CN106821380B - Biomedical electrical impedance imaging method and device based on multiplicative regularization - Google Patents

Abstract

The invention discloses a biomedical electrical impedance imaging method and device based on multiplicative regularization, wherein the method comprises the following steps: arranging a plurality of electrodes around an object to be imaged, and in the measuring process, exciting two electrodes in the plurality of electrodes by constant current in turn and measuring the potential difference on other electrodes in the plurality of electrodes to obtain a frame of measuring data; calculating a data error term of the electrical impedance imaging problem according to the measurement data and the forward simulation data, and calculating a target functional according to a preset multiplicative regular term and the data error term; and minimizing the target functional through an iteration method to reconstruct the image. The method can well reserve the edge form of the organism organ, so that the reconstructed image has the characteristic of a blocking constant and has good anti-noise performance.

Description

Biomedical electrical impedance imaging method and device based on multiplicative regularization

technical field

The invention relates to the technical field of biomedical electrical impedance imaging, in particular to a method and device for biomedical electrical impedance imaging based on multiplicative regularization.

Background technique

With the advancement of science and technology, medical imaging technology has made great progress. These technologies extract information on the shape, structure and certain physiological functions of tissues or organs in the organism by means of the interaction of certain energy with the organism, and provide imaging information for biological tissue research and clinical diagnosis, such as X-ray imaging, ultrasound Imaging, magnetic resonance imaging, infrared imaging, radionuclide imaging, optical imaging, electrical impedance tomography and other medical imaging technologies. Among them, Electrical Impedance Tomography (Electrical Impedance Tomography, English abbreviation: EIT) is a technology that uses the electrical impedance characteristics of the detected object to perform imaging. This technology has the advantages of high temporal resolution, low cost, light equipment, non-invasiveness, and no radiation, so it is an attractive and promising technology.

However, when using electrical impedance imaging technology to reconstruct morphological images of in vivo tissues or organs in practical applications, the acquired measurement data is usually less than the number of unknowns that need to be inverted, so the image reconstruction problem is often serious. sex. In order to improve this morbidity, in related art, the problem is usually regularized. Among them, there are various regularization methods, such as Tikhonov regularization, Total Variation regularization, and the like.

However, although traditional regularization methods can improve the ill-posedness of reconstruction problems, there are still some shortcomings. For example, Tikhonov regularization makes the boundaries of living organs too smooth; the original total variation regularization term is not differentiable, making it difficult to use Newton-like methods for image reconstruction.

In addition, the traditional additive regularization method needs to set a parameter in the objective functional to adjust the relative weight of the data error term and the regularization term. However, this parameter value needs to be determined by rather tedious numerical experiments, which greatly increases the computational complexity of the imaging algorithm.

SUMMARY OF THE INVENTION

The purpose of the present invention is to solve one of the above-mentioned technical problems at least to a certain extent.

Therefore, the first objective of the present invention is to propose a biomedical electrical impedance imaging method based on multiplicative regularization. The method can well preserve the edge morphology of living organs, make the reconstructed image have the characteristic of "blocking constant", and have good anti-noise performance.

The second object of the present invention is to propose a biomedical electrical impedance imaging device based on multiplicative regularization.

In order to achieve the above object, the biomedical electrical impedance imaging method based on multiplicative regularization according to the embodiment of the first aspect of the present invention includes the following steps: arranging a plurality of electrodes around the object to be imaged, and during the measurement process, alternating constant current excitation Two electrodes in the plurality of electrodes and the potential difference on other electrodes in the plurality of electrodes are measured to obtain a frame of measurement data; the data error of the electrical impedance imaging problem is calculated according to the measurement data and the forward simulation data term, and calculate the target functional according to the preset multiplicative regular term and the data error term; minimize the target functional by an iterative method to perform image reconstruction.

