CN116712057A - Pulmonary perfusion imaging method, system and equipment for shielding respiratory impedance change - Google Patents
Pulmonary perfusion imaging method, system and equipment for shielding respiratory impedance change Download PDFInfo
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/05—Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves
- A61B5/053—Measuring electrical impedance or conductance of a portion of the body
- A61B5/0536—Impedance imaging, e.g. by tomography
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7271—Specific aspects of physiological measurement analysis
- A61B5/7285—Specific aspects of physiological measurement analysis for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal
Abstract
The application relates to a lung perfusion imaging method, a system and equipment for shielding respiratory impedance change, and relates to the field of intelligent medical treatment, in particular to a method for acquiring chest resistance signal data of a patient, wherein the chest resistance signal data is edge voltage data acquired when excitation current is set to a frequency which minimizes the impedance difference between end expiration and end inspiration; a lung perfusion image is reconstructed by a regularization method based on the boundary voltage data. According to the application, by increasing the frequency of exciting current, the pulmonary perfusion imaging of the saline radiography is realized when the breath is not shielded, the disturbance of respiratory cycle caused by breath shielding is avoided, more importantly, the breath shielding is difficult to be finished when a patient breathes spontaneously, and the implementation success rate of the breath shielding saline radiography is limited, because the method has important response value.
Description
Technical Field
The application relates to the field of intelligent medical treatment, in particular to a lung perfusion imaging method, a system, equipment and a computer readable storage medium for shielding respiratory impedance change.
Background
EIT is used as a bedside non-invasive, continuous, real-time, non-radiative imaging technique, primarily for pulmonary ventilation and pulmonary perfusion (blood flow) monitoring. EIT pulmonary perfusion imaging usually adopts a hypertonic saline radiography method, high-conductivity contrast agent (10 ml,10% physiological saline) is injected from central vein through a 'bolus', changes of thoracic resistance signals are collected, and a local resistance-time change curve of saline radiography is established to reflect regional pulmonary perfusion conditions. To reduce respiratory disturbance to the resistance, it is desirable to implement during apnea (breath-hold) where the total chest resistance remains relatively constant, better reflecting the effects of saline contrast.
At present, bedside pulmonary vascular perfusion imaging based on Electrical Impedance Technology (EIT) has certain applications in the field of medical imaging, such as the following studies: document 1: "CN111449657A", a bedside based on saline radiography pulmonary ventilation-blood flow perfusion electrical impedance tomography method, has established image monitoring device, image monitoring system and pulmonary embolism diagnostic system, has improved blood flow perfusion imaging quality. Document 2: "CN114723844A", a method, system and apparatus for reconstructing a pulsatile perfusion image corrected by saline contrast, combining a pulmonary vessel pulsatile method and a hypertonic saline contrast method, and realizing real-time and accurate pulmonary perfusion imaging by using a pulsatile map and generated correction factors. Document 3: "CN115035208A", a lung perfusion and region V/Q noninvasive imaging method, system, apparatus and computer-readable storage medium, for introducing blood flow impedance data in a saline contrast into a beat map by a correction factor, generating a saline contrast corrected beat perfusion image, and constructing a lung ventilation/blood flow map in combination with a patient lung ventilation map and the saline contrast corrected beat perfusion image.
Disclosure of Invention
For EIT lung perfusion imaging, the following problems exist:
1) During saline injection, respiration can cause changes in thoracic impedance, causing signal interference.
2) The existing method requires the patient to hold breath for a minimum of more than 8 seconds, which can cause hypoxia of different degrees, even hypoxia, damage vital organs and endanger the life of the patient.
Based on the above two-point technical requirements, the inventor team proposes: by increasing the frequency of exciting current, the impedance change caused by respiration is shielded, and the lung perfusion imaging is realized.
The application creatively provides a lung perfusion imaging method for shielding respiratory impedance changes, which comprises the following steps:
acquiring chest resistance signal data of a patient, wherein the chest resistance signal data is boundary voltage data acquired when the exciting current frequency is set to be the frequency which minimizes the impedance difference between the end expiration and the end inspiration;
reconstructing a lung perfusion image based on the boundary voltage data.
