Electrical impedance imaging method and device based on hybrid excitation and program product

Abstract

The invention relates to the field of intelligent medical treatment, in particular to an electrical impedance imaging method and device based on mixed excitation and a program product. Comprising the steps that an initial current vector and an initial boundary voltage vector of mixed excitation are obtained, the mixed excitation comprises L kinds of excitation, and L is a natural number larger than 1; performing weighted fusion on the L excitation initial current vectors to obtain a mixed excitation current vector, and performing weighted fusion on the L excitation initial boundary voltages to obtain a mixed excitation voltage; performing electrical impedance imaging optimization function calculation based on the mixed excitation current vector and the mixed excitation voltage to obtain conductivity change; the optimization function is used for carrying out regularization calculation on regional potential distribution and conductivity variation based on the mixed excitation current vector and the mixed excitation voltage; an electrical impedance distribution image is generated based on the conductivity change. According to the invention, the resolution of EIT imaging is improved, and the method has good clinical value.

Description

Technical Field

[0001] The present application relates to the field of intelligent medicine, and particularly to an electrical impedance tomography method, device, program product and computer-readable storage medium based on hybrid excitation. Background Art

[0002] Electrical impedance tomography (EIT) is a novel medical imaging method, which has the characteristics of non-invasive, non-radiative, portable device, functional imaging, etc., and has received extensive attention in clinics. It has broad application prospects in aspects such as pulmonary ventilation monitoring, pulmonary blood perfusion assessment, dynamic detection of brain diseases, etc.

[0003] The EIT technology places an electrode array on the body surface of a human body, then applies a safe excitation current to the human body and simultaneously detects the body surface voltage. Finally, according to the image reconstruction algorithm, it calculates the distribution of impedance changes inside the human body, and then evaluates the physiological and pathological change information inside the human body based on the impedance change distribution. The imaging process of EIT belongs to the solution of a typical electromagnetic field inverse problem. In the solution of the inverse problem, since the measured boundary voltage mainly comes from the vicinity of the human body boundary (the excitation current is mainly distributed near the electrodes), a small change in the boundary conditions can have a huge impact on the imaging results. That is, the boundary region is highly sensitive, while the internal region is weakly sensitive, which is an obvious feature of the solution of the EIT inverse problem. Since the path of the current inside the human body is determined by the current excitation position, the imaging sensitivity distribution of EIT is closely related to the current excitation method. In order to obtain better imaging sensitivity, many researchers have compared different excitation methods. The most common ones are adjacent excitation (adjacent electrode excitation current) and opposite excitation (opposite electrode excitation current). However, different excitation methods still have their own problems. For example, in the adjacent excitation mode, the impedance change in the boundary region often has a large reverse artifact in the imaging result, and because the excitation electrodes are adjacent, the values of most measured channels are low, resulting in a very low signal-to-noise ratio of the actual measured data; while in the opposite excitation mode, the impedance change in the central region often shows very poor spatial resolution in the imaging result. The imaging characteristics of different excitation modes seriously affect the accuracy of the EIT imaging results. It is very necessary to explore new excitation modes and study the corresponding EIT imaging algorithms to improve the EIT imaging results and enhance the practical application ability of the EIT technology. Summary of the Invention

[0004] In view of the above problems, the present invention provides an electrical impedance tomography method based on hybrid excitation, specifically including:

[0005] Obtain an initial current vector and an initial boundary voltage vector of the hybrid excitation, where the hybrid excitation includes L kinds of excitations, and L is a natural number greater than 1;

[0006] Weighted fusion is performed on L kinds of excitation initial current vectors to obtain a mixed excitation current vector, and weighted fusion is performed on L kinds of excitation initial boundary voltages to obtain a mixed excitation voltage;

[0007] Based on the mixed excitation current vector and the mixed excitation voltage, an electrical impedance tomography optimization function is calculated to obtain the conductivity change; the optimization function is based on the mixed excitation current vector and the mixed excitation voltage for regional electric potential distribution and conductivity change regularization calculation;

[0008] Based on the conductivity change, an electrical impedance distribution image is generated.

