Three-dimensional ventilation image generation method, controller, and device

Abstract

A method, controller and device for generating a three-dimensional ventilation image, the method comprising the steps of generating, via a signal extraction algorithm and an image reconstruction algorithm, a three-dimensional ventilation image according to an electrical impedance signal obtained by an electrical impedance measurement of a target area to be measured, wherein the electrical impedance measurement of the target area to be measured is achieved by using an electrode array distributed three-dimensionally around the target area to be measured.

Description

Technical Field

[0001] This disclosure claims the priority of Chinese Patent Application CN202110111098.8, titled "Three-Dimensional Ventilation Image Generation Method, Controller, and Device", filed on January 26, 2021, the entire content of which is incorporated herein by reference.

[0002] This disclosure belongs to the technical field of electrical impedance tomography applications, and specifically relates to a three-dimensional ventilation image generation method, a controller, and a device.

Background Art

[0003] EIT (Electrical Impedance Tomography) technology is a non-invasive technology for reconstructing an internal tissue image targeting the electrical resistivity distribution inside the human body or other living organisms. The human body is a large bioelectrical conductor, and each tissue and organ has a certain impedance. When a disease occurs in a local organ of the human body, the impedance of the local site should be different from that of other sites. Therefore, by measuring the impedance, the diseases of the human organs can be diagnosed.

[0004] In the existing technology, only two-dimensional ventilation images can be generated, and these two-dimensional images reflect the change in electrical impedance caused by the change in gas content in a specific cross-section of the chest region of the human body to be measured. However, it is difficult for two-dimensional images to reflect the ventilation status of the chest of the human body in a specific volume of three-dimensional space.

[0005] Currently, there is an urgent need for a three-dimensional ventilation image generation method, a controller, and a device.

Summary of the Invention

Problems to be Solved by the Invention

[0006] The technical problem to be solved by the present disclosure is whether to generate a three-dimensional ventilation image so as to reflect the ventilation status of the human chest in each volume of the three-dimensional space.

[0007] Regarding the above problem, the present disclosure provides a three-dimensional ventilation image generation method, a controller, and an apparatus.

Means for Solving the Problem

[0008] In a first aspect, the present disclosure provides a three-dimensional ventilation image generation method including generating a three-dimensional ventilation image according to an electrical impedance signal obtained by measuring the electrical impedance of a target region to be measured through a signal extraction algorithm and an image reconstruction algorithm, wherein the measurement of the electrical impedance of the target region to be measured is realized by using an electrode array three-dimensionally distributed around the target region to be measured.

[0009] In a second aspect, the present disclosure provides a three-dimensional ventilation image generation controller including a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the above method are realized.

[0010] In a third aspect, the present disclosure provides a three-dimensional ventilation image generation apparatus including an electrode array three-dimensionally distributed around a target region to be measured, which measures the electrical impedance of the target region to be measured and transmits the measured electrical impedance to a three-dimensional ventilation image generation controller, and the above three-dimensional ventilation image generation controller.

Effect of the Invention

[0011] Other features and advantages of the present disclosure will be described in the following specification, and some will be apparent from the specification or can be understood by implementing the present disclosure. The objects and other advantages of the present disclosure are realized and achieved by the structures pointed out in the specification, the claims, and the accompanying drawings.

Brief Description of the Drawings

[0012] The accompanying drawings are used to provide a further understanding of the present disclosure, form a part of the specification, and are used to explain the present disclosure together with the embodiments of the present disclosure, but are not used to limit the present disclosure.

[0013]

Figure 1

Figure 2

Figure 3A

Figure 3B

Figure 4A

Figure 4B

Figure 5A

Figure 5B

Figure 6

Figure 7

Figure 8A

Figure 8B

Figure 9A

Figure 9B

Figure 10

Mode for Carrying Out the Invention

[0014] Hereinafter, embodiments of the present disclosure will be described in detail in conjunction with the accompanying drawings and examples, so that the implementation process in which the present disclosure applies technical means to solve technical problems and achieve technical effects can be fully understood and implemented accordingly. It should be noted that, unless there is a contradiction, each embodiment of the present disclosure and each feature in each embodiment can be combined with each other, and all the formed technical solutions are within the protection scope of the present disclosure.