Reconstructing a lung perfusion image based on the boundary voltage data is in effect: the voltage caused on the body surface is measured by injecting a known current into a specific part of the human body, the impedance distribution of each tissue and organ in the human body under the action of an electric field is calculated according to a certain reconstruction algorithm by utilizing the measured current and voltage value, and the tomography is generated by utilizing a computer. The reconstruction algorithm can also be performed by complex mathematical algorithms, including linear back projection (linear back projection, LBP) methods, singular value decomposition, newton-Raphsom algorithms, tikhonov iterative algorithms, neural network deep learning, and the like.
Further, a lung perfusion image is reconstructed by a regularization method based on the boundary voltage data.
Further, the method for acquiring the frequency with which the end-expiration and end-inspiration impedance difference is minimum comprises the steps of:
establishing an electrode sensor simulation model to simulate the chest of a human body, wherein an imaging target object is arranged in the chest and is used for simulating lung respiration;
setting a finite element solver as frequency, taking N frequency points in a logarithmic form, adopting adjacent excitation and adjacent measurement modes to respectively calculate the sum of absolute values of boundary voltages of end expiration and end inspiration under the N frequency points, drawing a thoracic impedance relative variation curve of end expiration and end inspiration along with the frequency variation, and obtaining the frequency with the minimum end expiration and end inspiration impedance difference, wherein N is a natural number integer.
Preferably, the frequency at which the end-tidal and end-inspiratory impedance differences are minimized is 100MHz.
Further, the regularization method in reconstructing the lung perfusion image based on the boundary voltage data through the regularization method solves the following formula:
ΔV m ＝V m,1 -V m,0
wherein DeltaV m Representing the difference between the end-inspiration and end-expiration boundary voltage matrices at the mth frequency point, V m,1 Represents the end-of-inspiration boundary voltage matrix, V m,0 End-tidal boundary voltage matrix, m=1:51, argmin represents a function parameterized by the function,representing the square of the two norms, R (Δσ) represents the regularization term, λ represents the regularization coefficient, and S represents the sensitivity matrix.
Further, N array electrodes are attached to the chest surface of a patient, a high-conductivity contrast agent is injected from a central vein through a 'bolus', the patient keeps normal respiratory rate, and then chest resistance signal data of the patient are obtained, wherein N is a natural number integer greater than or equal to 2.
Further, the collected boundary voltage data is the boundary voltage data collected by adopting the adjacent excitation and adjacent measurement modes.
It is an object of the present application to provide a pulmonary perfusion imaging device that shields changes in respiratory impedance, the device comprising a memory and a processor,
the memory is used for storing program instructions; the processor is configured to invoke the program instructions, which when executed, perform the lung perfusion imaging method described above that masks changes in respiratory impedance.
Further, the device comprises N array electrodes, wherein the N array electrodes are attached to the surface of the chest of a patient, the high-conductivity contrast agent is injected from the central vein through a 'bolus', the normal respiratory rate of the patient is kept, and then chest resistance signal data of the patient are obtained, wherein N is a natural number integer greater than or equal to 2.
The present application aims to provide a lung perfusion imaging system shielding respiratory impedance changes, comprising a computer program which, when executed by a processor, implements the above-mentioned lung perfusion imaging method shielding respiratory impedance changes.
It is an object of the present application to provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above-mentioned lung perfusion imaging method of shielding respiratory impedance variations.
The application has the beneficial effects that: the application provides a novel imaging method for shielding impedance change caused by respiration, which realizes lung perfusion imaging by increasing the frequency of exciting current, and avoids the problem that patients are damaged by hypoxia or even anoxia of different degrees possibly caused by long-time breath-holding of the patients, thereby endangering the lives of the patients.
Drawings
FIG. 1 is a schematic diagram of a lung perfusion imaging method for shielding respiratory impedance changes according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a lung perfusion imaging device that masks respiratory impedance changes, according to an embodiment of the present application;
FIG. 3 is a diagram of a simulation model of a 16-electrode sensor for simulating pulmonary breathing according to an embodiment of the present application;
FIG. 4 is a simulated human chest diagram of a 16-electrode sensor simulation model for simulating pulmonary breathing provided by an embodiment of the application;
FIG. 5 is a graph of the relative change in global impedance with electric field frequency for end-tidal and end-tidal lungs provided by an embodiment of the present application;
FIG. 6 is a graph of differential imaging of end-tidal and end-tidal lungs at 6 frequencies provided by an embodiment of the present application;
FIG. 7 is a diagram of a simulation model of a 16-electrode sensor for simulating lung breathing and lung perfusion, provided by an embodiment of the application;
fig. 8 is a graph of the imaging effect of lung respiration and lung perfusion at 100kHz and 100MHz frequency provided by an embodiment of the present application.