[0009] The optimization function further includes minimizing correction iterative calculation for the regional electric potential distribution and conductivity change regularization calculation;

[0010] Optionally, the optimization function is expressed as:

[0011]

[0012] Wherein, is the stiffness matrix, which represents the relationship between the excitation current and the regional electric potential distribution of the imaging area; represents the initial conductivity estimate, is the boundary measurement voltage under mixed excitation, and are regularization parameters respectively, and are regularization matrices respectively, 、 and are norms respectively, represents the excitation vector, represents the current conductivity change.

[0013] The calculation of the conductivity change is as follows: obtain the norm parameter, input the norm parameter into the optimal function to assign a value to the norm, and then transform and differentiate the assigned optimization function based on the Taylor formula to obtain the conductivity change;

[0014] Optionally, the norms in the optimization function include the norm p of the regional electric potential distribution, the norms q and o of the conductivity change rate;

[0015] Optionally, the assignment of the norms includes p assigned to 2, q assigned to 2, and o assigned to 0;

[0016] Optionally, when p of the norm is assigned to 2, q is assigned to 2, and o is assigned to 0, the conductivity change is:

[0017]

[0018] wherein, represents the initial conductivity estimate, is the boundary measurement voltage under mixed excitation, represents the regularization parameter, and represent the regularization matrix and the transposed matrix of the regularization matrix, represents the sensitivity matrix under mixed excitation conditions, represents the conductivity distribution under mixed excitation conditions, represents the initial conductivity distribution under mixed excitation conditions.

[0019] The mixed excitation includes any of the following: adjacent excitation, opposite excitation, and phase - to - phase excitation;

[0020] Optionally, when the mixed excitation is two kinds and the excitations are adjacent excitation and opposite excitation, the conductivity change is:

[0021]

[0022] wherein, is obtained by calculation under adjacent excitation, is obtained by calculation under opposite excitation, and are the weight coefficients corresponding to adjacent excitation and opposite excitation; and are the boundary measurement voltages corresponding to adjacent excitation and opposite excitation in the mixed excitation; and are the initial boundary measurement voltages corresponding to adjacent excitation and opposite excitation in the mixed excitation; and are the regularization matrices corresponding to adjacent excitation and opposite excitation in the mixed excitation;

[0023] Optionally, when the weights of opposite excitation and adjacent excitation are the same, the conductivity change is:

[0024] .

[0025] The method further includes calculating the conductivity change under independent excitation, and the calculation of the conductivity change under independent excitation is to obtain the conductivity change of any one of the L kinds of excitations by adjusting the weights in the conductivity change under mixed excitation;

[0026] Optionally, the adjustment of the weights is to adjust the weight of one kind of excitation to 1 and adjust the weights of other excitations to 0; Optionally, when the mixed excitation is adjacent excitation and opposite excitation and the independent excitation is adjacent excitation, the conductivity change is:

[0027] ;

[0028] Optionally, when the mixed excitation is adjacent excitation and opposite excitation, and the independent excitation is opposite excitation, the conductivity change is:

[0029] 。

[0030] The regularization adopts one or more of the following: Tikhonov regularization, L1 regularization, and hybrid regularization;

[0031] Optionally, the regularization matrix is calculated by point spread of Gaussian distribution.

[0032] The purpose of the present invention is to provide a method for generating an EIT functional image based on mixed excitation, including:

[0033] Obtain the electrical impedance distribution imaging of the subject according to the above-mentioned electrical impedance imaging method based on mixed excitation;

[0034] Obtain the tidal EIT image of the subject;

[0035] Extract parameters from the electrical impedance distribution imaging and the tidal EIT image to obtain functional parameter data;

[0036] Perform image reconstruction based on the functional parameter data to obtain the EIT functional image;

[0037] Optionally, the parameters include one or more of the following: ventilation homogeneity, ventilation time, left-right ventilation ratio, ventilation center, ratio of expiratory time to inspiratory time, and expiratory speed.

