Electrical impedance imaging method and imaging device for reconstructing the spatial distribution of lung gas

Abstract

The present disclosure describes an impedance imaging method for reconstructing the spatial distribution of lung gas, including: constructing a forward mapping of the boundary measurement voltage from the spatial distribution of lung gas to the chest area, constructing a data error term and a regularization term based on the actual value of the boundary measurement voltage and the calculated value of the boundary measurement voltage obtained by the forward mapping to obtain a target function, obtaining a relationship formula between the spatial distribution of lung gas in any state and the spatial distribution of lung gas in the original state based on the target function, obtaining a reverse mapping formula from the change of boundary measurement voltage to the change of lung gas spatial distribution based on the relationship formula, obtaining a lung gas spatial distribution image based on the reverse mapping formula and the boundary measurement voltage change, and being able to improve the continuity and stability of the lung gas spatial distribution, solve the reverse problem from the boundary measurement voltage of the chest area to the lung gas spatial distribution and obtain a lung gas spatial distribution image.

Description

Technical Field

[0001] The present disclosure generally relates to the field of medical imaging technology, and specifically relates to an electrical impedance imaging method and an imaging device for reconstructing the spatial distribution of lung gas. Background Art

[0002] Pulmonary function assessment is a common method for checking the function of the human respiratory system. Existing spirometers (including handheld simple spirometers, body volume recorders, and wall oscillography) can accurately measure the overall lung function parameters of the human body, such as forced expiratory volume in 1 second FEV1, forced expiratory vital capacity FVC, peak flow rate PEF, forced expiratory mid-flow FEF25%-75%, vital capacity VC, maximum spontaneous ventilation MVV, etc. However, due to the limitations of technical principles, existing spirometers can only characterize the overall gas in the lungs, and cannot measure the spatial distribution of gas in the human lungs, which is of great significance in accurately characterizing the function of the human respiratory system.

[0003] Electrical Impedance Tomography (EIT) is a new type of medical imaging technology that applies a safe current to the human body and measures the voltage on the body surface as the boundary measurement voltage to calculate the impedance distribution characteristics of the human lung. Due to the advantages of EIT such as being non-invasive, radiation-free, and portable equipment, EIT has been widely used in the field of medical imaging. From the technical principle of EIT, it can be seen that EIT can reflect the impedance distribution inside the human body in the form of images, so EIT has a good application scenario in evaluating human lung ventilation. However, traditional lung EIT technology aims to reconstruct the electrical impedance distribution of the lungs and cannot directly characterize the spatial distribution of gas in the lungs. Therefore, an electrical impedance imaging method that can reconstruct the spatial distribution of gas is needed. Summary of the invention

[0004] The present disclosure is made in view of the above-mentioned prior art conditions, and its purpose is to provide a method and device for electrical impedance imaging that can realize reconstruction of the spatial distribution of lung gas.

[0005] To this end, the first aspect of the present disclosure provides an electrical impedance imaging method for reconstructing the spatial distribution of lung gas, including: constructing a forward mapping of the boundary measurement voltage from the spatial distribution of lung gas to the chest area, constructing a data error term and a regularization term based on the actual value of the boundary measurement voltage and the calculated value of the boundary measurement voltage obtained by the forward mapping to obtain a target function, obtaining a relationship formula between the spatial distribution of lung gas in any state and the spatial distribution of lung gas in the original state based on the target function, obtaining a reverse mapping formula from the change of boundary measurement voltage to the change of lung gas spatial distribution based on the relationship formula, and obtaining a lung gas spatial distribution image based on the reverse mapping formula and the change of boundary measurement voltage.

[0006] In this case, by constructing a forward mapping from the lung gas spatial distribution to the boundary measurement voltage of the chest area, the relationship between the lung gas spatial distribution and the boundary measurement voltage can be clarified, and the objective function can be characterized by the forward mapping. At the same time, an objective function including a data error term and a regularization term can be constructed, and then the objective function can be used to reduce the data error between the boundary measurement voltage calculated from the lung gas spatial distribution and the actual boundary measurement voltage, and improve the continuity and stability of the lung gas spatial distribution, and then the objective function can be used to calculate the relationship formula and use the relationship formula to obtain the reverse mapping formula, thereby solving the reverse problem from the boundary measurement voltage of the chest area to the lung gas spatial distribution, and obtaining the lung gas spatial distribution image.

[0007] In addition, in the electrical impedance imaging method involved in this embodiment, optionally, the forward mapping includes a first forward mapping relationship from the spatial distribution of lung gas to the dielectric property distribution of the chest region and a second forward mapping relationship from the dielectric property distribution of the chest region to the boundary measurement voltage of the chest region, and the first forward mapping relationship is a linear relationship, and the first forward mapping relationship satisfies: Wherein, σ represents the dielectric property distribution of the chest area, α represents the linear coefficient of the lung gas spatial distribution and the dielectric property distribution of the chest area, represents the spatial distribution of lung gas, σ throax represents the dielectric property distribution of the breast tissue, represents the dielectric property distribution of the lung region, and the second forward mapping relationship satisfies: A: Wherein, A represents a nonlinear mapping operator from the dielectric property distribution of the chest region to the boundary measurement voltage of the chest region, Represents the boundary measurement voltage. Since the mapping relationship from the spatial distribution of lung gas to the boundary measurement voltage is relatively complex in the current technology, the current research direction mainly focuses on the relatively clear mapping relationship from the spatial distribution of lung gas to the dielectric property distribution of the chest area. Decomposing the forward mapping can obtain a relatively clear linear first forward mapping relationship and a nonlinear second forward mapping relationship, thereby simplifying the forward mapping, which is beneficial to the expression of subsequent objective functions, relationship formulas, and reverse mapping formulas.

[0008] In addition, in the electrical impedance imaging method involved in this embodiment, optionally, the forward mapping is processed using the chain rule to construct a sensitivity relationship between the boundary measurement voltage and the spatial distribution of lung gas, a sensitivity relationship between the boundary measurement voltage and the dielectric property distribution, and a linear coefficient of the dielectric property distribution and the spatial distribution of lung gas is calculated. The chain rule is expressed as: in, represents the spatial distribution of lung gas, J represents the Jacobian matrix of the nonlinear mapping operator, σ represents the dielectric property distribution of chest tissue, α represents the linear coefficient between the gas spatial distribution and its corresponding dielectric property, and S represents the sensitivity relationship between the boundary measurement voltage and the spatial distribution of lung gas. In this case, the Jacobian matrix of the nonlinear mapping operator and the linear coefficient of the dielectric property distribution of the lung gas spatial distribution can be obtained by using the chain rule, which is beneficial to the expression of subsequent objective functions, relationship formulas, and reverse mapping formulas.