Detailed Description
In order to enable those skilled in the art to better understand the present application, the following description will make clear and complete descriptions of the technical solutions according to the embodiments of the present application with reference to the accompanying drawings.
In some of the flows described in the specification and claims of the present application and in the foregoing figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, with the order of operations such as 101, 102, etc., being merely used to distinguish between the various operations, the order of the operations themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments according to the application without any creative effort, are within the protection scope of the application.
Fig. 1 is a flowchart of a lung perfusion imaging method for shielding respiratory impedance changes according to an embodiment of the present application, specifically, the method includes the following steps:
101: acquiring chest resistance signal data of a patient, wherein the chest resistance signal data is boundary voltage data acquired when the exciting current frequency is set to be the frequency which minimizes the impedance difference between the end expiration and the end inspiration;
102: reconstructing a lung perfusion image based on the boundary voltage data.
In one embodiment, a method for obtaining a frequency at which a difference in end-tidal and end-inspiratory impedance is minimal includes: establishing an electrode sensor simulation model to simulate the chest of a human body, wherein an imaging target object is arranged in the chest and is used for simulating lung respiration; setting a finite element solver as frequency, taking N frequency points in a logarithmic form, adopting adjacent excitation and adjacent measurement modes to respectively calculate the sum of absolute values of boundary voltages of end expiration and end inspiration under the N frequency points, drawing a thoracic impedance relative variation curve of end expiration and end inspiration along with the frequency variation, and obtaining the frequency with the minimum end expiration and end inspiration impedance difference, wherein N is a natural number integer. Preferably, the frequency at which the end-tidal and end-inspiratory impedance differences are minimized is 100MHz.
In one embodiment, N array electrodes are attached to the surface of the chest of a patient, a high-conductivity contrast agent is injected from the central vein through a "bolus", the patient maintains a normal respiratory rate, and then chest resistance signal data of the patient is acquired, wherein N is a natural number integer greater than or equal to 2.
In one embodiment, firstly, an equivalent circuit model of the lung of a human body is constructed (shown in fig. 3), wherein blood, fat, air and the like are equivalent to electric resistance in terms of electric characteristics, and the size of the heart and the chest, the lung size and the distance are equivalent to capacitance; when the whole thoracic cavity is measured, the lung, the heart and other parts are equivalent to the parallel connection of a capacitor and a resistor, and then are connected in series;
the global impedance Z can be expressed as:
wherein R is L1 Representing the resistance of the right lung, C L1 Representing the capacitance of the right lung, R L2 Representing the resistance of the left lung, C L2 Representing the capacitance of the left lung, R H Representing the resistance of the heart, C H Representing the capacitance of the heart, R O Representing the resistance of other tissues of the thoracic cavity, C O Representing the capacitance of the tissue in the other parts of the chest,ω i representing the frequency of the electric field.
It can be seen that with the electric field frequency omega i The global impedance Z will drop with this rise.
Numerical analysis is carried out on the chest relative impedance change by adopting a finite element method, and an EIT positive problem mathematical model can be expressed as follows:
where (x, y) denotes the coordinates of the two-dimensional imaging plane point, E (x, y) denotes the electric field strength, u (x, y) denotes the electric potential, σ (x, y) denotes the electrical conductivity, Ω denotes the measurement field,represents the gradient, L represents the number of electrodes, Γ l Represents the electrode boundary, electrode number L e {1,2,.. l Represents the current injected from the electrode l, n represents the normal direction along the boundary, s represents the surface of the electrode, n·s=0;
the sensitivity matrix S is defined as the partial derivative of the potential with respect to the conductivity:
wherein I represents the excitation current, I represents the ith finite element of the field, U j Represents the j-th independent measurement potential, u and v represent the potential distribution of the measurement field when the excitation electrode pair and the measurement electrode pair are used as current injection electrodes, respectively, S ij ∈S；
Let the electrical conductivity at the end of the lung be sigma 0 The conductivity of the end of inspiration is sigma 1 The respiration-induced conductivity change Δσ can be expressed as:
Δσ＝σ 1 -σ 0
the change matrix deltav of the boundary voltage can be calculated by EIT positive problem:
ΔV＝SΔσ
as can be seen from ohm's law i=u/R, the voltage changes in proportion to the changes in impedance, so that the change in boundary voltage calculated from the EIT positive problem can be used to reflect the relative changes in thoracic impedance.