[0038] The purpose of the present invention is to provide a computer program product, which includes a computer program or instruction, and the computer program or instruction is executed by a processor to implement the above-mentioned electrical impedance imaging method based on mixed excitation.

[0039] The purpose of the present invention is to provide a computer device, which includes a memory, a processor, and a computer program or instruction stored on the memory, and the computer program or instruction is executed by the processor to implement the above-mentioned electrical impedance imaging method based on mixed excitation.

[0040] The purpose of the present invention is to provide a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is executed by a processor to implement the above-mentioned electrical impedance imaging method based on mixed excitation.

[0041] Advantages of the present invention:

[0042] 1. In view of the problems of reverse artifacts and poor spatial resolution in the currently adopted excitation method for electrical impedance tomography (EIT) imaging, the present invention proposes a hybrid excitation method that can flexibly utilize different excitation modes and perform EIT imaging based on the boundary measurement results of the hybrid excitation, avoiding the problems of single excitation and having good EIT imaging effects, which is helpful for the clinical application of EIT imaging.

[0043] 2. The present invention fuses and calculates the boundary voltages of different excitations through an optimization function to obtain the conductivity change, providing a new implementation path for EIT imaging. At the same time, for the hybrid excitation, the conductivity changes of different excitations are obtained by adjusting the weights. When a certain excitation in the hybrid excitation is required, the weight of this excitation is adjusted to 1, and the weights of the other excitations are adjusted to 0. When it is necessary to highlight the characteristics of a certain excitation imaging, the weight of this excitation is increased, and the weights of other excitations are decreased until the EIT imaging that meets the requirements is obtained.

[0044] 3. The functional EIT image is obtained by fusing and reconstructing the EIT imaging (breathing EIT) with the tidal EIT imaging using hybrid excitation, which is helpful for improving the high resolution of the functional EIT image, facilitating clinical applications, and having good application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0046] Figure 1 Schematic diagram of the electrical impedance tomography method based on hybrid excitation provided by the embodiment of the present invention;

[0047] Figure 2 Schematic diagram of the electrical impedance tomography system based on hybrid excitation provided by the embodiment of the present invention;

[0048] Figure 3 Schematic diagram of the electrical impedance tomography device based on hybrid excitation provided by the embodiment of the present invention;

[0049] Figure 4 Respiratory waveforms of the subject with opposing excitation (sum of absolute values of boundary voltages of 192 channels), respiratory waveforms of opposing excitation (sum of absolute values of boundary voltages of 208 channels), and respiratory waveforms of opposing - adjacent hybrid excitation (sum of absolute values of boundary voltages of 200 channels) provided by the embodiment of the present invention;

[0050] Figure 5For the imaging based on hybrid excitation provided by the embodiments of the present invention, the first column is the moisture map of opposite excitation, the second column is the moisture map of adjacent excitation, and the third column is the moisture map of opposite-adjacent hybrid excitation. Detailed implementation manners

[0051] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0052] In some processes described in the specification, claims and the above-mentioned drawings of the present invention, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as S101, S102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this article are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that "first" and "second" are of different types.

[0053] Figure 1 The schematic diagram of the electrical impedance tomography method based on hybrid excitation provided by the embodiments of the present invention specifically includes:

[0054] S101: Obtain the initial current vector and the initial boundary voltage vector of the hybrid excitation, where the hybrid excitation includes L types of excitations, and L is a natural number greater than 1;

[0055] In one embodiment, the hybrid excitation includes any of the following: adjacent excitation, opposite excitation, and interphase excitation.

[0056] In a specific embodiment, the impedance data acquisition system: a 16-electrode chest system, the excitation current is 1 mA, during the data acquisition process, the subject wears an EIT chest restraint belt, keeps a sitting posture and calm breathing, and collects the chest boundary voltage with opposite-adjacent hybrid excitation for 3 minutes.

[0057] In one embodiment, when the hybrid excitation obtains the boundary voltage, L types of excitation currents are performed in sequence, and L types of boundary voltages are obtained in sequence. The L types of excitation collections are repeated until the preset duration.