[0015] Example 1

[0016] In order to solve the above technical problems existing in the prior art, the embodiments of the present disclosure provide a three-dimensional ventilation image generation method, where the three-dimensional ventilation image generation method of this embodiment is realized by two methods as specifically shown in Figs. 1 and 2.

[0017] Referring to Fig. 1, the three-dimensional ventilation image generation method of this embodiment includes the following steps.

[0018] In step S110, a ventilation-related signal is extracted from an electrical impedance signal obtained by measuring the electrical impedance of a target area to be measured through a signal extraction algorithm. Here, the measurement of the electrical impedance of the target area to be measured is realized by using an electrode array three-dimensionally distributed around the target area to be measured. The electrode array can use an electrode vest having a plurality of impedance bands or a three-dimensional distribution of electrodes.

[0019] In step S120, a three-dimensional ventilation image is reconstructed according to the ventilation-related signal through an image reconstruction algorithm.

[0020] In one embodiment, the electrical impedance signal includes a ventilation-related signal and a blood perfusion-related signal. The step of extracting a ventilation-related signal from the electrical impedance signal obtained by measuring the electrical impedance of the target area to be measured through a signal extraction algorithm includes the step of extracting a ventilation-related signal from the electrical impedance signal obtained by measuring the electrical impedance of the target area to be measured by using a low-pass filter. Here, 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.

[0021] 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.

[0022] In step S120, the image reconstruction algorithm is a linear difference reconstruction algorithm or an image reconstruction algorithm based on a neural network.

[0023] Referring to FIG. 2, the three-dimensional ventilation image generation method of this embodiment includes the following steps.

[0024] Step S210: According to the electrical impedance signal obtained by measuring the electrical impedance of the target area to be measured through an image reconstruction algorithm, a three-dimensional image is reconstructed. Here, the measurement of the electrical impedance of the target area to be measured is realized by using an electrode array three-dimensionally distributed around the target area to be measured.

[0025] Step S220: According to the three-dimensional image data of multiple instants, enumerate the time series of each pixel in the three-dimensional image. Here, the time series of each pixel is composed of the values of each pixel at different instants.

[0026] Step S230: Extract the time series of ventilation-related pixels from the time series of each pixel in the three-dimensional image.

[0027] Step S240: Construct a three-dimensional ventilation image according to the time series of ventilation-related pixels.

[0028] In step S230, the step of extracting the time series of ventilation-related pixels from the time series of each pixel in the three-dimensional image is realized by any one of the algorithms of frequency domain filtering method, principal component analysis method and neural network method.

[0029] In step S210, the image reconstruction algorithm is a linear difference reconstruction algorithm or an image reconstruction algorithm based on a neural network.

[0030] Example 2

[0031] To solve the above technical problems existing in the prior art, the embodiments of the present disclosure provide a three-dimensional ventilation image generation method applied to the chest of a human body based on Example 1. Here, the three-dimensional ventilation image generation method of this embodiment is realized by two methods specifically shown in FIGS. 3A and 3B.

[0032] As shown in FIG. 3A, the three-dimensional ventilation image generation method of this embodiment includes the following steps. First, measure the electrical impedance of the chest region of the human body to be measured. Then, extract the ventilation-related signal from the measurement signal. Finally, reconstruct the three-dimensional ventilation image. The specific process is as follows.

[0033] Step 1: Measure the electrical impedance of the chest region of the human body to be measured. In the electrical impedance measurement, first, it is necessary to fix an electrode array around the chest of the human body to be measured. The electrode array includes several electrodes distributed in a three-dimensional space. Then, through the electrode array, the chest of the human body to be measured is excited, and the response generated thereby is measured, that is, a current excitation is sequentially applied to the electrodes, and the voltage signals generated thereby are sequentially measured at other electrodes.