[0009] In addition, in the electrical impedance tomography method involved in this embodiment, optionally, the objective function satisfies the formula: in, represents the objective function, arg min represents the minimum value, represents the spatial distribution of lung gas. represents the data error term, λ represents the regularization term parameter, In this case, the objective function can be used to reduce the data error between the boundary measurement voltage calculated from the lung gas spatial distribution and the actual boundary measurement voltage, and improve the continuity and stability of the lung gas spatial distribution.

[0010] In addition, in the electrical impedance tomography method involved in this embodiment, optionally, the data error term satisfies the formula: where F represents the forward mapping from the spatial distribution of lung gas to the boundary measurement voltage, The distribution of gas space in the lungs is The calculated value of the voltage measured at the boundary under The distribution of gas space in the lungs is The actual value of the voltage measured under the boundary, express In this case, the data error term can represent the difference between the calculated value and the actual value of the boundary measurement voltage.

[0011] In addition, in the electrical impedance tomography method involved in this embodiment, optionally, the regularization term satisfies the formula: Where L represents the first-order difference operator, express In this case, the continuity and stability of the spatial distribution of lung gas can be improved.

[0012] In addition, in the electrical impedance tomography method involved in this embodiment, optionally, a Gauss-Newton iteration method, a sparse method, a total variation method or a D-bar method is used to solve the objective function to obtain a relationship formula between the spatial distribution of lung gas in any state and the spatial distribution of lung gas in the original state, and the relationship formula satisfies: Wherein, t0 represents the time of the original state, represents the spatial distribution of lung gas in the original state, t represents the time of the arbitrary state, represents the spatial distribution of lung gas under any state, S represents the sensitivity relationship between the boundary measurement voltage and the spatial distribution of lung gas, L represents the first-order difference operator, The distribution of gas space in the lungs is The calculated value of the voltage measured at the boundary under The distribution of gas space in the lungs is The actual value of the voltage measured under the boundary, In this case, different solution methods can be used to process the objective function to obtain a relationship formula between the lung gas spatial distribution in any state and the lung gas spatial distribution in the original state.

[0013] In addition, in the electrical impedance tomography method involved in this embodiment, optionally, the reverse mapping formula satisfies: in, represents the change in the spatial distribution of lung gas within Δt time, R represents the regularization matrix, R = L T L, J represents the Jacobian matrix of the nonlinear mapping operator, represents the boundary measurement voltage change within Δt time, B represents the reconstruction matrix, In this case, the reverse mapping formula can be simplified, because the reconstruction matrix can be determined by different EIT methods, that is, after the EIT method is determined, the reconstruction matrix is ​​a constant, which can reduce the complexity of the calculation.

[0014] In addition, in the electrical impedance imaging method involved in this embodiment, optionally, the chest field is discretized to obtain a plurality of chest unit fields, the dielectric property distribution changes of the plurality of chest unit fields are calculated based on the reconstruction matrix, and the linear coefficients of the dielectric property distribution of the lung gas space and the chest region are obtained based on the dielectric property distribution changes of the plurality of chest unit fields and the boundary measurement voltage changes. In this case, since α can be obtained by partial derivative of the chest field, discretization of the chest field can simplify the calculation process, and at the same time, the chest field is decomposed into a plurality of chest unit fields, and the dielectric property distribution changes of the chest field can be obtained based on the dielectric property distribution changes of each chest unit field.

[0015] The second aspect of the present disclosure provides an impedance imaging device for reconstructing the spatial distribution of lung gas, and uses the impedance imaging method involved in the first aspect to obtain a lung gas spatial distribution image. In this case, by constructing a forward mapping of the boundary measurement voltage from the lung gas spatial distribution to the chest area, the relationship between the lung gas spatial distribution and the boundary measurement voltage can be clarified, and the forward mapping can be used to characterize the objective function. At the same time, an objective function including a data error term and a regularization term can be constructed, and then the objective function can be used to reduce the data error between the boundary measurement voltage calculated from the lung gas spatial distribution and the actual boundary measurement voltage, and improve the continuity and stability of the lung gas spatial distribution, and then the objective function can be used to calculate the relationship formula, and the relationship formula can be used to obtain the reverse mapping formula, thereby solving the reverse problem from the boundary measurement voltage of the chest area to the lung gas spatial distribution, and obtaining the lung gas spatial distribution image.

[0016] According to the present disclosure, an electrical impedance imaging method and an imaging device capable of reconstructing the spatial distribution of lung gas and characterizing the spatial distribution of gas in the lung can be provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present disclosure will now be explained in further detail by way of example only with reference to the accompanying drawings, in which:

[0018] Figure 1 It is a schematic diagram showing an application scenario of the electrical impedance imaging method for reconstructing the spatial distribution of lung gas involved in the example of the present disclosure.

[0019] Figure 2 is a flow chart showing the electrical impedance imaging method involved in the example of the present disclosure.

[0020] Figure 3 is a flow chart showing the electrical impedance imaging method for reconstructing the spatial distribution of lung gas involved in the example of the present disclosure.

[0021] Figure 4 is a schematic diagram showing the forward mapping of boundary measurement voltages from the lung gas spatial distribution to the chest region involved in the examples of the present disclosure.

[0022] Figure 5 is a schematic diagram showing the reverse mapping from the boundary measurement voltage of the chest area to the spatial distribution of the lung gas involved in the examples of the present disclosure.

[0023] Figure 6 It is a schematic diagram showing a flow chart of obtaining an objective function involved in the example of the present disclosure.

[0024] Figure 7 It is a schematic diagram showing a flow chart of obtaining a reverse mapping formula involved in the example of the present disclosure.

[0025] Figure 8 is a schematic diagram showing a flow chart of obtaining linear coefficients involved in an example of the present disclosure.

[0026] Fig. 9 It is a schematic diagram showing a flow chart of a method for obtaining an imaging image according to an example of the present disclosure.

[0027] Fig.10 Schematic diagram showing a lung ventilation change curve in an imaging method according to an example of the present disclosure.

[0028] Fig.11 Schematic diagram showing the boundary measurement voltage change and curve in the imaging method involved in the example of the present disclosure.

[0029] Fig.12 is a schematic diagram showing a spatial distribution image of lung gas involved in the examples of the present disclosure.

[0030] Fig.13 Schematic diagram showing the spatial distribution images of lung gas at different acquisition moments involved in the examples of the present disclosure.