With reference to fig. 4, a 16-electrode sensor simulation model is established to simulate the chest of a human body, and a circle is built in as an imaging target object for simulating lung respiration; setting a field material (comprising conductivity and dielectric constant) as fat, and setting a target material as properties of lung end-expiration and lung end-inspiration respectively for lung ventilation differential imaging;
the finite element solver is set to be frequency, 51 frequency points are obtained in a logarithmic form from 10Hz to 100GHz, the sum of absolute values of boundary voltages of end expiration and end inspiration under the 51 frequency points is calculated respectively in a mode of adjacent excitation and adjacent measurement, the sum represents the relative variation of thoracic impedance, and each group of boundary voltages comprises 208 voltages. Referring to fig. 5, a chest impedance relative variation curve of end-expiration and end-inspiration with frequency is drawn to find out the frequency f with minimum end-expiration and end-inspiration impedance difference v (here 100 MHz) value.
Drawing a chest impedance relative change amount curve of end-expiration and end-inspiration along with the change of frequency according to the sum of the absolute values of the calculated boundary voltages, and finding out the frequency f with the minimum end-expiration and end-inspiration impedance difference v ；
Imaging a target object (single circle) by solving the EIT inverse problem at a single frequency;
the EIT inverse problem is solved by adopting a regularization method:
ΔV m ＝V m,1 -V m,0
wherein DeltaV m Representing the difference between the end-inspiration and end-expiration boundary voltage matrices at the mth frequency point, V m,1 Represents the end-of-inspiration boundary voltage matrix, V m,0 End-tidal boundary voltage matrix, m=1:51, argmin represents a function parameterized by the function,representing the square of a two-norm, R (delta sigma) representing the regularization term, lambda representing the regularization coefficient, S representing the sensitivity matrix;
differential imaging was performed on the targets at electric field frequencies of 1kHz,10kHz,100kHz,1MHz,10MHz, and 100MHz, respectively, and the results are shown in FIG. 6. EIT imaging is carried out on targets under different electric field frequencies, and the fact that the targets gradually disappear along with the increase of the frequency, and the images completely disappear under the electric field with the frequency of 100MHz shows that the thoracic impedance change caused by lung respiration under high frequency is shielded.
Referring to fig. 7, the targets (single circles) are replaced with double targets (two circles), one circle is used for simulating lung respiration, the other circle is used for simulating lung perfusion, and the field material is fat; the former moment, the two round materials (comprising conductivity and dielectric constant) are both set as the properties of the end of the lung expiration, and the latter moment, the target object for simulating lung respiration is set as the properties of the end of lung inspiration, and the target object material for simulating lung perfusion is set as physiological saline;
the finite element solver is set as frequency, two frequencies of 100kHz (pulmonary ventilation imaging frequency) and 100MHz are set, and the sum of absolute values of boundary voltages at two moments under two frequency points is calculated respectively by adopting adjacent excitation and adjacent measurement modes, wherein each group of boundary voltages comprises 208 voltage values;
solving an EIT inverse problem by adopting a regularization method, and performing differential imaging on a target object under two frequencies of 100kHz and 100 MHz; at an electric field frequency of 100kHz, both targets can be imaged, at an electric field frequency of 100MHz, the targets for simulating lung respiration disappear, and the targets for simulating lung perfusion can be imaged, and the result is shown in figure 8.
In one embodiment, 16 array electrodes are attached to the chest surface of the human body, high conductivity contrast medium (10 ml,10% saline) is injected from the central vein through "bolus" and the patient maintains normal breathing rate, and the excitation current is set to a frequency f that minimizes the end-tidal and end-inspiratory impedance differences v The boundary voltage data are acquired by adopting the adjacent excitation and adjacent measurement modes, and the image is reconstructed by a regularization method, at the moment, the imaging end only displays the image of blood flow, and the impedance change caused by lung respiration is shielded.