[0034] In the second stage, a ventilation-related signal is extracted from the electrical impedance signal measured in the previous stage. In one embodiment of this stage, a filter is used to extract the ventilation-related signal from the measured electrical impedance signal. As the filter, a finite impulse response filter, an infinite impulse response filter, or the like can be used. The following is an example of measuring the chest of a human body. FIG. 4A shows the time-domain signal of the measurement data. The curve in the figure represents the voltage signal measured at a specific electrode when the excitation of the specific electrode is represented. The data obtained in other excitation-measurement situations is the same. It should be noted that the vertical axis in the figure is the value before being converted to the voltage value directly read from the digital voltmeter. FIG. 4B shows the frequency-domain signal of the measurement data. The signal shown in FIG. 4B is the Fourier transform of the signal shown in FIG. 4A. From FIG. 4B, the ventilation-related signal and the blood perfusion-related signal can be distinguished. To extract the ventilation-related signal, a low-pass filter, which can be a finite impulse response low-pass digital filter, is designed, and the cut-off frequency of the 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. The signal graphs after filtering are as shown in FIGS. 5A and 5B, where FIG. 5A is the time-domain signal graph and FIG. 5B is the frequency-domain signal graph.

[0035] In another embodiment of this stage, a method based on PCA (Principal Component Analysis) is used to extract the ventilation-related signal. Specifically, assume the measured 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 principal components {p1, p2,..., p Nc} of the signal are obtained by principal component analysis, where the size of p i (i = 1, 2,..., N c ) is N tIt is ×1, and the values of its corresponding features decrease sequentially. Using the first several principal components (for example, p1, p2) as templates, perform template matching filtering on the signal u to obtain the ventilation-related signal u V obtained.

[0036] In another embodiment of this stage, a neural network-based method is used to extract the ventilation-related signal. Specifically, the neural network-based method can be divided into two stages: training and prediction. In the training phase, use the training data to train the ventilation-related signal extraction network in a supervised or unsupervised manner. In the prediction phase, use the trained ventilation-related signal extraction network to extract the ventilation-related signal in the electrical impedance measurement signal.

[0037] In the third stage, use the ventilation-related signal extracted in the second stage to reconstruct the three-dimensional ventilation image through an image reconstruction algorithm. The three-dimensional ventilation image reflects the change in electrical impedance within the human body region to be measured caused by breathing. In one embodiment of this stage, the image reconstruction algorithm is a linear difference reconstruction algorithm. The following is an example of the linear difference reconstruction algorithm for reconstructing the three-dimensional ventilation image.

[0038] The time-domain form of the ventilation-related signal extracted in the second stage is u(t), where t is a time variable. The EIT difference reconstruction can be expressed as the following least squares problem.

[0039]

Equation

[0040] Here, J is the Jacobian matrix, and δu = u(t) - u(t ref ) is the reference instant t refis the change in the signal at the instant t with respect to, where δσ is the change in the conductivity of the human body caused by ventilation at the above two instants, R is the regularization matrix, and α is the regularization parameter. Reference instant t ref can be set so as not to change throughout the image reconstruction process, and can be set to be dynamically updated as the image reconstruction process progresses. δσ is defined in a discretized three-dimensional model such as a tetrahedral grid or a voxel grid. The solution to the above problem is δσ * =(J T ·J + αR T ·R) -1 ·J T ·δu, where D = (J T ·J + αR T ·R) -1 ·J T Then, the above equation can be expressed as δσ * = D·δu. The above δσ * is the calculated three-dimensional ventilation image.

[0041] Figure 6 shows a schematic diagram of a three-dimensional ventilation image of the chest of the human body generated by the above method.