[0031] Fig.14 is a schematic diagram showing the structure of an imaging device for obtaining a spatial distribution image of lung gas involved in an example of the present disclosure. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", "third" and "fourth" in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices. In the following description, the same symbols are given to the same components, and repeated descriptions are omitted. In addition, the drawings are only schematic diagrams, and the ratio of the sizes of the components to each other or the shapes of the components may be different from the actual ones.

[0034] The embodiments of the present disclosure propose an electrical impedance imaging method for reconstructing the spatial distribution of lung gas, which can obtain a reverse mapping formula from the boundary measurement voltage change to the lung gas spatial distribution change, which is used for the study and diagnosis of lung pathology in the biomedical field.

[0035] The embodiments of the present disclosure provide an imaging method for obtaining a lung gas spatial distribution image, which can reconstruct the lung gas spatial distribution image according to a reverse mapping formula.

[0036] The embodiments of the present disclosure propose an electrical impedance imaging device for reconstructing the spatial distribution of lung gas, which can obtain a reverse mapping formula from the boundary measurement voltage change to the lung gas spatial distribution change, which is used for the study and diagnosis of lung pathology in the biomedical field.

[0037] The embodiments of the present disclosure provide an imaging device for obtaining a lung gas spatial distribution image, which can reconstruct the lung gas spatial distribution image according to a reverse mapping formula.

[0038] Figure 1 Schematic diagram showing the application scenario of the electrical impedance imaging method for reconstructing the spatial distribution of lung gas involved in the example of the present disclosure. The scenario described in the example of the present disclosure is to more clearly illustrate the technical solution of the present disclosure, and does not constitute a limitation on the technical solution provided by the present disclosure. Figure 2 is a flow chart showing the electrical impedance imaging method involved in the example of the present disclosure.

[0039] For some examples, see Figure 1 The application scenario of the electrical impedance imaging method for reconstructing the spatial distribution of lung gas is to obtain the spatial distribution of lung gas by applying a safe current to the human body and measuring the voltage on the body surface as the boundary measurement voltage. In some examples, the boundary measurement voltage can be obtained by using the acquisition module 41, and the spatial distribution of lung gas can be calculated by using the calculation module 42, and the spatial distribution of lung gas can be displayed by the display module 43.

[0040] For some examples, see Figure 2 The steps of the existing electrical impedance imaging method may include: applying a safe current to the human body (step S101), measuring the voltage on the body surface as a boundary measurement voltage (step S103), and calculating the distribution characteristics of the electrical impedance of the human lung (step S105).

[0041] In some examples, in step S101, the safe current can be the lowest value of the current passing through the human body, which can also be called a safe flow or an allowable continuous current. The safe voltage that the human body can withstand is 36V, and the safe current is 10MA. In this case, the impact of the current on human organs can be reduced, thereby improving the safety of the electrical impedance imaging method.

[0042] In some examples, the voltage on the body surface may be measured as the boundary measurement voltage in step S103 .

[0043] In some examples, after step S101 and step S103 are completed, step S105 may be performed, and step S105 may calculate the distribution characteristics of the electrical impedance of the human lung.

[0044] Figure 3 is a schematic flow chart showing an electrical impedance imaging method for reconstructing the spatial distribution of lung gas involved in an example of the present disclosure; Figure 4 is a schematic diagram showing forward mapping of boundary measurement voltages from the lung gas spatial distribution to the chest region involved in the examples of the present disclosure; Figure 5 is a schematic diagram showing the reverse mapping from the boundary measurement voltage of the chest area to the spatial distribution of the lung gas involved in the examples of the present disclosure.

[0045] For some examples, see Figure 3 The steps of the electrical impedance imaging method for reconstructing the spatial distribution of lung gas may include: constructing a forward mapping (step S201), obtaining a target function (step S203), obtaining a relationship formula (step S205), obtaining a reverse mapping formula (step S207), and obtaining a lung gas spatial distribution image (step S209).

[0046] In some examples, a forward mapping of the boundary measurement voltage from the lung gas spatial distribution to the chest region may be constructed in step S201. In some examples, constructing the forward mapping (or constructing the reverse mapping described later) may be to list only the mapping relationship, for example, to construct the mapping relationship using a mapping function, wherein the mapping function may not have a specific analytical expression.

[0047] In some examples, the spatial distribution of lung gas can be used to study the pathological characteristics of the lungs. Some lung diseases will cause the spatial distribution of lung gas in patients to present specific image characteristics. Even in healthy people, there are regional differences in the spatial distribution of lung gas. In this case, the pathological conditions of the lungs can be studied by monitoring the spatial distribution of lung gas.

[0048] In some examples, the boundary measurement voltage of the chest region can be used to replace the boundary measurement voltage of the lung region. In this case, since the boundary measurement voltage of the lung region cannot be directly obtained and the boundary measurement voltage of the chest region is relatively convenient to obtain, the portability of obtaining the boundary measurement voltage of the lung region can be improved.

[0049] In some examples, the detection point may be set at a position between the 4th and 5th ribs in the middle of the human chest cavity to obtain a boundary measurement voltage of the chest area.

[0050] In some examples, a safe current may be applied to a human body and the voltage on the body surface may be measured to obtain a boundary measurement voltage.

[0051] In some examples, the boundary measurement voltage may be a voltage difference between two adjacent detection points in the chest area, and in some examples, the boundary measurement voltage may also be a voltage difference between each detection point and a reference potential. In some examples, the detection points may be divided at equal distances around the chest of the human body.

[0052] In some examples, forward mapping may be a mapping relationship from the spatial distribution of lung gas to the dielectric property distribution of the chest region, as current technical research generally stays at a mapping relationship between the dielectric property distribution of the chest region and the boundary measurement voltage of the chest region. Since there is a nonlinear mapping relationship between the dielectric property distribution of the chest region and the boundary measurement voltage of the chest region, a forward mapping relationship from the spatial distribution of lung gas to the boundary measurement voltage of the chest region is constructed through the dielectric property distribution of the chest region.

[0053] For some examples, see Figure 4 The forward mapping relationship 1 may be a common research direction in the current related research field, and may include: a first forward mapping relationship 11 and a second forward mapping relationship 12.

[0054] In some examples, the first forward mapping relationship 11 may be used to characterize the relationship between the dielectric property distribution of the chest region and the spatial distribution of the lung gas.

