72 results about "Impedance imaging" patented technology

The invention relates to a lung region electrical impedance tomography (EIT) image reconstruction method based on a graph structure, and establishes an EIT depth imaging model in which double branch channels of a high-frequency channel graph attention network and a low-frequency channel graph attention network are parallel, and the model is named as HLGAT. The method comprises the following steps: injecting current excitation into a finite element simulation model of a lung region to obtain boundary voltage data required by EIT image reconstruction, and performing non-uniform mesh generation on the lung region needing image reconstruction to obtain nodes required by a graph neural network and an adjacency relationship between the nodes, so as to obtain a lung region model; storing the data in a graph structure form suitable for network input; the HLGAT aggregates the low-frequency characteristic information and the high-frequency characteristic information through the two channels, node output characteristic information of the two channels is fused in a learnable mode, a new adjacency matrix is reconstructed according to the node characteristic information output by the network, then a new Laplace matrix is constructed, more accurate conductivity distribution can be obtained, and the accuracy of the conductivity distribution is improved. And the physical process of electrical impedance imaging can be described more accurately, so that the accuracy of model imaging is improved.

The invention relates to the technical field of material detection, in particular to a silver coating adhesive force detection method based on multi-frequency impedance imaging, which comprises the following steps: applying a dual-mode multi-frequency excitation signal to the surface of a silver coating through an attached microelectrode array, synchronously acquiring voltage-current response data, and generating a dual-mode multi-frequency impedance original data set; performing time-varying impedance phase-locked separation on the dual-mode multi-frequency impedance original data set, extracting a dynamic impedance component of a coating-matrix interface region, and reconstructing a three-dimensional sub-interface impedance distribution diagram; and outputting an adhesive force quantized value through an oscillation frequency-adhesive force conversion model according to the spatial oscillation frequency characteristics of the impedance phase in the three-dimensional sub-interface impedance distribution diagram. According to the invention, a double-phase-locked loop system is adopted to realize the time-varying component demodulation of the impedance response of the silver coating-matrix interface, the low-frequency dynamic impedance signal modulated by silver ion migration is effectively extracted, and the weak change of the interface bonding state is sensitively reflected.

The invention relates to an electrical impedance detection method and device, an electronic device and a storage medium, which are applied to the field of electrical impedance imaging, and the electrical impedance detection method comprises the following steps: obtaining an electrode signal of a target electrode in a measurement area; a plurality of preset electrodes are attached to the measurement area; the target electrode comprises any one of a plurality of preset electrodes; the electrode signal carries excitation electrode data and measurement electrode data generated by the target electrode; determining a fluctuation value of the target electrode according to the excitation electrode data and the measurement electrode data; when the fluctuation value is within a preset fluctuation threshold range, based on a preset electrode measurement method, electrode contact impedance of the measurement area is obtained; and generating an electrode adjustment result based on the electrode contact impedance and sending the electrode adjustment result to the client. According to the method and the device, the problem that the efficiency of electrical impedance measurement is relatively low due to the fact that whether the electrode falls off or is in poor contact cannot be quickly judged during bioelectrical impedance imaging is solved.

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.

The invention discloses an electrical impedance tomography method, which integrates a physical model and deep learning to construct a deep learning framework with physical constraints so as to improve physical consistency and reconstruction precision. According to the method, a neural network based on an encoder-decoder structure is adopted, and measured boundary voltage is used as input to predict conductivity distribution of a target object; in order to improve the performance of the neural network, a physical Jacobian matrix is introduced, and the sensitivity of boundary voltage to conductivity is described; and constructing a physical regularization loss function, comparing a physically calculated Jacobi matrix with a neural network predicted Jacobi matrix, and combining a final total loss function with data error loss and physical consistency loss. According to the method, data driving model training is guided through physical constraints, combination of physical information and deep learning is achieved, the precision of electrical impedance reconstruction and the generalization ability of the model are remarkably improved, and a new solution thought is provided for the fields of medicine, flexible electronics, environment monitoring and the like.