[0042] In another embodiment at this stage, the image reconstruction algorithm is a method based on machine learning. EIT differential imaging can be expressed as δσ = F(δu), where F(·) is the reconstruction operator, δu is the change in measurement data at different instants, and δσ is the change in conductivity at the corresponding instant. The method based on machine learning is divided into two phases: training and prediction. First, in the training phase, when training data {δu i , δσ i} is given, the machine learning model N can be trained to approximate the operator F(·). In the prediction phase, when the differential measurement signal δu is given, the corresponding conductivity change can be predicted by N.

[0043]

Number

[0044] In addition to the image reconstruction algorithm in the above embodiments, at this stage, various linear or non-linear, iterative or non-iterative, random or deterministic image reconstruction algorithms can be further used.

[0045] As shown in FIG. 3B, the three-dimensional ventilation image generation method of this embodiment includes the following steps. First, measure the electrical impedance of the chest region of the human body to be measured, then reconstruct the three-dimensional difference image, and finally, extract the three-dimensional ventilation image from the three-dimensional difference image. The specific process is as follows.

[0046] Step 1, measure the electrical impedance of the chest region of the human body to be measured.

[0047] Step 2, use the electrical impedance signal measured in the previous step to reconstruct a three-dimensional difference image through an image reconstruction algorithm. The three-dimensional difference image reflects the change in electrical impedance within the chest of the human body to be measured, and this change in electrical impedance may be caused by ventilation or blood perfusion of the human body. The above-mentioned image reconstruction algorithm can be used for the image reconstruction algorithm. FIG. 7 shows the three-dimensional difference image generated using the data shown in FIG. 4 and the linear difference reconstruction algorithm.

[0048] Step 3, extract the ventilation image from the three-dimensional difference image obtained in the previous step. In one embodiment of this step, a filter is used to extract the ventilation image from the three-dimensional difference image. Assume that the three-dimensional difference images at N instants can be arranged as matrix A{α1,α2,...,α M}, T where α i (i = 1,2,...,M) is a column vector composed of the values of pixel i at N instants, and M is the total number of pixels in the three-dimensional image. The time series α iFor (i = 1, 2,..., M), perform low-pass filtering to obtain the time series of the corresponding pixels on the ventilation image. Specifically, assuming that the filtering function is f(·), the ventilation image is A V ={f(a1), f(a2),..., f(a M ),} T .

[0049] FIG. 8A and the corresponding spectrogram FIG. 8B show the time series exemplifying the three-dimensional difference image pixel points of the chest of the human body in FIG. 7. From FIG. 8B, the ventilation-related signal and the blood perfusion-related signal can be distinguished. To extract the ventilation-related signal, a low-pass filter that can be a finite impulse response low-pass digital filter is designed, and the cut-off frequency of the 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.

[0050] FIG. 9A and the spectrogram FIG. 9B show the time-domain signals after filtering of the pixel points in FIG. 7. For each pixel in the three-dimensional difference image, the above low-pass filtering can be processed to obtain a three-dimensional ventilation image, as shown in FIG. 10. In another two embodiments at this stage, methods based on principal component analysis and neural networks can be used to extract the ventilation image.

[0051] It should be noted that A.U. in FIGS. 8A, 8B, 9A, and 9B is an arbitrary unit.

[0052] The three-dimensional ventilation image generation method applied to the chest of the human body in this embodiment reflects the ventilation status of the chest of the human body in each volume in the three-dimensional space by providing a three-dimensional ventilation image in the chest of the human body that can reflect the electrical impedance changes caused by human ventilation.

[0053] Example 3

[0054] To solve the above technical problems existing in the prior art, embodiments of the present disclosure provide a three-dimensional ventilation image generation controller.

[0055] The three-dimensional ventilation image generation controller of this embodiment includes a memory and a processor. A computer program is stored in the memory. When the computer program is executed by the processor, the steps of the methods described in Embodiment 1 and Embodiment 2 are realized.

[0056] Embodiment 4

[0057] To solve the above technical problems existing in the prior art, embodiments of the present disclosure further provide a three-dimensional ventilation image generation device.