[0055] In some examples, the second forward mapping relationship 12 may be a nonlinear mapping relationship, and the second forward mapping relationship 12 may also be called a nonlinear second forward mapping relationship 12. In some examples, the second forward mapping relationship 12 may be used to characterize the relationship between the boundary measurement voltage of the chest region and the dielectric property distribution of the chest region.

[0056] In some examples, the first forward mapping relationship 11 is a linear relationship. The first forward mapping relationship 11 may also be referred to as a linear first forward mapping relationship 11. The first forward mapping relationship 11 may satisfy:

[0057]

[0058] Among them, σ can represent the dielectric property distribution of the chest area, α can represent the linear coefficient of the spatial distribution of lung gas and the dielectric property distribution, It can represent the spatial distribution of lung gas, σ throax It can represent the dielectric property distribution of chest tissue, It can represent the distribution of dielectric properties of lung gas.

[0059] In some examples, the second forward mapping relationship 12 may be a nonlinear relationship, and the second forward mapping relationship 12 may satisfy:

[0060] A:

[0061] Wherein, A may represent a nonlinear mapping operator from the dielectric property distribution of the chest region to the boundary measurement voltage of the chest region, σ may represent the dielectric property distribution of the chest region, It can represent the boundary measurement voltage. In this case, since the mapping relationship from the spatial distribution of lung gas to the boundary measurement voltage is relatively complex in the current technology, the current research direction mainly focuses on the relatively clear mapping relationship from the spatial distribution of lung gas to the dielectric property distribution of the chest area. Decomposing the forward mapping can obtain a relatively clear linear first forward mapping relationship 11 and a nonlinear second forward mapping relationship 12, thereby simplifying the forward mapping, which is beneficial to the expression of subsequent objective functions, relationship formulas and reverse mapping formulas.

[0062] In some examples, the forward mapping can be processed using the chain rule to construct a sensitivity relationship between the boundary measurement voltage and the spatial distribution of lung gas, a sensitivity relationship between the boundary measurement voltage and the dielectric property distribution, and calculate linear coefficients of the dielectric property distribution and the spatial distribution of lung gas, and the chain rule is expressed as:

[0063]

[0064] in, can represent the spatial distribution of lung gas, J can represent the Jacobian matrix of the nonlinear mapping operator, σ can represent the dielectric property distribution of chest tissue, α can represent the linear coefficient of the spatial distribution of lung gas and the dielectric property distribution of the chest area, and S can represent the sensitivity relationship between the boundary measurement voltage and the spatial distribution of lung gas. In this case, since the chain rule can be used to solve the derivative of a composite function, the derivative of the composite function is the product of the derivatives of the finite number of functions that constitute the composite at the corresponding points. The chain rule is used to process the forward mapping relationship from the spatial distribution of lung gas to the dielectric property distribution of the chest area, and then to the boundary measurement voltage of the chest area, and the sensitivity relationship between the spatial distribution of lung gas and the boundary measurement voltage of the chest area can be obtained. Among them, the Jacobian matrix is ​​a matrix in which the first-order partial derivatives are arranged in a certain way, which can reflect the optimal linear approximation of a differentiable equation to a given point.

[0065] In some examples, the Jacobian matrix can be obtained through a nonlinear second forward mapping relationship 12.

[0066] In some examples, the dielectric property distribution of chest tissue can reflect the change in the spatial distribution of lung gas. In this case, a linear first forward mapping relationship 11 between the spatial distribution of lung gas and the dielectric property distribution of chest tissue can be constructed.

[0067] In some examples, the dielectric property of breast tissue may refer to the electrical conductivity or electrical impedance of the breast tissue.

[0068] In some examples, the chain rule can be used to obtain the Jacobian matrix of the nonlinear mapping operator and the linear coefficients of the dielectric property distribution of the lung gas spatial distribution. In this case, it can be beneficial to the expression of subsequent objective functions, relationship formulas, and reverse mapping formulas.

[0069] For some examples, see Figure 5 The reverse mapping relationship 2 may include: a first reverse mapping relationship 21 and a second reverse mapping relationship 22, wherein the first reverse mapping relationship 21 can be used to express the relationship from the boundary measurement voltage of the chest area to the dielectric property distribution of the chest area, and the second reverse mapping relationship 22 can be used to express the relationship from the dielectric property distribution of the chest area to the spatial distribution of lung gas.

[0070] In some examples, the first reverse mapping relationship 21 may satisfy:

[0071] B:

[0072] Wherein, B can represent a nonlinear mapping operator from the boundary measurement voltage of the chest region to the dielectric property distribution of the chest region, σ may represent the boundary measurement voltage, and σ may represent the dielectric property distribution of the chest region. In this case, the dielectric property distribution of the chest region can be obtained by measuring the boundary measurement voltage of the chest region.

[0073] In some examples, the second reverse mapping relationship 22 may satisfy:

[0074] C:

[0075] Where C can represent the mapping operator from the dielectric property distribution of the chest region to the spatial distribution of the lung gas, σ can represent the dielectric property distribution of the chest region, The spatial distribution of the lung gas can be represented. In this case, the spatial distribution of the lung gas can be obtained by studying the distribution of dielectric properties of the chest area.

[0076] In some examples, the boundary measurement voltage of the chest region in the first reverse mapping relationship 21 and the lung gas spatial distribution in the second reverse mapping relationship 22 can be linked through the dielectric property distribution of the chest region. In this case, the lung gas spatial distribution can be obtained by calculating the boundary measurement voltage of the chest region, reducing the complexity of obtaining the lung gas spatial distribution image.

[0077] Figure 6 It is a schematic diagram showing a flow chart of obtaining an objective function involved in the example of the present disclosure. Figure 7 It is a schematic diagram showing a flow chart of obtaining a reverse mapping formula involved in the example of the present disclosure.

[0078] In some examples, after step S201 is completed, step S203 may be performed, in which an objective function may be obtained according to a data error term and a regularization term constructed based on an actual value of the boundary measurement voltage and a calculated value of the boundary measurement voltage obtained by forward mapping.

[0079] In some examples, the actual value of the boundary measurement voltage may be a value of the boundary measurement voltage under a non-ideal state, that is, under a realistic state affected by many realistic influencing factors.

[0080] In some examples, the calculated value of the boundary measurement voltage can be calculated and obtained through a forward mapping relationship from the spatial distribution of lung gas to the boundary measurement voltage.

[0081] For some examples, see Figure 6 The steps of obtaining the objective function may include: setting an error term (step S301), setting a regularization term (step S303), setting a regularization term parameter (step S305), and processing the error term using the result of the forward mapping process using the chain rule (step S307).