The multiplicative regularization-based biomedical electrical impedance imaging method according to the embodiment of the present invention uses a constant current source to excite and measure two electrodes in a plurality of electrodes in turn to obtain measurement data, and according to the measurement data and forward simulation The data calculates the data error term of the electrical impedance imaging problem, calculates the target functional according to the preset multiplicative regular term and the data error term, and then minimizes the target functional through an iterative method for image reconstruction, so as to preserve the organism well The edge morphology of the organ gives the reconstructed image a "blocking constant" property and good anti-noise performance.

In order to achieve the above object, the biomedical electrical impedance imaging device based on multiplicative regularization according to the embodiment of the second aspect of the present invention includes: a measurement module, configured to arrange a plurality of electrodes around the object to be imaged, and during the measurement process, take turns Constant current excites two electrodes in the plurality of electrodes and measures the potential difference on other electrodes in the plurality of electrodes to obtain a frame of measurement data; a calculation module is used for according to the measurement data and forward simulation data Calculate the data error term of the electrical impedance imaging problem, and calculate the target functional according to the preset multiplicative regular term and the data error term; the image reconstruction module is used to minimize the target functional by an iterative method to perform image analysis reconstruction.

In the biomedical electrical impedance imaging device based on multiplicative regularization in the embodiment of the present invention, the constant current source is used to excite and measure two electrodes in the plurality of electrodes in turn to obtain measurement data, and according to the measurement data and forward simulation The data calculates the data error term of the electrical impedance imaging problem, calculates the target functional according to the preset multiplicative regular term and the data error term, and then minimizes the target functional through an iterative method for image reconstruction, so as to preserve the organism well The edge morphology of the organ gives the reconstructed image a "blocking constant" property and good anti-noise performance.

Additional aspects and advantages of the present invention will be set forth, in part, from the following description, and in part will be apparent from the following description, or may be learned by practice of the invention.

Description of drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of embodiments taken in conjunction with the accompanying drawings, wherein:

1 is a flowchart of a biomedical electrical impedance imaging method based on multiplicative regularization according to an embodiment of the present invention;

2 is an example diagram of a simulation model constructed by a multiplicative regularization-based biomedical electrical impedance imaging method according to an embodiment of the present invention;

3(a) is a schematic diagram of an image reconstruction result without noise according to an embodiment of the present invention;

3(b) is a schematic diagram of an image reconstruction result when noise is applied according to an embodiment of the present invention;

Figure 3(c) is another schematic diagram of an image reconstruction result when noise is applied according to an embodiment of the present invention;

4 is a schematic structural diagram of a biomedical electrical impedance imaging device based on multiplicative regularization according to an embodiment of the present invention.

Detailed ways

The following describes in detail the embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary, and are intended to explain the present invention and should not be construed as limiting the present invention.

The method and apparatus for biomedical electrical impedance imaging based on multiplicative regularization according to embodiments of the present invention will be described below with reference to the accompanying drawings.

FIG. 1 is a flowchart of a biomedical electrical impedance imaging method based on multiplicative regularization according to an embodiment of the present invention. As shown in Figure 1, the multiplicative regularization-based biomedical electrical impedance imaging method may include:

S101, arranging a plurality of electrodes around the object to be imaged, and during the measurement process, excite two electrodes of the plurality of electrodes with a constant current in turn and measure the potential difference on other electrodes of the plurality of electrodes, so as to obtain a frame of measurement data .

Specifically, in order to obtain the reconstructed image of the object to be imaged, a plurality of electrodes may be arranged around the object to be imaged, and during the measurement process, two electrodes of the plurality of electrodes are excited in turn with a constant current, and the electrodes of the plurality of electrodes are measured. Potential difference on other electrodes to get a frame of measurement data. An example is as follows:

Assuming that multiple electrodes are arranged around the human thoracic cavity, during the measurement process, a constant current excitation is applied to two of the multiple electrodes and the potential difference of the other electrodes is measured, and the position of the excitation electrodes is continuously changed and corresponding measurements are made. , and finally get a frame of measurement data, which can be recorded as a vector m.

Wherein, the number of electrodes in this embodiment may be 16, or may be other numbers, which are not limited herein.