[0058] In one embodiment, when the hybrid excitation obtains the boundary voltage, the collection duration is allocated to the L types of excitations. When the first excitation voltage is collected, the second excitation voltage is collected to obtain the boundary voltages of the L types of excitations. Optionally, the collection durations of the L types of excitations are equally divided for collection.

[0059] In one embodiment, the method further includes data preprocessing, in which the boundary voltage after acquisition is filtered for noise to obtain the filtered initial boundary voltage.

[0060] S102: Weightedly fuse the L initial excitation current vectors to obtain a mixed excitation current vector, and weightedly fuse the L initial excitation boundary voltages to obtain a mixed excitation voltage;

[0061] In a specific embodiment, a mixed excitation mode is established: construct the mixed excitation current vector as , and the boundary measurement voltage vector as .

[0062] Among them, , , is the weight coefficient of the nth excitation, and its value range is 0 to 1, and ; is the excitation vector of the nth excitation; is the boundary voltage collected under the nth excitation.

[0063] S103: Calculate the conductivity change by calculating the electrical impedance tomography optimization function based on the mixed excitation current vector and the mixed excitation voltage; the optimization function is based on the mixed excitation current vector and the mixed excitation voltage for regional potential distribution and conductivity change regularization calculation;

[0064] In one embodiment, the optimization function further includes minimizing and correcting iterative calculation for the regional potential distribution and conductivity change regularization calculation.

[0065] In one embodiment, the optimization function is expressed as:

[0066]

[0067] Among them, is the stiffness matrix, which represents the relationship between the excitation current and the potential distribution in the imaging area; represents the initial conductivity estimate, is the boundary measurement voltage under mixed excitation, and are regularization parameters respectively, and are regularization matrices respectively, , and are norms respectively, represents the excitation vector, represents the current conductivity distribution.

[0068] In one embodiment, the calculation of the conductivity change is as follows: obtain the norm parameter, input the norm parameter into the optimal function to assign a value to the norm, and then transform and differentiate the assigned optimal function based on the Taylor formula to obtain the conductivity change.

[0069] In one embodiment, the norms in the optimization function include the norm p of the regional potential distribution, the norms q and o of the conductivity change rate.

[0070] In one embodiment, the assignment of values to the norms includes assigning 2 to p, 2 to q, and 0 to o.

[0071] In one embodiment, when 2 is assigned to the norm p, 2 to q, and 0 to o, the conductivity change is:

[0072]

[0073] where, represents the initial conductivity estimate, is the boundary measurement voltage under mixed excitation, represents the regularization parameter, and represent the regularization matrix and the transpose matrix of the regularization matrix, represents the sensitivity matrix under mixed excitation conditions, represents the conductivity distribution under mixed excitation conditions, represents the initial conductivity distribution under mixed excitation conditions.

[0074] In one embodiment, when the mixed excitation is two types and the excitations are adjacent excitation and opposite excitation, the conductivity change is:

[0075]

[0076] where, is obtained by calculation under adjacent excitation, is obtained by calculation under opposite excitation, and are the weight coefficients corresponding to adjacent excitation and opposite excitation; and are the boundary measurement voltages corresponding to adjacent excitation and opposite excitation in the mixed excitation; and are the initial boundary measurement voltages corresponding to adjacent excitation and opposite excitation in the mixed excitation; and are the regularization matrices corresponding to adjacent excitation and opposite excitation in the mixed excitation;

[0077] Optionally, when the weights of the opposing excitation and the adjacent excitation are the same, the conductivity change is:

[0078] .

[0079] In one embodiment, the method further includes calculating the conductivity change of independent excitation, which is obtained by adjusting the weights in the conductivity change of mixed excitation to obtain the conductivity change of any one of the L excitations.

[0080] Optionally, the weight adjustment is to adjust the weight of one excitation to 1 and the weights of other excitations to 0.

[0081] In one embodiment, when the mixed excitation is adjacent excitation and opposing excitation, and the independent excitation is adjacent excitation, the conductivity change is:

[0082] .