[0082] In some examples, the error term may be set in step S301, and the data error term may satisfy the formula: where F can represent the forward mapping from the spatial distribution of lung gas to the boundary measurement voltage, The distribution of gas space in the lungs can be expressed as The calculated value of the voltage measured at the boundary under The distribution of gas space in the lungs can be expressed as The actual value of the voltage measured under the boundary, Can be expressed In this case, the data error term can represent the difference between the calculated value and the actual value of the boundary measurement voltage.

[0083] In some examples, a regularization term may be set in step S303, and the regularization term may satisfy the formula: Among them, L can represent the first-order difference operator, Can be expressed In this case, the continuity and stability of the spatial distribution of lung gas can be improved.

[0084] In some examples, the first-order difference operator can be the difference between two consecutive terms in a discrete function. In this case, it can be used to study the problem of the change in the difference between two consecutive terms of the function.

[0085] In some examples, the regularization term can be used to prevent the loss function from overfitting. In this case, the data error between the boundary measurement voltage calculated from the lung gas spatial distribution and the actual boundary measurement voltage can be reduced and the continuity and stability of the lung gas spatial distribution can be improved.

[0086] In some examples, the regularization term parameter can be set in step S305, and the regularization term parameter can reflect the credibility of the objective function. When the regularization term parameter is relatively small, the regularization term plays a small role in the objective function, and the objective function has a large uncertainty and is unreliable to a certain extent. When the regularization parameter is relatively large, the regularization term dominates the objective function, and the credibility of the objective function is very high and close to the actual situation. In this case, selecting a larger regularization parameter can improve the credibility of the objective function.

[0087] In some examples, the objective function may satisfy the formula: in, can represent the objective function, arg min can represent the minimum value, It can represent the spatial distribution of lung gas. can represent the data error term, λ can represent the regularization term parameter, In this case, the objective function can be used to reduce the data error between the boundary measurement voltage calculated from the lung gas spatial distribution and the actual boundary measurement voltage, and improve the continuity and stability of the lung gas spatial distribution.

[0088] In some examples, after step S201 and step S203 are completed, step S205 may be performed.

[0089] In some examples, in step S205, a relationship formula between the spatial distribution of lung gas in any state and the spatial distribution of lung gas in the original state may be obtained based on the objective function.

[0090] In some examples, the spatial distribution of lung gas in the original state may be the spatial distribution of lung gas at an initially selected state point, which may be considered as any selected initial moment.

[0091] In some examples, the parameter variable of the spatial distribution of lung gas in any state may be time. The relationship formula of the spatial distribution of lung gas at different times may be obtained through the objective function and the spatial distribution of lung gas in the selected initial state. The relationship formula may be obtained through iteration (described later).

[0092] In some examples, the objective function may be solved using a Gauss-Newton iteration method, a sparse method, a total variation method, or a D-bar method to obtain a relationship formula between the lung gas spatial distribution in any state and the lung gas spatial distribution in the original state, and the relationship formula may satisfy:

[0093]

[0094] Among them, t0 can represent the moment of the original state, It can represent the spatial distribution of lung gas in the original state, and t can represent the moment of any state. can represent the spatial distribution of lung gas under any state, S can represent the sensitivity relationship between the boundary measurement voltage and the spatial distribution of lung gas, L can represent the first-order difference operator, The distribution of gas space in the lungs can be expressed as The calculated value of the voltage measured at the boundary under The distribution of gas space in the lungs can be expressed as The actual value of the voltage measured under the boundary, In this case, the objective function can be processed using different solution methods to obtain a relationship formula between the lung gas spatial distribution in any state and the lung gas spatial distribution in the original state.

[0095] In some examples, the objective function can be processed by the Gauss-Newton iteration method, which is an iterative method for least squares method of regression parameters in nonlinear regression model, using Taylor series expansion to approximately replace the nonlinear regression model, and then through multiple iterations, multiple corrections of regression coefficients, the regression coefficients are made to continuously approach the optimal regression coefficients of the nonlinear regression model, and finally the residual sum of squares of the original model is minimized. In this case, the complexity of processing the objective function can be simplified, which is conducive to obtaining the relationship formula between the spatial distribution of lung gas in any state and the spatial distribution of lung gas in the original state.

[0096] In some examples, after step S205 is completed, step S207 can be performed to construct a reverse mapping relationship from the boundary measurement voltage change to the lung gas spatial distribution change based on the relationship formula obtained in step S205, thereby obtaining a reverse mapping formula from the boundary measurement voltage change to the lung gas spatial distribution change (described later).

[0097] For some examples, see Figure 7 In step S207, a reverse mapping formula may be obtained. The step of obtaining the reverse mapping formula may include: obtaining a target relationship formula based on a relationship formula (S401), obtaining a reverse mapping formula based on the relationship formula and the target relationship formula (S403), and processing the reverse mapping formula based on the result of the forward mapping relationship processing using the chain rule (S405).

[0098] In some examples, step S401 may arbitrarily select an original moment and another moment to obtain a first relationship formula between the spatial distribution of lung gas at the selected another moment and the spatial distribution of lung gas at the selected original moment.

[0099] In some examples, step S403 may be performed after step S401, and a third moment different from the previous two moments may be selected to obtain a second relationship formula between the lung gas spatial distribution at the third moment and the lung gas spatial distribution at the original moment:

[0100]

[0101] In some examples, step S405 may obtain a reverse mapping formula by subtracting the second relationship formula from the first relationship formula.

[0102] In some examples, step S407 can simplify the reverse mapping formula obtained in step S405 by processing the forward mapping relationship using the chain rule. In this case, the complexity of calculating the reverse mapping formula can be reduced, which facilitates the subsequent calculation of the linear coefficient and the processing of the lung gas spatial distribution image.

[0103] In some examples, the reverse mapping formula may satisfy:

[0104]

[0105] in, It can represent the change in the spatial distribution of lung gas within Δt time, and R can represent the regularization matrix, R = L T L, J can represent nonlinear mapping operators, can represent the boundary measurement voltage change within Δt time, and B can represent the reconstruction matrix, In this case, the reverse mapping formula can be simplified, thereby reducing the complexity of calculating the spatial distribution of lung gas.

[0106] In some examples, the reconstruction matrix can be determined by different EIT methods, that is, after the EIT method is determined, the reconstruction matrix is ​​a constant value. In this case, the complexity of calculating the spatial distribution of lung gas can be reduced.