It should be noted that, in this embodiment, in addition to arranging multiple electrodes in the chest cavity of the human body, multiple electrodes can also be arranged in the brain of the human body, and the specific arrangement positions can be arranged in corresponding parts of the human body according to actual needs , the arrangement positions of the plurality of electrodes are not specifically limited here.

Further, in this embodiment, for the determined constant current excitation, the potential distribution in the object to be imaged needs to satisfy the following Poisson equation and boundary conditions:

At electrode l on the boundary (2)

Elsewhere on the border (3)

Among them, φ is the potential distribution in the object to be imaged, σ is the conductivity distribution, I _l is the current passing through the electrode 1, n is the boundary normal, and r is the spatial coordinate. The above partial differential equation can be solved by the finite element method, and then the potential difference of the electrodes on the boundary can be obtained.

S102: Calculate the data error term of the electrical impedance imaging problem according to the measurement data and the forward simulation data, and calculate the target functional according to the preset multiplicative regular term and the data error term.

Specifically, after the measurement data is obtained, the data error term of the electrical impedance imaging problem can be calculated by subtracting the forward simulation data and the measurement data.

Among them, the data error term of the electrical impedance imaging problem can be calculated by the following formula:

Data error term = ||S(σ)-m|| ² (4)

Among them, σ is the conductivity to be inverted, S(·) is the forward operator for solving the positive problem, and m is the measurement data.

Furthermore, the target functional can be calculated according to the preset multiplicative regular term and the data error term.

It should be noted that, in this embodiment, the preset multiplicative regular term is set based on the principle of total variation, and the multiplicative regular term may include various forms, such as L ₂ norm regular term, weighted L ₂ -norm regularization term, etc.

The specific implementation process of calculating the target functional according to the preset multiplicative regular term and the data error term may include the following steps:

The data error term and the multiplicative regular term based on the total variation principle are multiplied to obtain the target functional. The calculation of the objective functional can be achieved by the following formula:

C _n (σ)=||S(σ)-m|| ² ×R _n (σ) (5)

Among them, C _n (σ) is the target functional, σ is the conductivity to be inverted, S( ) is the forward operator to solve the positive problem, m is the measurement data, R _n (σ) is the multiplicative regular term, n is the number of iterations.

S103, minimize the target functional by an iterative method to perform image reconstruction.

Specifically, minimizing the objective functional can be achieved in a number of ways. For example, the steepest descent method, the Gauss-Newton method, the conjugate gradient method, etc. Of course, there are other iterative methods, which will not be repeated here.

The following takes the Gauss-Newton method as an example to describe the minimization objective functional in detail.

First, solve the gradient vector g _n (σ) and the Hessian matrix G _n (σ) of the target functional, which can be written as:

in, is the gradient vector of the multiplicative regular term, is the Hessian matrix of the multiplicative regular term, J(σ) is the Jacobian matrix, σ is the discrete conductivity to be inverted, S( ) is the discretized forward operator for solving the positive problem, m is the measurement data, n is the number of iterations.

It should be noted that the above Jacobian matrix can be quickly obtained by formula (8):

Among them, Ω is the problem solving area, φ _i is the potential distribution generated in the area Ω when the ith pair of electrodes is excited, u _j is the potential distribution generated in the area Ω when the jth pair of electrodes is excited, and δσ(e) is the unit eigenfunction of e.

Second, the conductivity to be inverted can be updated by the following search directions:

Δσ _n = -[G _n ] ^-1 ·g _n (9)

Among them, g _n is the gradient vector of the target functional, and G _n is the Hessian matrix of the target functional.

Furthermore, after minimizing the target functional through the above iterative method, a reconstructed image of the object to be imaged can be obtained.

In order to verify the performance of the above reconstruction method, a simulation model is also constructed in the embodiment of the present invention, and its finite element division is shown in FIG. 2 . The background conductivity in this model is 0.25 S/m, the conductivity of the semicircle with sharp edges is 0.1 S/m, and the 16 dots around the model represent electrodes. Using the model to solve the positive problem with the finite element method can generate simulated measurement data, and then the performance of the above reconstruction method can be verified according to the generated simulated measurement data.