[0083] In one embodiment, when the mixed excitation is adjacent excitation and opposing excitation, and the independent excitation is opposing excitation, the conductivity change is:

[0084] .

[0085] In one embodiment, the regularization adopts one or more of the following: Tikhonov regularization, L1 regularization, mixed regularization;

[0086] Optionally, the regularization matrix is calculated by the point spread of the Gaussian distribution.

[0087] In one embodiment, the mixed excitation includes L excitations. After the mixed excitation is collected and calculated, the conductivity change of the mixed excitation is obtained. Based on the conductivity change of the mixed excitation, electrical impedance tomography is constructed. In addition, the conductivity change of any one or several of the L excitations can be obtained by adjusting the weights in the mixed excitation, and electrical impedance tomography is constructed based on the adjusted conductivity change.

[0088] In a specific embodiment, an optimization function for EIT imaging of the mixed excitation mode is constructed:

[0089]

[0090] where is the stiffness matrix, which represents the relationship between the excitation current and the potential distribution in the imaging region; represents the initial conductivity estimate, is the boundary measurement voltage under the mixed excitation, and are the regularization parameters respectively, and are regularization matrices, , and are norms respectively.

[0091] When , and , the optimization function can be rewritten as:

[0092]

[0093] According to the Taylor expansion, , ignoring the high-order terms , denoting , then, can be further rewritten as,

[0094]

[0095] And let , so:

[0096]

[0097] Derive the above formula and set the derivative to 0, then,

[0098]

[0099] It can be further obtained that

[0100] So, the change in conductivity can be written as, .

[0101] In a specific embodiment, when the total number of excitation modes , and the excitation modes are opposite excitation and adjacent excitation respectively, the change in conductivity can be written as,

[0102]

[0103] where, , is obtained by calculation under adjacent excitation, , similarly, is obtained by calculation under opposite excitation, ; and are the weight coefficients corresponding to adjacent excitation and opposite excitation, and ; and are the boundary measurement voltages corresponding to adjacent excitation and opposite excitation in the mixed excitation; and are the initial boundary measurement voltages corresponding to adjacent excitations and the initial boundary measurement voltages corresponding to opposing excitations in the hybrid excitation; and are the regularization matrices corresponding to adjacent excitations and the regularization matrices corresponding to opposing excitations in the hybrid excitation.

[0104] In a specific embodiment, when the weights of the opposing excitation and the adjacent excitation are the same, that is, when , the conductivity change is

[0105]

[0106] Similarly, when the weight of the opposing excitation , which is equivalent to only retaining the opposing excitation. At this time, the conductivity change is

[0107]

[0108] Similarly, when the weight of the adjacent excitation , which is equivalent to only retaining the adjacent excitation. At this time, the conductivity change is

[0109] .

[0110] In a specific embodiment, the regularization matrix is calculated using a point spread function with a Gaussian distribution, that is, and , is the covariance of the amplitude responses at different positions within the imaging region. In addition, the regularization parameter is selected.

[0111] S104: Generate an electrical impedance distribution image based on the conductivity change.

[0112] In one embodiment, the electrical impedance distribution image is a respiratory EIT image.

[0113] In a specific embodiment, human data is collected: 15 healthy adult males, aged 20 - 23 years old, with a weight of 68 - 72 kg;

[0114] Electrical impedance data acquisition system: 16 - electrode chest system, excitation current 1 mA, supporting opposing excitation, adjacent excitation, and opposing - adjacent hybrid excitation. Data acquisition process: The subject wears an EIT chest restraint belt, maintains a sitting position and calm breathing, and sequentially collects chest boundary voltage data for 3 minutes with opposing excitation, chest boundary voltage data for 3 minutes with adjacent excitation, and chest boundary voltage data for 3 minutes with opposing - adjacent hybrid excitation.

[0115] Data analysis: First, the discrete wavelet transform method is used to extract the respiratory component and noise from the original chest boundary voltage data, and then the signal-to-noise ratio SNR of the respiratory signal under three excitation conditions is calculated respectively.

[0116]

[0117] where is the time-series respiratory component signal, is the time-series noise signal.