[0107] In some examples, after step S207 is completed, step S209 can be performed, and a lung gas space distribution image can be obtained through the obtained reverse mapping formula and the boundary measurement voltage change, and the lung gas space distribution change can be clearly seen through the lung gas space distribution image. In this case, since the mapping relationship from the lung gas space distribution to the boundary measurement voltage is relatively complex in the current technology, the current research direction mainly focuses on the relatively clear mapping relationship from the lung gas space distribution to the dielectric property distribution of the chest area. Decomposing the forward mapping can obtain a relatively clear linear first forward mapping relationship 11 and a nonlinear second forward mapping relationship 12, thereby simplifying the forward mapping, which is beneficial to the expression of subsequent objective functions, relationship formulas, and reverse mapping formulas.

[0108] Figure 8 is a schematic diagram showing a flow chart of obtaining linear coefficients involved in an example of the present disclosure.

[0109] For some examples, see Figure 8 , the step of obtaining the linear coefficient may include: discretizing the chest field (step S501), obtaining a determined reconstruction matrix (step S503), measuring the boundary measurement voltage change of the chest unit field and the change of the lung gas spatial distribution (step S505), calculating the sum of the dielectric property distribution changes of the chest unit field (step S507), and obtaining a linear coefficient that can express the relationship between the lung gas spatial distribution and the dielectric property of the chest area (step S509).

[0110] In some examples, step S501 may discretize the chest field to obtain a plurality of chest field calculation units. Specifically, the chest may be divided to obtain a plurality of chest units. In this case, the chest field may be divided into a plurality of chest unit fields.

[0111] In some examples, step S501 and step S503 may be performed simultaneously, and step S503 may determine the reconstruction matrix by using an already determined EIT imaging method.

[0112] In some examples, step S505 may occur after step S501, and the boundary measurement voltage change of the chest unit field and the change of the lung gas spatial distribution may be measured to calculate a linear coefficient that can represent the relationship between the lung gas spatial distribution and the dielectric properties of the chest area.

[0113] In some examples, step S507 may calculate the sum of the dielectric property distribution changes in the chest unit field, and the sum of the dielectric property distribution changes in the chest unit field satisfies the formula: Wherein, Δσ can represent the change in the distribution of dielectric properties of the chest field, i can represent the i-th chest unit field, N can represent the number of chest unit fields divided by the chest field, and Δσ i can represent the change in the dielectric property distribution of the i-th chest unit field, and B can represent the reconstruction matrix. Can represent the change of boundary measurement voltage.

[0114] In some examples, step S509 may occur after S507, and the formula for calculating the linear coefficient of the relationship between the spatial distribution of lung gas and the dielectric properties of the chest region may satisfy: Among them, α can represent the linear coefficient of the spatial distribution of lung gas and the dielectric property distribution of the chest area, Δσ can represent the change of the dielectric property distribution of the chest field, i can represent the i-th chest unit field, N can represent the number of chest unit fields divided by the chest field, and Δσ i can represent the change in the dielectric property distribution of the i-th chest unit field, and B can represent the reconstruction matrix. The change of the boundary measurement voltage can be represented. In this case, the complexity of calculating the linear coefficient of the relationship between the spatial distribution of lung gas and the dielectric properties of the chest area can be simplified.

[0115] In some examples, the linear coefficient of the spatial distribution of lung gas and the distribution of dielectric properties can be calculated under the condition of a large change in the amount of air in the lung. Specifically, the linear coefficient of the spatial distribution of lung gas and the distribution of dielectric properties can be calculated when the change in the amount of air in the lung is greater than a preset value. The linear coefficient can satisfy the formula: Among them, α can represent the linear coefficient of the spatial distribution of lung gas and the dielectric property distribution of the chest area, can represent the spatial distribution of lung gas, N can represent the number of chest unit fields divided by the chest field, Δσ i large It can represent the maximum value of the node change of the i-th chest unit field, and B can represent the reconstruction matrix. The maximum value of the voltage change that can be measured for the boundary, The preset value of the change in the spatial distribution of the lung gas can be indicated. In this case, the influence of the environment such as noise on the spatial distribution of the lung gas can be reduced, and the accuracy of the spatial distribution of the lung gas can be improved.

[0116] In some examples, the preset value may be no less than 100 ml.

[0117] In some examples, the reverse mapping formula can be expressed as:

[0118]

[0119] in, It can represent the change of lung gas spatial distribution within Δt time, N can represent the number of chest unit fields divided by the chest field, Δσ i large It can represent the maximum value of the node change of the i-th chest unit field, and B can represent the reconstruction matrix. It can represent the maximum value of the boundary measurement voltage change, The maximum value of the change in the spatial distribution of the lung gas can be represented. In this case, the complexity of obtaining the change in the spatial distribution of the lung gas can be reduced, and the accuracy of the change in the spatial distribution of the lung gas can be improved.

[0120] As described above, the embodiments of the present disclosure also provide an imaging method for obtaining a lung gas spatial distribution image, which can obtain the source of the parameters involved in the electrical impedance imaging method.

[0121] Fig. 9 It is a schematic diagram showing a flow chart of a method for obtaining an imaging image according to an example of the present disclosure. Fig.10 Schematic diagram showing a lung ventilation change curve in an imaging method according to an example of the present disclosure. Fig.11 Schematic diagram showing the boundary measurement voltage change and curve in the imaging method involved in the example of the present disclosure. Fig.12 Schematic diagram showing a spatial distribution image of lung gas involved in the examples of the present disclosure. Fig.13 Schematic diagram showing the spatial distribution images of lung gas at different acquisition moments involved in the examples of the present disclosure.

[0122] For some examples, see Fig. 9 The imaging method may include: obtaining the Jacobian matrix and reconstruction matrix of the nonlinear mapping operator (step S601), obtaining the change value of the boundary measurement voltage in any time period and the change of the lung gas spatial distribution (step S603), obtaining the linear coefficient of the lung gas spatial distribution and the dielectric property distribution (step S605), calculating the lung gas spatial distribution change based on the reverse mapping formula and forming a lung gas spatial distribution image (step S607).

[0123] The imaging method is described below by taking the target object as a 28-year-old male without respiratory diseases as an example, but the present disclosure is not limited thereto, and the imaging method involved in the present disclosure can also be used for other target objects.

[0124] In some examples, the Jacobian matrix of the nonlinear mapping operator in step S601 may be obtained by a nonlinear mapping operator from the dielectric property distribution of the chest region to the boundary measurement voltage of the chest region.

[0125] In some examples, the reconstruction matrix may be obtained by the Jacobian matrix of the nonlinear mapping operator in step S601. For example, the reconstruction matrix may be calculated by the GREIT algorithm.

