image according to an electrical impedance signal obtained by carrying out electrical impedance measurement on the target area to be measured by a signal extraction algorithm and an image reconstruction algorithm, wherein the electrical impedance measurement on the target area to be measured is realized by using an electrode array which is three-dimensionally distributed on the periphery of the target area to be measured.

In some embodiments of the present invention, a three-dimensional ventilation image is generated according to an electrical impedance signal obtained by performing electrical impedance measurement on a target region to be measured by a signal extraction algorithm and an image reconstruction algorithm, and the method includes the following steps:

extracting ventilation related signals from electrical impedance signals obtained by carrying out electrical impedance measurement on a target region to be measured by a signal extraction algorithm;

and reconstructing a three-dimensional ventilation image according to the ventilation related signal through an image reconstruction algorithm.

In some embodiments of the present invention, the electrical impedance signals include a ventilation-related signal and a blood perfusion-related signal, and the ventilation-related signal is extracted from the electrical impedance signals obtained by performing electrical impedance measurement on the target region to be measured by a signal extraction algorithm, including the following steps:

and extracting a ventilation related signal from an electrical impedance signal obtained by measuring the electrical impedance of the target region to be measured by using a low-pass filter, 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 fundamental frequency of the blood perfusion related signal.

In some embodiments of the present invention, a three-dimensional ventilation image is generated according to an electrical impedance signal obtained by performing electrical impedance measurement on a target region to be measured by a signal extraction algorithm and an image reconstruction algorithm, and the method includes the following steps:

reconstructing a three-dimensional image according to an electrical impedance signal obtained by performing electrical impedance measurement on a target region to be measured by an image reconstruction algorithm;

and extracting a three-dimensional ventilation image from the three-dimensional image through a signal extraction algorithm.

In some embodiments of the invention, extracting a three-dimensional ventilation image from the three-dimensional image by a signal extraction algorithm comprises the steps of:

listing a time sequence of each pixel in the three-dimensional image according to the three-dimensional image data at a plurality of moments, wherein the time sequence of each pixel consists of the values of each pixel at different moments;

extracting a time series of ventilation-related pixels from the time series of each pixel in the three-dimensional image;

a three-dimensional ventilation image is constructed from the time series of ventilation-related pixels.

In some embodiments of the present invention, the extracting of the time-series of ventilation-related pixels from the time-series of each pixel in the three-dimensional image is performed by any one of a frequency-domain filtering method, a principal component analysis method, and a neural network method.

In some embodiments of the present invention, the signal extraction algorithm is any one of a frequency domain filtering method, a principal component analysis method, and a neural network method.

In some embodiments of the invention, the image reconstruction algorithm is a linear differential reconstruction algorithm or a neural network based image reconstruction algorithm.

In a second aspect, the invention provides a three-dimensional ventilation image generation controller comprising a memory having stored thereon a computer program which, when executed by a processor, performs the steps of the method described above.

In a third aspect, the present invention provides a three-dimensional ventilation image generation apparatus comprising:

the electrode array is distributed in three dimensions on the periphery of the target area to be measured and is used for carrying out electrical impedance measurement on the target area to be measured and sending the measured electrical impedance to the three-dimensional ventilation image generation controller; and

the three-dimensional ventilation image generation controller.

Compared with the prior art, one or more embodiments in the above scheme can have the following advantages or beneficial effects:

by applying the three-dimensional ventilation image generation method, the three-dimensional ventilation image is generated according to the electrical impedance signal obtained by carrying out electrical impedance measurement on the target region to be measured by the signal extraction algorithm and the image reconstruction algorithm, wherein the electrical impedance measurement on the target region to be measured is realized by using the electrode array which is three-dimensionally distributed on the periphery of the target region to be measured, and the three-dimensional ventilation image can be provided, so that the ventilation condition of the human thorax in each volume in the three-dimensional space is reflected.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:

FIG. 1 illustrates a flow chart of a method of three-dimensional ventilation image generation in accordance with an embodiment of the present invention;

FIG. 2 illustrates another flow chart of a method of generating a three-dimensional ventilation image in accordance with an embodiment of the present invention;

FIG. 3(a) shows a flow chart of a two-dimensional ventilation image generation method according to an embodiment of the invention;

FIG. 3(b) shows another flow chart of a two-dimensional ventilation image generation method according to an embodiment of the invention;

FIG. 4(a) is a schematic time domain signal diagram of measured data of human thorax according to a second embodiment of the present invention;

FIG. 4(b) is a schematic diagram showing the frequency domain signals of measured data of human thorax according to the second embodiment of the present invention;