Further, the image reconstruction of the object to be imaged can be performed under different noise levels through the above simulation model. For details, please refer to Fig. 3(a)-Fig. 3(c). Among them, Figure 3(a) is a schematic diagram of the image reconstruction result of the simulated measurement data when no noise is applied; Figure 3(b) is a schematic diagram of the image reconstruction result of the simulated measurement data when 1% noise is applied; Figure 3(c) is a schematic diagram of the image reconstruction result. Schematic diagram of the image reconstruction result when the simulated measurement data is applied with 2% noise. Among them, the image reconstruction in Fig. 3(a)-Fig. 3(c) uses a different grid from that in Fig. 2.

Based on the above examples of image reconstruction of the object to be imaged under different noise levels, it can be seen that the edges of the inclusions in the simulation model can be reconstructed clearly, and the reconstructed image has the characteristic of "blocking constant", and, in the simulation measurement When the noise level of the data is relatively high, such as 2%, the edge of the image can also be reconstructed well, so it shows that the biomedical electrical impedance imaging method based on multiplicative regularization in the embodiment of the present invention has good anti-noise performance .

In summary, the multiplicative regularization-based biomedical electrical impedance imaging method according to the embodiment of the present invention uses a constant current source to excite and measure two electrodes in a plurality of electrodes in turn to obtain measurement data, and according to the measurement data Calculate the data error term of the electrical impedance imaging problem with the forward simulation data, calculate the target functional according to the preset multiplicative regularity term and the data error term, and then minimize the target functional by iterative method to reconstruct the image, so that the image can be reconstructed well. The edge morphology of biological organs is preserved, so that the reconstructed image has the characteristic of "blocking constant" and has good anti-noise performance. In addition, compared with the additive regularization method, the multiplicative regularization method disclosed in the present invention does not need to set a parameter in the objective functional to adjust the relative weights of the data error term and the regularization term, because in minimizing the objective In the process of the functional, the relative weight of the data error term and the regular term can be adjusted dynamically, eliminating the need to determine the parameter value through a rather tedious numerical experiment.

In order to realize the above embodiments, the present invention also proposes a biomedical electrical impedance imaging device based on multiplicative regularization.

4 is a schematic structural diagram of a biomedical electrical impedance imaging device based on multiplicative regularization according to an embodiment of the present invention.

As shown in FIG. 4 , the biomedical electrical impedance imaging device based on multiplicative regularization may include: a measurement module 110 , a calculation module 120 and an image reconstruction module 130 .

The measurement module 110 is used for arranging a plurality of electrodes around the object to be imaged, and during the measurement process, the two electrodes of the plurality of electrodes are excited in turn with a constant current and the potential difference on the other electrodes of the plurality of electrodes is measured, so as to Get a frame of measurement data.

Specifically, in order to obtain the reconstructed image of the object to be imaged, a plurality of electrodes may be arranged around the object to be imaged, and during the measurement process, two electrodes of the plurality of electrodes are excited in turn with a constant current, and the electrodes of the plurality of electrodes are measured. Potential difference on other electrodes to get a frame of measurement data. An example is as follows:

Assuming that multiple electrodes are arranged around the human thoracic cavity, during the measurement process, a constant current excitation is applied to two of the multiple electrodes and the potential difference of the other electrodes is measured, and the position of the excitation electrodes is continuously changed and corresponding measurements are made. , and finally get a frame of measurement data, which can be recorded as a vector m.

Wherein, the number of electrodes in this embodiment may be 16, or may be other numbers, which are not limited herein.

It should be noted that, in this embodiment, in addition to arranging multiple electrodes in the chest cavity of the human body, multiple electrodes can also be arranged in the brain of the human body, and the specific arrangement positions can be arranged in corresponding parts of the human body according to actual needs , the arrangement positions of the plurality of electrodes are not specifically limited here.