[0118] Secondly, the respiratory EIT images under opposite excitation, adjacent excitation, and opposite-adjacent hybrid excitation are reconstructed respectively, and the average tidal EIT image within 3 minutes is calculated as the final EIT functional image, which consists of 32 (length) * 32 (width) pixels.

[0119] Then, the reverse artifact parameter (the sum of negative values divided by the sum of positive values) and the separation degree parameter of both lungs (the difference between the maximum positive value and the minimum negative value among 32 pixels in the 16th row in the long-axis direction) of the EIT functional images under three excitation conditions are calculated.

[0120] Finally, the t-test is used to compare the signal-to-noise ratio of the respiratory signal, the reverse artifact ratio, and the separation degree of both lungs among the three excitation methods.

[0121] Figure 4 are the opposite-excitation respiratory waveform (the sum of the absolute values of the boundary voltages of 192 channels), the opposite-excitation respiratory waveform (the sum of the absolute values of the boundary voltages of 208 channels), and the opposite-adjacent hybrid-excitation respiratory waveform (the sum of the absolute values of the boundary voltages of 200 channels) of Subject 1. It can be seen from the results that there is obvious interference in the adjacent-excitation respiratory waveform, while the interference in the opposite-excitation and hybrid-excitation respiratory waveforms is significantly smaller. Through statistical calculation, there are significant differences in the SNR of the chest boundary voltage data under opposite excitation, hybrid excitation, and adjacent excitation (26.89 ± 3.93 dB > 26.30 ± 3.20 dB > 23.88 ± 3.10 dB; p = 0.049 < 0.05), indicating that the hybrid excitation inherits the high signal-to-noise ratio ability of the opposite excitation.

[0122] Figure 5Imaging results of 15 subjects. As can be seen from the figure, there are fewer artifacts in the images of opposite excitations (the first column), while there are many reverse artifacts in the images of adjacent excitations (the second column), resulting in deformation of the pulmonary ventilation area in some patients (the third column). In addition, in the images of opposite excitations, the pulmonary ventilation areas on both sides are connected to each other without an obvious boundary, while in the images of adjacent excitations, the pulmonary ventilation on both sides is significantly independently displayed. From the images of mixed excitations, it can be seen that not only are there few image artifacts, but also the pulmonary ventilation areas on both sides can be independently displayed. Through statistical calculation, there are significant differences in the parameters of the separation degree of the two lungs in the imaging results of opposite excitations, mixed excitations, and adjacent excitations (0.49 (±0.19) < 1.02 (±0.23) < 2.41 (±0.49); p < 0.001), indicating that the mixed excitation inherits the advantage of the adjacent excitation in being able to better separate the bilateral pulmonary ventilation. In addition, through statistical calculation, there are significant differences in the reverse artifact parameters of the imaging results of opposite excitations, mixed excitations, and adjacent excitations (0.13 (±0.0059) < 0.014 (±0.0043) < 0.039 (±0.038); p = 0.004 < 0.01), indicating that the mixed excitation inherits the advantage of the opposite excitation in having fewer reverse imaging artifacts.

[0123] In summary, the mixed excitation inherits the advantages of the opposite excitation in high signal-to-noise ratio ability and having fewer reverse imaging artifacts. At the same time, it inherits the advantage of being able to better separate the bilateral pulmonary ventilation, demonstrating the advantages of the mixed excitation imaging method proposed by the present invention.

[0124] The disclosed embodiment of the present invention also provides a computer program product or system, including a computer program, which implements the steps of the above-mentioned electrical impedance imaging method based on mixed excitation when executed by a processor.