In one embodiment, 16 array electrodes are attached to the chest surface near the fourth and fifth intercostals of the human body and connected to the lung by relay cablesThe electrical impedance monitoring apparatus was prepared with 10% NaCl, 10ml, and it was confirmed that the patient had established a central venous catheter (either internal jugular vein or subclavian vein). Saline injection: typically requiring 2 operators to complete together, one of which is a command to confirm that the EIT machine is working properly, to issue a number of saline injections; after another operator gets the confirmation instruction, 10ml of 10% NaCl is quickly injected into the patient from the central venous catheter; the EIT monitor turns on recording mode during the entire operation, and the excitation current frequency is set to a frequency f that minimizes the end-tidal and end-inspiratory impedance differences v The method comprises the steps of continuously acquiring chest electrical impedance signal data 2 minutes before saline injection, acquiring boundary voltage data in a mode of adjacent excitation and adjacent measurement, wherein the whole process is required to last at least 2 minutes, completely recording the process of resistance reduction caused by saline injection, namely chest electrical impedance signal data of a patient, and reconstructing a lung perfusion EIT image based on the boundary voltage data.
Fig. 2 is a schematic diagram of a lung perfusion imaging device of the present application, which masks changes in respiratory impedance, the device including a memory and a processor,
the memory is used for storing program instructions; the processor is configured to invoke the program instructions, which when executed, perform a lung perfusion imaging method as described above that masks changes in respiratory impedance.
The application aims to disclose a lung perfusion imaging system shielding respiratory impedance changes, comprising a computer program which, when executed by a processor, implements a lung perfusion imaging method shielding respiratory impedance changes as described above.
The application aims to disclose a lung perfusion imaging system for shielding respiratory impedance changes, comprising:
an acquisition unit for acquiring chest resistance signal data of a patient, the chest resistance signal data being boundary voltage data acquired when an excitation current is set to a frequency that minimizes a difference between end-tidal and end-inspiratory impedances;
a reconstruction unit for reconstructing a lung perfusion image based on the boundary voltage data by a regularization method.
Definition:
electrical impedance imaging (Electrical Impedance Tomography, EIT) refers to the fact that when a certain current or voltage is applied to the surface of a human body, different impedance distributions in the body will measure different voltages or currents on the body surface. Therefore, the electrical impedance imaging technique is actually: the method comprises the steps of injecting known voltage into a specific part of a human body to measure current caused on the body surface, or injecting known current to measure voltage caused on the body surface, calculating impedance distribution of tissues and organs in the human body under the action of an electric field according to a certain reconstruction algorithm by using the measured current voltage value, and generating tomography by using a computer. Specifically, if body surface boundary voltage data is measured based on excitation current in the application, a lung perfusion EIT image is reconstructed according to the existing reconstruction algorithm.
TABLE 1 sign-physical meaning relation table of the present application
| (symbol) | Physical quantity | (symbol) | Physical quantity |
| R L1 | Resistance of right lung | C L1 | Capacitance of right lung |
| R L2 | Resistance of left lung | C L2 | Capacitance of left lung |
| R H | Resistance of heart | C H | Capacitance of heart |
| R O | Resistance of other parts of thoracic cavity | R H | Capacitance of other parts of thoracic cavity |
| ω i | Frequency of electric field | Z | Thoracic global impedance |
| E | Electric field strength | u | Electric potential |
| σ | Conductivity of | Ω | Measuring field |
| ▽ | Gradient of | L | Number of electrodes |
| Γ l | Electrode boundary | Γ | Adjacent toGap between two electrodes |
| I l | Current injected from electrode l | n | Along the normal direction of the boundary |
| s | Surface of electrode | S | Sensitive matrix |
| σ 0 | Conductivity of end-tidal lungs | σ 1 | Conductivity of the pulmonary end of inspiration |
| ΔV | Boundary voltage change matrix | λ | Regularization coefficient |
The results of the verification of the present verification embodiment show that assigning an inherent weight to an indication may moderately improve the performance of the present method relative to the default settings.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program to instruct related hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in implementing the methods of the above embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, where the storage medium may be a read only memory, a magnetic disk or optical disk, etc.
While the foregoing describes a computer device provided by the present application in detail, those skilled in the art will appreciate that the foregoing description is not meant to limit the application thereto, as long as the scope of the application is defined by the claims appended hereto.
Claims (10)
1. A lung perfusion imaging method of shielding respiratory impedance variations, comprising
Acquiring chest resistance signal data of a patient, wherein the chest resistance signal data is boundary voltage data acquired when the exciting current frequency is set to be the frequency which minimizes the impedance difference between the end expiration and the end inspiration;
reconstructing a lung perfusion image based on the boundary voltage data.