Further, in this embodiment, for the determined constant current excitation, the potential distribution in the object to be imaged needs to satisfy the following Poisson equation and boundary conditions:

At electrode l on the boundary (2)

Elsewhere on the border (3)

Among them, φ is the potential distribution in the object to be imaged, σ is the conductivity distribution, I _l is the current passing through the electrode 1, n is the boundary normal, and r is the spatial coordinate. The above partial differential equation can be solved by the finite element method, and then the potential difference of the electrodes on the boundary can be obtained.

The calculation module 120 is configured to calculate the data error term of the electrical impedance imaging problem according to the measurement data and the forward simulation data, and calculate the target functional according to the preset multiplicative regular term and the data error term;

Specifically, after obtaining the measurement data, the calculation module 120 may further calculate the data error term of the electrical impedance imaging problem.

Among them, the data error term of the electrical impedance imaging problem can be calculated by the following formula:

Data error term = ||S(σ)-m|| ² (4)

Among them, σ is the conductivity to be inverted, S(·) is the forward operator for solving the positive problem, and m is the measurement data.

Furthermore, the target functional can be calculated according to the preset multiplicative regular term and the data error term.

It should be noted that, in this embodiment, the preset multiplicative regular term is set based on the principle of total variation, and the multiplicative regular term may include various forms, such as the L ₂ norm regular term, the weighted L ₂ -norm regularization term, etc.

The specific implementation process of calculating the target functional according to the preset multiplicative regular term and the data error term may include the following steps:

The data error term and the multiplicative regular term based on the total variation principle are multiplied to obtain the target functional. The calculation of the objective functional can be achieved by the following formula:

C _n (σ)=||S(σ)-m|| ² ×R _n (σ) (5)

Among them, C _n (σ) is the target functional, σ is the conductivity to be inverted, S( ) is the forward operator to solve the positive problem, m is the measurement data, R _n (σ) is the multiplicative regular term, n is the number of iterations.

The image reconstruction module 130 is used to minimize the target functional by an iterative method for image reconstruction. Specifically, minimizing the objective functional can be achieved in a number of ways. For example, any of the steepest descent method, the Gauss-Newton method, and the conjugate gradient method. Of course, there are other iterative methods, which will not be repeated here.

The following takes the Gauss-Newton method as an example to describe the minimization objective functional in detail.

First, solve the gradient vector g _n (σ) and the Hessian matrix G _n (σ) of the target functional, which can be written as:

in, is the gradient vector of the multiplicative regular term, is the Hessian matrix of the multiplicative regular term, J(σ) is the Jacobian matrix, σ is the discrete conductivity to be inverted, S( ) is the discretized forward operator for solving the positive problem, m is the measurement data, n is the number of iterations.

It should be noted that the above Jacobian matrix can be quickly obtained by formula (8):

Among them, Ω is the problem solving area, φ _i is the potential distribution generated in the area Ω when the ith pair of electrodes is excited, u _j is the potential distribution generated in the area Ω when the jth pair of electrodes is excited, and δσ(e) is the unit eigenfunction of e.

Second, the conductivity to be inverted can be updated by the following search directions:

Δσ _n = -[G _n ] ^-1 ·g _n (9)

Among them, g _n is the gradient vector of the target functional, and G _n is the Hessian matrix of the target functional.

Furthermore, the image reconstruction module 130 can obtain a reconstructed image of the object to be imaged after minimizing the target functional through the above-mentioned iterative method.

In order to verify the performance of the above reconstruction method, a simulation model is also constructed in the embodiment of the present invention, and its finite element division is shown in FIG. 2 . The background conductivity in this model is 0.25 S/m, the conductivity of the semicircle with sharp edges is 0.1 S/m, and the 16 dots around the model represent electrodes. Using the model to solve the positive problem with the finite element method can generate simulated measurement data, and then the performance of the above reconstruction method can be verified according to the generated simulated measurement data.