[0125] Figure 2 Schematic diagram of the electrical impedance imaging system based on mixed excitation provided by the embodiment of the present invention, specifically including:

[0126] Acquisition unit: Acquire the initial current vector and initial boundary voltage vector of the mixed excitation, where the mixed excitation includes L kinds of excitations, and L is a natural number greater than 1;

[0127] Weighting unit: Perform weighted fusion on the initial current vectors of the L kinds of excitations to obtain the mixed excitation current vector, and perform weighted fusion on the initial boundary voltages of the L kinds of excitations to obtain the mixed excitation voltage;

[0128] Calculation unit: Calculate the conductivity change based on the mixed excitation current vector and the mixed excitation voltage by using the electrical impedance imaging optimization function; the optimization function is based on the mixed excitation current vector and the mixed excitation voltage for regional potential distribution and conductivity change regularization calculation;

[0129] Imaging unit: Generate an electrical impedance distribution image based on the conductivity change.

[0130] An embodiment of the present invention provides a method for generating an EIT functional image based on hybrid excitation, including:

[0131] Obtain the electrical impedance distribution imaging of the subject according to the above-mentioned electrical impedance imaging method based on hybrid excitation;

[0132] Obtain the tidal EIT image of the subject;

[0133] Extract parameters from the electrical impedance distribution imaging and the tidal EIT image to obtain functional parameter data;

[0134] Perform image reconstruction based on the functional parameter data to obtain an EIT functional image.

[0135] In one embodiment, the parameters include one or more of the following: ventilation uniformity, ventilation time, left-right ventilation ratio, ventilation center, ratio of exhalation time to inhalation time, exhalation speed.

[0136] Figure 3 A schematic diagram of a computer device provided by an embodiment of the present invention specifically includes:

[0137] A memory and a processor; the memory is used to store program instructions; the processor is used to call the program instructions, when the program instructions are executed for any one of the above-mentioned electrical impedance imaging methods based on hybrid excitation, or the above-mentioned method for generating an EIT functional image based on hybrid excitation.

[0138] The disclosed embodiment of the present invention also provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it is for any one of the above-mentioned electrical impedance imaging methods based on hybrid excitation, or the above-mentioned method for generating an EIT functional image based on hybrid excitation.

[0139] The verification results of this verification embodiment show that allocating fixed weights for indications can improve the performance of this method compared to the default settings. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein. In several embodiments provided by this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the couplings or direct couplings or communication connections shown or discussed with each other can be indirect couplings or communication connections through some interfaces, devices, or units, and can be in electrical, mechanical, or other forms. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units. Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and this program can be stored in a computer-readable storage medium. The storage medium can include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk, or optical disk, etc.

[0140] Those of ordinary skill in the art can understand that all or part of the steps in implementing the methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. The above-mentioned medium storage can be read-only memory, magnetic disk, or optical disk, etc.

[0141] The above has introduced in detail a computer device provided by the present invention. For those of ordinary skill in the art, according to the idea of the embodiments of the present invention, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for electrical impedance tomography based on hybrid excitation, characterized in that Including: Obtaining an initial current vector and an initial boundary voltage vector of a mixed excitation, where the mixed excitation includes L types of excitations, and L is a natural number greater than 1; Performing weighted fusion on the initial current vectors of the L types of excitations to obtain a mixed excitation current vector, and performing weighted fusion on the initial boundary voltages of the L types of excitations to obtain a mixed excitation voltage; Calculating an electrical impedance tomography optimization function based on the mixed excitation current vector and the mixed excitation voltage to obtain a conductivity change; the optimization function is based on the mixed excitation current vector and the mixed excitation voltage for regional potential distribution and conductivity change regularization calculation; Generating an electrical impedance distribution image based on the conductivity change.

2. The electrical impedance tomography method based on hybrid excitation according to claim 1, wherein The optimization function further includes minimizing correction iterative calculation for regional potential distribution and conductivity change regularization calculation; Optionally, the optimization function is expressed as: Among them, is the stiffness matrix, which represents the relationship between the excitation current and the potential distribution in the imaging region; represents the initial conductivity estimate, is the boundary measurement voltage under the mixed excitation, and are the regularization parameters respectively, and are the regularization matrices respectively, , and are the norms respectively, represents the excitation vector, represents the current conductivity distribution.