[0058] The three-dimensional ventilation image generation device of this embodiment includes an electrode array three-dimensionally distributed around the target area to be measured, which measures the electrical impedance of the target area to be measured and transmits the measured electrical impedance to the three-dimensional ventilation image generation controller, and the three-dimensional ventilation image generation controller described in Embodiment 3.

[0059] The three-dimensional ventilation image generation device of this embodiment further includes an image display device used for the generation and display of the three-dimensional ventilation image generated by the three-dimensional ventilation image generation controller.

[0060] Compared with the prior art, one or more of the above solutions can have the following advantages or beneficial effects. By applying the three-dimensional ventilation image generation method of the present disclosure, through a signal extraction algorithm and an image reconstruction algorithm, a three-dimensional ventilation image is generated according to the electrical impedance signal obtained by measuring the electrical impedance of the target area to be measured. Here, the measurement of the electrical impedance of the target area to be measured is realized by using an electrode array three-dimensionally distributed around the target area to be measured. By providing a three-dimensional ventilation image, the ventilation situation of the human chest in each volume in the three-dimensional space can be reflected.

[0061] The embodiments disclosed in the present disclosure are as described above. However, the above content is for illustration to facilitate the understanding of the present disclosure and does not limit the present disclosure. Those skilled in the technical field to which the present disclosure pertains can arbitrarily modify and change the embodiments and details without departing from the spirit and scope disclosed in the present disclosure. However, the protection scope of the present disclosure must still be defined by the appended claims.

Claims

1. A three-dimensional ventilation image generation method, comprising: reconstructing a three-dimensional difference image according to an electrical impedance signal obtained by measuring the electrical impedance of a target area to be measured through an image reconstruction algorithm, wherein the measurement of the electrical impedance of the target area to be measured is realized by using an electrode array three-dimensionally distributed around the target area to be measured; listing the time series of each pixel in the three-dimensional difference image according to the three-dimensional image data of a plurality of instants, wherein the time series of each pixel is composed of the values of each pixel at different instants; extracting the time series of ventilation-related pixels from the time series of each pixel in the three-dimensional difference image; constructing a three-dimensional ventilation image, which is an image reflecting the ventilation status of the chest of a human body in each volume of a three-dimensional space, according to the time series of the ventilation-related pixels; The three-dimensional ventilation image is constructed as follows: A Ｖ = {f(a １ ), f(a ２ ) ,..., f(a Ｍ ),} Ｔ Here, a ｉ (i = 1, 2,..., M) represents a column vector consisting of the values of pixel i at a plurality of instants, M represents the total number of pixels in the three-dimensional difference image, A Ｖ represents a ventilation image, and f(·) represents a filter function. Method.

2. The electrical impedance signal includes a ventilation-related signal and a blood perfusion-related signal, and the step of extracting the time series of the ventilation-related pixels from the time series of each pixel in the three-dimensional difference image includes: using a low-pass filter to extract the time series of the ventilation-related pixels from the time series of each pixel in the three-dimensional difference image, 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. The method according to claim 1.

3. The step of extracting the time series of the ventilation-related pixels from the time series of each pixel in the three-dimensional difference image is realized by any one of the principal component analysis method and the neural network method. The method according to claim 1.

4. The image reconstruction algorithm is a linear difference reconstruction algorithm or an image reconstruction algorithm based on a neural network. The method according to claim 1.

5. A three-dimensional ventilation image generation controller including a memory and a processor, ​ When a computer program is stored in the memory and the computer program is executed by a processor, the three-dimensional ventilation image generation method according to any one of claims 1 to 4 is realized. The three-dimensional ventilation image generation controller.

6. A three-dimensional ventilation image generation device, An electrode array three-dimensionally distributed around a target area to be measured, which measures the electrical impedance of the target area to be measured and transmits the measured electrical impedance to a three-dimensional ventilation image generation controller; A three-dimensional ventilation image generation controller according to claim 5, characterized by comprising: The three-dimensional ventilation image generation device.

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