[0126] In some examples, the change value of the boundary measurement voltage in any time period in step S603 can be obtained by the boundary measurement voltage at the first and last moments, and the change in the spatial distribution of lung gas can be obtained by the spatial distribution of lung gas at the first and last moments.

[0127] In some examples, in step S603, the acquisition module 41 may be placed between the 4th and 5th intercostal spaces of the target object to collect EIT data. In some examples, the acquisition module 41 may include 16 electrodes disposed around the target object between the 4th and 5th intercostal spaces.

[0128] In some examples, the acquisition frame rate of the acquisition module 41 is determined by the data acquisition system, such as 20 frames / second, 40 frames / second, 50 frames / second, and 100 frames / second. In this embodiment, the acquisition frame rate is 20 frames / second.

[0129] In some examples, the target subject may be taught in advance to collect lung ventilation data while in a standing position, with a nose clip, and breathing through the mouth.

[0130] In some examples, during the acquisition of lung ventilation data, see Fig.10 and Fig.11 , the target subject can try to perform the SVC pulmonary function test, including: after two quiet breaths, slowly and deeply inhale to the total lung volume, and then slowly and deeply inhale to the residual air position.

[0131] In some examples, chest boundary measurement voltage data and lung ventilation data may be recorded while the target subject attempts to perform an SVC pulmonary function test maneuver.

[0132] In some examples, the boundary measurement voltage change and the curve may be used to obtain the boundary measurement voltage change. When obtaining the boundary measurement voltage change, the number of measurement channels may be M, where M may be 208.

[0133] In some examples, the lung ventilation data may represent changes in the amount of air in the lungs, and in some examples, the lung ventilation data may be acquired by a ventilator.

[0134] In some examples, the pulmonary ventilation change curve can be used to obtain the changes in the spatial distribution of pulmonary gas as described above. Fig.10 The difference between two points in the pulmonary ventilation change curve can represent the change in the spatial distribution of lung gas.

[0135] In some examples, step S605 may be performed after step S603, and the linear coefficients of the spatial distribution of lung gas and the dielectric property distribution may be obtained through the reconstruction matrix, the change value of the boundary measurement voltage in any time period, and the change of the spatial distribution of lung gas.

[0136] In some examples, in step S605, the linear coefficient formula may be used: Calculate the linear coefficients. For example, you can choose t0 and t ins-end Calculate Δl from lung ventilation data at two moments large , where t0 can represent the end of exhalation of the second quiet breath, t ins-end It can indicate the moment of taking a deep breath to the total lung capacity.

[0137] In some examples, t0 and t ins-end The changes in the spatial distribution of lung gas and the ins-end The voltage change is measured at the chest area boundary between and Fig.10 , we can use the areas with large differences in the spatial distribution of lung gas (such as t0 and t ins-end Determination of the spatial distribution of lung gas Δl large The sum of the dielectric property distribution changes in the chest area and the dielectric property distribution changes in the chest area (i.e., the sum of the dielectric property distribution changes in each chest unit field in the chest area)

[0138] In this example, Therefore, the linear coefficient of the target object can be calculated.

[0139] In some examples, step S607 may be performed after step S605. In step S607, the change in the spatial distribution of lung gas may be calculated based on the reverse mapping formula and a lung gas spatial distribution image may be formed.

[0140] In some examples, the reconstruction matrix obtained in step S601, the boundary measurement voltage obtained in step S603, and the linear coefficient obtained in step S605 may be substituted into the reverse mapping formula to obtain the change in the spatial distribution of the lung gas.

[0141] For some examples, see Fig.12 , the lung gas space distribution image can be used to reflect the gas space distribution of the chest field. In this case, the gas space distribution of each chest unit field in the chest field can be determined, for example, Fig.12 It is a 32*32 pixel lung gas space distribution image, in which the gas space distribution change (ie ventilation volume) of the chest unit field at the pixel position (24,12) in the image is 11.18 ml.

[0142] For some examples, see Fig.13 Each image reflects the spatial distribution of lung gas in each frame. The spatial distribution images of lung gas at different acquisition times can be used to reflect the changes in the spatial distribution of lung gas of the target object at different acquisition times. In this case, the changes in the lung gas during breathing can be reproduced.

[0143] As described above, the embodiments of the present disclosure provide an electrical impedance imaging device for reconstructing the spatial distribution of lung gas, which can improve the continuity and stability of the spatial distribution of lung gas, solve the reverse problem of measuring voltage from the boundary of the chest area to the spatial distribution of lung gas, and obtain a spatial distribution image of lung gas.

[0144] In some examples, an impedance imaging device for reconstructing the spatial distribution of lung gas can obtain a lung gas spatial distribution image by the impedance imaging method involved in the present disclosure. In this case, by constructing a forward mapping of the boundary measurement voltage from the lung gas spatial distribution to the chest area, the relationship between the lung gas spatial distribution and the boundary measurement voltage can be clarified, and the forward mapping can be used to characterize the objective function. At the same time, an objective function including a data error term and a regularization term can be constructed, and then the objective function can be used to reduce the data error between the boundary measurement voltage calculated from the lung gas spatial distribution and the actual boundary measurement voltage and improve the continuity and stability of the lung gas spatial distribution, and then the objective function can be used to calculate the relationship formula and use the relationship formula to obtain the reverse mapping formula, thereby solving the reverse problem from the boundary measurement voltage of the chest area to the lung gas spatial distribution, and obtaining a lung gas spatial distribution image.

[0145] Fig.10 is a schematic diagram showing the structure of an imaging device for obtaining a spatial distribution image of lung gas involved in an example of the present disclosure.

[0146] As described above, the embodiments of the present disclosure provide an imaging device for obtaining a spatial distribution image of lung gas.

[0147] In some examples, the imaging device may acquire a lung gas spatial distribution image using the imaging method for acquiring a lung gas spatial distribution image involved in the present disclosure.

[0148] For some examples, see Figure 1 and Fig.10 The imaging device 4 may include: an acquisition module 41 , a calculation module 42 , and a display module 43 .

[0149] In some examples, the acquisition module 41 is used to acquire the Jacobian matrix of the nonlinear mapping operator, the change value of the boundary measurement voltage in any time period, the change of the spatial distribution of the lung gas, and the reconstruction matrix.

[0150] In some examples, the calculation module 42 is used to calculate the linear coefficient of the lung gas spatial distribution and the dielectric property distribution through the relevant variable values ​​obtained by the acquisition module, and obtain the lung gas spatial distribution change based on the reverse mapping formula described above.