3. The electrical impedance tomography method based on hybrid excitation according to claim 1, wherein The calculation of the conductivity change is: obtaining a norm parameter, inputting the norm parameter into the optimization function to assign a value to the norm, and then converting and differentiating the optimized function with the assigned value based on the Taylor formula to obtain the conductivity change; Optionally, the norms in the optimization function include the norm p of the regional potential distribution, the norms q and o of the conductivity change rate; Optionally, the assignment of the norms includes assigning p as 2, q as 2, and o as 0; Optionally, when p of the norm is assigned as 2, q as 2, and o as 0, the conductivity change is: Among them, represents the initial conductivity estimate, is the boundary measurement voltage under mixed excitation, represents the regularization parameter, and represent the regularization matrix and the transposed matrix of the regularization matrix, represents the excitation vector, represents the current conductivity distribution, represents the initial conductivity distribution under mixed excitation conditions.

4. The electrical impedance tomography method based on hybrid excitation according to claim 1, wherein The mixed excitation includes any of the following: adjacent excitation, opposite excitation, interphase excitation; Optionally, when the mixed excitation is two types and the excitations are adjacent excitation and opposite excitation, the conductivity change is: Among them, is obtained by calculation under adjacent excitation, is obtained by calculation under opposite excitation, and are the weight coefficients corresponding to adjacent excitation and opposite excitation; and are the boundary measurement voltages corresponding to adjacent excitation and the boundary measurement voltages corresponding to opposite excitation in the mixed excitation; and are the initial boundary measurement voltages corresponding to adjacent excitation and the initial boundary measurement voltages corresponding to opposite excitation in the mixed excitation; and are the regularization matrices corresponding to adjacent excitation and the regularization matrices corresponding to opposite excitation in the mixed excitation; Optionally, when the weights of the opposite excitation and the adjacent excitation are the same, the conductivity change is: 。 5. The electrical impedance tomography method based on hybrid excitation according to claim 1, wherein The method further includes independent excitation conductivity change calculation, and the independent excitation conductivity change calculation is to obtain the conductivity change of any one of the L types of excitations by adjusting the weights in the mixed excitation conductivity change; Optionally, the adjustment of the weights is to adjust the weight of one excitation to 1 and the weights of other excitations to 0; optionally, when the mixed excitation is adjacent excitation and opposite excitation and the independent excitation is adjacent excitation, the conductivity change is: ； Optionally, when the mixed excitation is adjacent excitation and opposite excitation and the independent excitation is opposite excitation, the conductivity change is: 。 6. The electrical impedance tomography method based on hybrid excitation according to claim 1, wherein The regularization adopts one or more of the following: Tikhonov regularization, L1 regularization, mixed regularization; Optionally, the regularization matrix is calculated by point spread of a Gaussian distribution.

7. A method for generating EIT functional images based on hybrid excitation, characterized in that, Including: Obtaining the electrical impedance distribution imaging of the subject according to the electrical impedance tomography method based on mixed excitation described in claims 1-6; Obtaining the tidal EIT image of the subject; Performing parameter extraction on the electrical impedance distribution imaging and the tidal EIT image to obtain functional parameter data; Performing image reconstruction based on the functional parameter data to obtain an EIT functional image; Optionally, the parameters include one or more of the following: ventilation uniformity, ventilation time, left-right ventilation ratio, ventilation center, ratio of exhalation time to inhalation time, exhalation speed.

8. A computer program product, which includes a computer program or instructions thereon, characterized in that, The computer program or instruction, when executed by a processor, implements the hybrid-excitation-based electrical impedance tomography method according to any one of claims 1-6, or the hybrid-excitation-based EIT functional image generation method according to claim 7.

9. A computer device, comprising a memory, a processor, and a computer program or instruction stored on the memory, characterized in that The computer program or instruction, when executed by a processor, implements the hybrid-excitation-based electrical impedance tomography method according to any one of claims 1-6, or the hybrid-excitation-based EIT functional image generation method according to claim 7.

10. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, The computer program or instruction, when executed by a processor, implements the hybrid-excitation-based electrical impedance tomography method according to any one of claims 1-6, or the hybrid-excitation-based EIT functional image generation method according to claim 7.

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