Further, the image reconstruction of the object to be imaged can be performed under different noise levels through the above simulation model. For details, please refer to Fig. 3(a)-Fig. 3(c). Among them, Figure 3(a) is a schematic diagram of the image reconstruction result of the simulated measurement data when no noise is applied; Figure 3(b) is a schematic diagram of the image reconstruction result of the simulated measurement data when 1% noise is applied; Figure 3(c) is a schematic diagram of the image reconstruction result. Schematic diagram of the image reconstruction result when the simulated measurement data is applied with 2% noise. Among them, the image reconstruction in Fig. 3(a)-Fig. 3(c) uses a different grid from that in Fig. 2.

Based on the above examples of reconstructed images of the object to be imaged under different noise levels, it can be seen that the edges of the inclusions in the simulation model can be clearly reconstructed, and the reconstructed image has the characteristic of "blocking constant", and, in the simulation measurement When the noise level of the data is relatively high, such as 2%, the edge of the image can also be reconstructed well, so it shows that the biomedical electrical impedance imaging method based on multiplicative regularization in the embodiment of the present invention has good anti-noise performance .

In the biomedical electrical impedance imaging device based on multiplicative regularization in the embodiment of the present invention, the constant current source is used to excite and measure two electrodes in the plurality of electrodes in turn to obtain measurement data, and according to the measurement data and forward simulation The data calculates the data error term of the electrical impedance imaging problem, calculates the target functional according to the preset multiplicative regular term and the data error term, and then minimizes the target functional through an iterative method for image reconstruction, so as to preserve the organism well The edge morphology of the organ gives the reconstructed image a "blocking constant" property and good anti-noise performance. In addition, compared with the additive regularization method, the multiplicative regularization method disclosed in the present invention does not need to set a parameter in the objective functional to adjust the relative weights of the data error term and the regularization term, because in minimizing the objective In the process of the functional, the relative weight of the data error term and the regular term can be adjusted dynamically, eliminating the need to determine the parameter value through a rather tedious numerical experiment.

In addition, the terms "first" and "second" are only used for descriptive purposes, and should not be construed as indicating or implying relative importance or implying the number of indicated technical features. Thus, a feature delimited with "first", "second" may expressly or implicitly include at least one of that feature. In the description of the present invention, "plurality" means at least two, such as two, three, etc., unless otherwise expressly and specifically defined.

In the description of this specification, description with reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples", etc., mean specific features described in connection with the embodiment or example , structure, material or feature is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine the different embodiments or examples described in this specification, as well as the features of the different embodiments or examples, without conflicting each other.

Any description of a process or method in the flowcharts or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing a specified logical function or step of the process , and the scope of the preferred embodiments of the invention includes alternative implementations in which the functions may be performed out of the order shown or discussed, including performing the functions substantially concurrently or in the reverse order depending upon the functions involved, which should It is understood by those skilled in the art to which the embodiments of the present invention belong.

The logic and/or steps represented in flowcharts or otherwise described herein, for example, may be considered an ordered listing of executable instructions for implementing the logical functions, may be embodied in any computer-readable medium, For use with, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other system that can fetch instructions from and execute instructions from an instruction execution system, apparatus, or apparatus) or equipment. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport the program for use by or in connection with an instruction execution system, apparatus, or apparatus. More specific examples (non-exhaustive list) of computer readable media include the following: electrical connections with one or more wiring (electronic devices), portable computer disk cartridges (magnetic devices), random access memory (RAM), Read Only Memory (ROM), Erasable Editable Read Only Memory (EPROM or Flash Memory), Fiber Optic Devices, and Portable Compact Disc Read Only Memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program may be printed, as the paper or other medium may be optically scanned, for example, followed by editing, interpretation, or other suitable medium as necessary process to obtain the program electronically and then store it in computer memory.

It should be understood that various parts of the present invention may be implemented in hardware, software, firmware or a combination thereof. In the above-described embodiments, various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or a combination of the following techniques known in the art: Discrete logic circuits, application specific integrated circuits with suitable combinational logic gates, Programmable Gate Arrays (PGA), Field Programmable Gate Arrays (FPGA), etc.