[0151] In some examples, display module 43 may obtain a lung gas spatial distribution image based on changes in the lung gas spatial distribution.

[0152] Various embodiments of the present invention are described above in the detailed description. Although these descriptions directly describe the above embodiments, it should be understood that modifications and / or variations of the specific embodiments shown and described herein may be conceived by those skilled in the art. Any such modifications or variations that fall within the scope of this specification are also intended to be included therein. Unless otherwise specified, it is the intention of the inventor that the words and phrases in the specification and claims are given the ordinary and customary meanings of ordinary technicians.

[0153] The above description of various embodiments of the present invention known to the applicant at the time of filing this application has been presented and is intended for the purpose of illustration and description. This description is not intended to be exhaustive of the present invention, nor to limit the present invention to the exact form disclosed, and many modifications and variations may be made in accordance with the above teachings. The described embodiments serve to explain the principles of the present invention and its practical application, and to enable others skilled in the art to utilize the present invention in various embodiments and various modifications suitable for the intended specific use. Therefore, it is intended that the present invention is not limited to the specific embodiments disclosed for implementing the present invention.

[0154] Although specific embodiments of the present invention have been shown and described, it is obvious to those skilled in the art that, based on the teachings of the present invention, variations and modifications may be made without departing from the present invention and its broader aspects, and the appended claims will cover within their scope all such changes and modifications that are within the true spirit and scope of the present invention. It will be understood by those skilled in the art that, in general, the terms used in the present invention are generally intended to be "open" terms (e.g., the term "including" should be interpreted as "including but not limited to", the term "having" should be interpreted as "at least having", etc.).

Claims

1. An electrical impedance imaging method for reconstructing the spatial distribution of lung gas, characterized in that: include: Construct a forward map from the spatial distribution of lung gas to the boundary measured voltages in the chest region, A data error term and a regularization term constructed according to the actual value of the boundary measurement voltage and the calculated value of the boundary measurement voltage obtained by the forward mapping are used to obtain an objective function, Based on the objective function, a relationship formula between the spatial distribution of lung gas in any state and the spatial distribution of lung gas in the original state is obtained. Based on the relationship formula, a reverse mapping formula from the boundary measurement voltage change to the lung gas spatial distribution change is obtained. A lung gas spatial distribution image is obtained based on the reverse mapping formula and the boundary measurement voltage change, wherein the forward mapping includes a first forward mapping relationship from the lung gas spatial distribution to the dielectric property distribution of the chest region and a second forward mapping relationship from the dielectric property distribution of the chest region to the boundary measurement voltage of the chest region, the first forward mapping relationship is a linear relationship, and the first forward mapping relationship satisfies: σ=α·l+σ throax , where σ represents the dielectric property distribution of the chest area, α represents the linear coefficient of the lung gas space distribution and the dielectric property distribution of the chest area, l represents the lung gas space distribution, σ throax represents the dielectric property distribution of chest tissue, α·l represents the dielectric property distribution of the lung region, and the second forward mapping relationship satisfies: A: Wherein, A represents a nonlinear mapping operator from the dielectric property distribution of the chest region to the boundary measurement voltage of the chest region, represents the boundary measurement voltage.

2. The electrical impedance tomography method according to claim 1, characterized in that: The forward mapping is processed using the chain rule to construct a sensitivity relationship between the boundary measurement voltage and the spatial distribution of the lung gas, a sensitivity relationship between the boundary measurement voltage and the dielectric property distribution of the chest region, and to calculate a linear coefficient of the dielectric property distribution of the chest region and the spatial distribution of the lung gas, wherein the chain rule is expressed as: Wherein, l represents the spatial distribution of lung gas, J represents the Jacobian matrix of the nonlinear mapping operator, σ represents the dielectric property distribution of the chest area, α represents the linear coefficient between the gas spatial distribution and its corresponding dielectric property, and S represents the sensitivity relationship between the boundary measurement voltage and the lung gas spatial distribution.

3. The electrical impedance tomography method according to claim 2, characterized in that: The objective function satisfies the formula: Among them, l * represents the objective function, arg min represents the minimum value, l represents the spatial distribution of lung gas, θ(l) represents the data error term, λ represents the regularization term parameter, represents the regularization term.

4. The electrical impedance tomography method according to claim 3, characterized in that: The data error term satisfies the formula: Wherein, F represents the forward mapping from the lung gas spatial distribution to the boundary measurement voltage, F(l) represents the calculated value of the boundary measurement voltage when the lung gas spatial distribution is l, represents the actual value of the boundary measurement voltage when the lung gas space distribution is l, express The 2-norm of .

5. The electrical impedance tomography method according to claim 3, characterized in that: The regularization term satisfies the formula: Wherein, L represents the first-order difference operator, and ||Ll||2 represents the 2-norm of Ll.

6. The electrical impedance tomography method according to claim 3, characterized in that: The objective function is solved by using Gauss-Newton iteration method, sparse method, total variation method or D-bar method to obtain a relationship formula between the spatial distribution of lung gas in any state and the spatial distribution of lung gas in the original state. The relationship formula satisfies: Wherein, t0 represents the time of the original state, represents the spatial distribution of lung gas in the original state, t represents the time of the arbitrary state, l t represents the spatial distribution of lung gas under any state, S represents the sensitivity relationship between the boundary measurement voltage and the spatial distribution of lung gas, L represents the first-order difference operator, represents the calculated value of the boundary measurement voltage when the lung gas space distribution is l, represents the actual value of the boundary measurement voltage when the lung gas space distribution is l, Indicates the boundary measurement voltage.

7. The electrical impedance tomography method according to claim 6, characterized in that: The reverse mapping formula satisfies: Where Δl represents the change in the spatial distribution of lung gas within Δt, R represents the regularization matrix, R = L T L, J represent the Jacobian matrix of the nonlinear mapping operator, represents the boundary measurement voltage change within Δt time, B represents the reconstruction matrix, 8. The electrical impedance tomography method according to claim 7, characterized in that: The chest field is discretized to obtain a plurality of chest unit fields, and the dielectric property distribution changes of the plurality of chest unit fields are calculated based on the reconstruction matrix. The linear coefficients of the spatial distribution of lung gas and the dielectric property distribution of the chest area are obtained based on the dielectric property distribution changes of the plurality of chest unit fields and the boundary measurement voltage changes.

9. An electrical impedance imaging device for reconstructing the spatial distribution of lung gas, characterized in that: The electrical impedance imaging method according to any one of claims 1 to 8 is used to acquire a lung gas spatial distribution image.

Images