FIG. 5(a) is a time domain signal diagram of a ventilation-related signal after filtering of human thorax measurement data according to a second embodiment of the present invention;

FIG. 5(b) is a frequency domain signal diagram of a ventilation-related signal after filtering of human thorax measurement data according to a second embodiment of the present invention;

FIG. 6 is a schematic diagram of a three-dimensional ventilation image of the chest of a human body generated by the three-dimensional ventilation image generation method shown in FIG. 3(a) according to the second embodiment of the present invention;

FIG. 7 is a schematic diagram of a three-dimensional difference image of the human thorax generated by the three-dimensional ventilation image generation method shown in FIG. 3(b) according to the second embodiment of the present invention;

FIG. 8(a) shows a time domain signal diagram of the exemplary pixel of FIG. 7;

FIG. 8(b) shows a frequency domain signal diagram of the exemplary pixel of FIG. 7;

FIG. 9(a) shows a time domain signal diagram of the ventilation-related signal after filtering of the example pixel point data of FIG. 7;

fig. 9(b) shows a frequency domain signal diagram of the ventilation-related signal after filtering of the example pixel point data of fig. 7;

fig. 10 is a schematic diagram of a three-dimensional ventilation image of the chest of a human body generated by the three-dimensional ventilation image generation method shown in fig. 3(b) according to the second embodiment of the present invention.

Detailed Description

The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.

Example one

In order to solve the above technical problems in the prior art, an embodiment of the present invention provides a three-dimensional ventilation image generation method, wherein the three-dimensional ventilation image generation method of the present embodiment is implemented in two ways, as specifically shown in fig. 1 and 2.

Referring to fig. 1, the three-dimensional ventilation image generation method of the present embodiment includes the following steps:

s110, extracting ventilation related signals from electrical impedance signals obtained by performing electrical impedance measurement on a target area to be measured by a signal extraction algorithm, wherein the electrical impedance measurement on the target area to be measured is realized by using an electrode array which is distributed in three dimensions on the periphery of the target area to be measured, and the electrode array can adopt a plurality of impedance bands or an electrode vest with the electrodes distributed in three dimensions;

and S120, reconstructing a three-dimensional ventilation image according to the ventilation related signal through an image reconstruction algorithm.

In one embodiment, the electrical impedance signals include a ventilation-related signal and a blood perfusion-related signal, and the ventilation-related signal is extracted from the electrical impedance signals obtained by measuring the electrical impedance of the target region to be measured by a signal extraction algorithm, and the method includes the following steps:

and extracting a ventilation related signal from an electrical impedance signal obtained by measuring the electrical impedance of the target region to be measured by using a low-pass filter, 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 fundamental frequency of the blood perfusion related signal.

In step S110, the signal extraction algorithm is any one of a frequency domain filtering method, a principal component analysis method, and a neural network method.

In step S120, the image reconstruction algorithm is a linear difference reconstruction algorithm or a neural network-based image reconstruction algorithm.

Referring to fig. 2, the three-dimensional ventilation image generation method of the present embodiment includes the following steps:

s210, reconstructing a three-dimensional image according to an electrical impedance signal obtained by performing electrical impedance measurement on a target area to be measured by using an image reconstruction algorithm, wherein the electrical impedance measurement on the target area to be measured is realized by using an electrode array which is three-dimensionally distributed on the periphery of the target area to be measured;

s220, listing a time sequence of each pixel in the three-dimensional image according to the three-dimensional image data at a plurality of moments, wherein the time sequence of each pixel is composed of values of each pixel at different moments;

s230, extracting a time sequence of ventilation related pixels from the time sequence of each pixel in the three-dimensional image;

and S240, constructing a three-dimensional ventilation image according to the time sequence of the ventilation related pixels.

In step S230, the extraction of the time series of ventilation-related pixels from the time series of each pixel in the three-dimensional image is performed by any one of a frequency domain filtering method, a principal component analysis method, and a neural network method.

In step S210, the image reconstruction algorithm is a linear difference reconstruction algorithm or a neural network-based image reconstruction algorithm.

Example two

In order to solve the above technical problems in the prior art, an embodiment of the present invention provides a three-dimensional ventilation image generation method applied to a human thorax, based on the first embodiment, wherein the three-dimensional ventilation image generation method of the present embodiment is implemented in two ways, specifically as shown in fig. 3(a) and 3 (b).