2. The method of claim 1, wherein the method of obtaining a frequency at which the end-tidal and end-inspiratory impedance differences are minimized comprises:
establishing N electrode sensor simulation models to simulate human chest, wherein an imaging target object is arranged in the chest and is used for simulating lung respiration;
setting a finite element solver as frequency, taking N frequency points in a logarithmic form, respectively calculating the sum of absolute values of boundary voltages of end expiration and end inspiration under the N frequency points, drawing a thoracic impedance relative variation curve of the end expiration and the end inspiration along with the frequency variation, and obtaining the frequency with the minimum end expiration and end inspiration impedance difference, wherein N is a natural number integer;
preferably, the frequency at which the end-tidal and end-inspiratory impedance differences are minimized is 100MHz.
3. The method of claim 2, wherein establishing the electrode sensor simulation model to simulate a human chest cavity comprises: establishing N electrode sensor simulation models to simulate human chest, wherein a circle is arranged in the chest as an imaging target object for simulating lung respiration; the field material is set to be fat, and the target material is set to be the attribute of the lung end-expiration and the lung end-inspiration respectively and is used for lung ventilation differential imaging.
4. The method for imaging lung perfusion with shielding respiratory impedance variation according to claim 1, wherein the reconstructing of the lung perfusion image based on the boundary voltage data is performed according to a reconstruction algorithm, calculating impedance distribution presented by each tissue and organ in the human body, and generating tomographic imaging by using a computer; preferably, the reconstruction algorithm comprises one or more of the following algorithms: the linear back projection method is used for singular value decomposition, and a Newton-Raphsom algorithm, a Tikhonov iterative algorithm and a neural network deep learning algorithm are used.
5. The lung perfusion imaging method of claim 1, wherein the reconstructing a lung perfusion image based on the boundary voltage data is reconstructing a lung perfusion image based on the boundary voltage data by a regularization method; preferably, the regularization method solves the formula as follows:
ΔV m ＝V m,1 -V m,0
wherein DeltaV m Representing the difference between the end-inspiration and end-expiration boundary voltage matrices at the mth frequency point, V m,1 Represents the end-of-inspiration boundary voltage matrix, V m,0 End-tidal boundary voltage matrix, m=1:51, argmin represents a function parameterized by the function,representing the square of the two norms, R (Δσ) represents the regularization term, λ represents the regularization coefficient, and S represents the sensitivity matrix.
6. The method for pulmonary perfusion imaging with shielding respiratory impedance variation according to claim 1, wherein N array electrodes are attached to the surface of the chest of the patient, a high-conductivity contrast agent is injected, the patient maintains a normal respiratory rate, and further chest resistance signal data of the patient is obtained, wherein the method comprises the steps ofWherein N is a natural number integer greater than or equal to 2; preferably, 16 array electrodes are attached to the chest surface near the fourth and fifth intercostals of the patient, the patient is connected to a pulmonary impedance monitor by a relay cable, 10% NaCl is prepared, 10% NaCl is injected after confirming that the patient has established a central venous catheter, the patient maintains normal respiratory rate, the EIT monitor is turned on for the recording mode during the whole operation period, and the excitation current frequency is set to a frequency f which minimizes the impedance difference between the end-expiration and end-inspiration v The method is characterized in that chest electrical impedance signal data are continuously collected before 10% NaCl is injected, boundary voltage data are collected in a mode of adjacent excitation and adjacent measurement, the whole process is required to last at least 2 minutes, and the process of resistance reduction caused by 10% NaCl injection, namely chest electrical resistance signal data of a patient, is completely recorded.
7. The method of claim 1, wherein the acquired boundary voltage data is acquired by means of adjacent excitation, adjacent measurement.
8. A lung perfusion imaging device that masks changes in respiratory impedance, the device comprising a memory and a processor, the memory for storing program instructions; the processor is configured to invoke program instructions which, when executed, perform the lung perfusion imaging method of any one of claims 1-7, which masks changes in respiratory impedance.
9. A respiratory impedance change-screening lung perfusion imaging system comprising a computer program, wherein the computer program when executed by a processor implements the respiratory impedance change-screening lung perfusion imaging method of any one of claims 1-7.
10. A computer readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the method of pulmonary perfusion imaging shielding respiratory impedance variations of any one of claims 1-7.
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