Those skilled in the art can understand that all or part of the steps carried by the methods of the above embodiments can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, and the program can be stored in a computer-readable storage medium. When executed, one or a combination of the steps of the method embodiment is included.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing module, or each unit may exist physically alone, or two or more units may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware, and can also be implemented in the form of software function modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, and the like. Although the embodiments of the present invention have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and should not be construed as limiting the present invention. Embodiments are subject to variations, modifications, substitutions and variations.

Claims (8)

1. a biomedical electrical impedance imaging method based on multiplicative regularization, is characterized in that, comprises the following steps: A plurality of electrodes are arranged around the object to be imaged, and during the measurement process, two electrodes of the plurality of electrodes are excited by turns with a constant current and the potential difference on the other electrodes of the plurality of electrodes is measured to obtain a frame Measurement data; The data error term of the electrical impedance imaging problem is calculated according to the measurement data and the forward simulation data, and the target functional is calculated according to the preset multiplicative regular term and the data error term, wherein the preset multiplicative regular term is set based on the principle of total variation, and the target functional is calculated according to the preset multiplicative regular term and the data error term, including: Multiplying the data error term and the multiplicative regular term based on the total variation principle to obtain the target functional; The objective functional is minimized by an iterative method for image reconstruction. 2. The method for biomedical electrical impedance imaging based on multiplicative regularization as claimed in claim 1, wherein the data error term of the electrical impedance imaging problem is calculated and obtained by the following formula: Data error term = ||S(σ)-m|| ² Among them, σ is the conductivity to be inverted, S(·) is the forward operator for solving the positive problem, and m is the measurement data. 3. The biomedical electrical impedance imaging method based on multiplicative regularization according to claim 1, wherein, for the determined constant current excitation, the potential distribution in the object to be imaged satisfies the following Poisson equation and Boundary conditions: On the border at electrode l elsewhere on the border Among them, φ is the potential distribution in the object to be imaged, σ is the conductivity distribution, Ω is the area of the object to be imaged, Il is the current passing through the electrode ₁ , n is the boundary normal, and r is the spatial coordinate. 4 . The method for biomedical electrical impedance imaging based on multiplicative regularization according to claim 1 , wherein the iterative method comprises any one of the steepest descent method, the Newton method, and the conjugate gradient method. 5 . 5. A biomedical electrical impedance imaging device based on multiplicative regularization, comprising: The measurement module is used for arranging a plurality of electrodes around the object to be imaged, and during the measurement process, the two electrodes of the plurality of electrodes are excited by turns with a constant current and the potential difference on the other electrodes of the plurality of electrodes is measured , to get a frame of measurement data; a calculation module, configured to calculate the data error term of the electrical impedance imaging problem according to the measurement data and the forward simulation data, and calculate the target functional according to the preset multiplicative regular term and the data error term, wherein the preset The multiplicative regular term of is set based on the principle of total variation, and the calculation module is specifically used for: Multiplying the data error term and the multiplicative regular term based on the total variation principle to obtain the target functional; An image reconstruction module for minimizing the target functional by an iterative method for image reconstruction. 6 . The biomedical electrical impedance imaging device based on multiplicative regularization according to claim 5 , wherein the data error term of the electrical impedance imaging problem is calculated and obtained by the following formula: 7 . Data error term = ||S(σ)-m|| ² Among them, σ is the conductivity to be inverted, S(·) is the forward operator for solving the positive problem, and m is the measurement data. 7. The biomedical electrical impedance imaging device based on multiplicative regularization according to claim 5, wherein, for the determined constant current excitation, the potential distribution in the object to be imaged satisfies the following Poisson equation and Boundary conditions: On the border at electrode l elsewhere on the border Among them, φ is the potential distribution in the object to be imaged, σ is the conductivity distribution, Ω is the area of the object to be imaged, Il is the current passing through the electrode ₁ , n is the boundary normal, and r is the spatial coordinate. 8 . The biomedical electrical impedance imaging device based on multiplicative regularization according to claim 5 , wherein the iterative method comprises any one of the steepest descent method, the Newton method, and the conjugate gradient method. 9 .