As shown in fig. 3(a), the three-dimensional ventilation image generation method of the present embodiment includes the steps of: firstly, carrying out electrical impedance measurement on a thoracic cavity region of a human body to be measured; then, extracting a ventilation-related signal from the measurement signal; and finally, reconstructing a three-dimensional ventilation image. The specific process is as follows:

firstly, carrying out electrical impedance measurement on a chest region of a human body to be measured. In the electrical impedance measurement, first, an electrode array needs to be fixed around the chest of a human body to be measured. The electrode array comprises a plurality of electrodes distributed in a three-dimensional space. Then, the chest of the human body to be tested is excited by the electrode array and the response generated thereby is measured, namely: applying current excitation to the electrodes in turn, and measuring voltage signals generated by the current excitation on other electrodes in turn;

and secondly, extracting a ventilation related signal from the electrical impedance signal obtained by the measurement in the last step. In one embodiment of this step, a ventilation-related signal is extracted from the measured electrical impedance signal using a filter. The filter may use a finite impulse response filter or an infinite impulse response filter, etc. The following is an example of a measurement of a human thorax. Fig. 4(a) shows a time domain signal of the measurement data. The curves in the graph represent the voltage signals measured at a particular electrode when the particular electrode is energized. Other excitation-measurement scenarios yield data similar to this. It should be noted that the ordinate in the figure is a numerical value directly read from the digital voltmeter, and the numerical value is not converted into a voltage value. Fig. 4(b) shows a frequency domain signal of the measurement data. The signal shown in fig. 4(b) is obtained by fourier transforming the signal shown in fig. 4 (a). The ventilation-related signal and the blood perfusion-related signal can be distinguished from fig. 4 (b). To extract the ventilation-related signal, a low-pass filter, which may be a finite impulse response low-pass digital filter, is designed with a cut-off frequency that is greater than the second harmonic frequency of the ventilation-related signal and less than the fundamental frequency of the blood perfusion-related signal. The filtered signal pattern is shown in fig. 5(a) and 5(b), where fig. 5(a) is a time domain signal pattern and fig. 5(b) is a frequency domain signal pattern.

In another embodiment of this step, the ventilation-related signal is extracted using a PCA (Principle Component Analysis) based method. Specifically, assume that the measurement signal is u. Having a size of N_t×N_cWherein N is_tNumber of sampling points, N_cIs the feature number (here the number of measurement channels). Principal component analysis of the signalWherein p is_i(i＝1,2,…,N_c) Has a size of N_tX 1, and their corresponding characteristic values decrease in order. The first several principal components (e.g. p)₁，p₂) Performing template matching filtering on the signal u as a template to obtain a ventilation related signal u_V。

In another embodiment of this step, the ventilation-related signal is extracted using a neural network-based approach. Specifically, the neural network-based approach is divided into two phases, training and prediction. In the training stage, training a ventilation related signal extraction network by using training data through a supervised or unsupervised method; in the prediction phase, a ventilation-related signal in the electrical impedance measurement signal is extracted by using the trained ventilation-related signal extraction network.

And thirdly, reconstructing a three-dimensional ventilation image through an image reconstruction algorithm by using the ventilation related signals extracted in the second step. The three-dimensional ventilation image reflects changes in electrical impedance within the body region to be measured due to respiration. In an embodiment of this step, the image reconstruction algorithm is a linear difference reconstruction algorithm. The following is an embodiment of a linear differential reconstruction algorithm for reconstructing three-dimensional ventilation images.

Let u (t) be the temporal form of the ventilation-related signal extracted in the second step, where t is a time variable. EIT differential reconstruction can be expressed as a least squares problem as follows:

min_δσ||J·δσ-δu||²+α||R·δσ||²，

wherein J is a Jacobian matrix, δ u ═ u (t) t_ref) Is a signalAt a time t relative to a reference time t_refδ σ is the change in electrical conductivity in the human body due to ventilation at the two moments, R is the regularization matrix, and α is the regularization parameter. Reference time t_refIt can be either fixed during the whole image reconstruction process or dynamically updated as the image reconstruction process progresses. δ σ is defined in a discretized three-dimensional model, such as a tetrahedral mesh or a voxel mesh. The solution of the above problem is

δσ_*＝(J^T·J+αR^T·R)^-1·J^T·δu.

Let D be (J)^T·J+αR^T·R)^-1·J^TThen the above formula can be expressed as:

δσ_*＝D·δu.

delta sigma above_*Namely the calculated three-dimensional ventilation image.

Figure 6 shows a schematic representation of a three-dimensional ventilation image of a human thorax generated using the method described above.

In another embodiment of this step, the image reconstruction algorithm is a machine learning based method. EIT differential imaging can be expressed as:

wherein the content of the first and second substances,for the reconstruction operator, δ u is the change in the measured data at different times and δ σ is the change in conductivity at the corresponding time. The machine learning-based method is divided into two stages, training and prediction. First, in the training phase, training data { δ u is givenⁱ,δσⁱCan train a machine learning modelTo approximate operatorIn the prediction phase, given a differential measurement signal δ u, can be obtainedTo predict the corresponding conductivity change:

in addition to the image reconstruction algorithms in the above embodiments, various linear or non-linear, iterative or non-iterative, random or deterministic image reconstruction algorithms may be used in this step.

As shown in fig. 3(b), the three-dimensional ventilation image generation method of the present embodiment includes the steps of: firstly, the electrical impedance measurement is carried out on the thorax region of a human body to be measured, then a three-dimensional difference image is reconstructed, and finally a three-dimensional ventilation image is extracted from the three-dimensional difference image. The specific process is as follows:

firstly, carrying out electrical impedance measurement on a chest region of a human body to be measured.

And secondly, reconstructing a three-dimensional differential image by using the electrical impedance signal obtained by the measurement in the previous step through an image reconstruction algorithm. The three-dimensional differential image reflects the electrical impedance change in the thoracic cavity of the human body to be measured, which may be caused by ventilation or blood perfusion of the human body. The image reconstruction algorithm may employ the image reconstruction algorithm described above. FIG. 7 shows a three-dimensional differential image generated using the data and linear differential reconstruction algorithm shown in FIG. 4.

And thirdly, extracting a ventilation image from the three-dimensional difference image obtained in the last step. In one embodiment of this step, a ventilation image is extracted from the three-dimensional difference image using a filter. Assume that three-dimensional differential images at N times can be arranged in a matrix a ═ a₁,a₂,…,a_M}^TWherein a is_i(i-1, 2, …, M) is a column vector consisting of the values of pixel i at N instants, and M is the total number of pixels in the three-dimensional image. Time series a for each pixel i_i(i＝1,2,…,M) The low-pass filtering may result in a time series of corresponding pixels on the ventilation image. In particular, assuming that the filter function is f (·), the ventilation image is a_V＝{f(a₁),f(a₂),…,f(a_M)}^T。

Fig. 8(a) and its corresponding spectrum fig. 8(b) show a time series of example pixel points of the three-dimensional difference image of the human thorax in fig. 7. The ventilation-related signal and the blood perfusion-related signal can be distinguished from fig. 8 (b). To extract the ventilation-related signal, a low-pass filter, which may be a finite impulse response low-pass digital filter, is designed with a cut-off frequency that is greater than the second harmonic frequency of the ventilation-related signal and less than the fundamental frequency of the blood perfusion-related signal.

Fig. 9(a) and spectrogram 9(b) show time domain signals after filtering the example pixel points in fig. 7. After the low-pass filtering is performed on each pixel in the three-dimensional difference image, a three-dimensional ventilation image is obtained, as shown in fig. 10. In two other embodiments of this step, the ventilation image may be extracted using principal component analysis-based and neural network-based methods.

The a.u. in fig. 8(a), 8(b), 9(a), and 9(b) is an arbitrary unit.

The three-dimensional ventilation image generation method applied to the human thorax of the embodiment provides a three-dimensional ventilation image capable of reflecting the electrical impedance change in the human thorax due to human ventilation, so that the ventilation condition of the human thorax in each volume in a three-dimensional space is reflected.

EXAMPLE III

In order to solve the above technical problems in the prior art, an embodiment of the present invention provides a three-dimensional ventilation image generation controller.

A three-dimensional ventilation image generation controller of the present embodiment comprises a memory and a processor, the memory having stored thereon a computer program that, when executed by the processor, performs the steps of the method of the first and second embodiments.

Example four

In order to solve the technical problems in the prior art, the embodiment of the invention also provides a three-dimensional ventilation image generation device.

The three-dimensional ventilation image generation device of the embodiment includes:

the electrode array is distributed in three dimensions on the periphery of the target area to be measured and is used for carrying out electrical impedance measurement on the target area to be measured and sending the measured electrical impedance to the three-dimensional ventilation image generation controller;

the three-dimensional ventilation image generation controller according to the third embodiment.

The three-dimensional ventilation image generation apparatus of the present embodiment further includes: and an image display device for displaying the three-dimensional ventilation image generated by the three-dimensional ventilation image generation controller.

Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.