The invention relates to the technical field of medical instruments, and provides a radio frequency gold microneedle control system based on subcutaneous impedance identification, and a radio frequency gold microneedle is provided with an electrode array for impedance tomography. Comprising the following steps: an impedance distribution reconstruction module generates reconstructed impedance distribution from a voltage signal of subcutaneous tissue collected by an electrode array through an impedance distribution reconstruction model based on a genetic algorithm; the impedance imaging module generates an impedance distribution image for reflecting subcutaneous tissue impedance distribution from the reconstructed impedance distribution through an impedance rate segmentation model based on a UNet architecture; the depth control module determines the recommended depth of the radio frequency gold microneedle according to the impedance distribution image; and the radio frequency energy control module adjusts control parameters of a control algorithm for controlling the radio frequency energy of the radio frequency gold microneedle according to the plurality of impedance distribution images. Accurate segmentation judgment and better imaging effect on impedance distribution are achieved, and then impedance imaging is applied to depth and energy pulse control of the radio frequency gold microneedle.

The present invention discloses a method for detecting cancer by bioelectrical impedance based on deep learning, comprising the following steps: S1: obtaining the electrical signal data of benign and malignant samples of cancer tissues by using an electrical impedance imaging system and combining it with physiological data; S2: extracting global features of the electrical signals by using a matrix decomposition module; S3: taking the result of global feature extraction and the original electrical signal data together as new inputs and further extracting signal features by using data dependency; S4: integrating the extracted feature maps into a final classification result. The present invention uses an electrical impedance imaging system to obtain the electrical signal data of cancer tissues, performs global feature extraction through a matrix decomposition module, and at the same time takes the result of global feature extraction and the original electrical signal data together as new inputs, and further extracts signal features by using data dependency, which can improve the utilization rate of data and further enhance the accuracy and reliability of cancer detection.

A physical neural network high-resolution electrical impedance tomography optimization method belongs to the field of electrical impedance tomography and comprises the following steps: firstly, acquiring an initial image of a to-be-tested part of a human body by using an electrical impedance tomography (EIT) technology; secondly, performing preliminary denoising processing on the initial image through a three-dimensional block matching algorithm BM3D; and finally, on the basis of the non-local similarity in the prior information of the natural image, introducing the prior information of the CT image as a constraint condition, adding the prior information into the physical neural network PINNs, and continuously iterating and optimizing to realize high-resolution reconstruction of the image, thereby achieving the purpose of improving the image quality. The EIT imaging resolution is high, and the imaging result is more reliable.

The invention belongs to the technical field of respiratory therapy equipment control, and discloses a multi-band airway vibration sputum excretion adaptive optimization system, which obtains acoustic impedance distribution data of each micro-section of the inner wall of the airway of a patient through an acoustic impedance imaging technology, accurately recognizes an abnormal region of a tissue interface, and improves the accuracy of sputum excretion. And calculating a reflection enhancement coefficient and a transmission attenuation coefficient of the vibration wave in the areas. And based on the multi-path interference model and the sputum excretion demand priority matrix, a multi-band vibration wave composite field with phase modulation characteristics is generated, and effective compensation of the energy transfer efficiency of the abnormal region of the tissue interface is realized. A closed-loop feedback control system is formed by monitoring spectrum characteristics of pressure pulsation signals in an airway in real time, identifying characteristic harmonic peaks and dynamically adjusting vibration wave parameters according to the characteristic harmonic peaks. The sputum excretion parameters can be optimized in real time according to individual differences of patients and sputum characteristics, the sputum excretion efficiency is remarkably improved, and discomfort of the patients is relieved.

The invention relates to an electrical impedance tomography data processing method and device and a storage medium. The impedance imaging data processing method comprises the following steps: acquiring a plurality of channel data of a plurality of electrodes in a measurement area; calculating a signal disturbance comprehensive index based on the channel data; under the condition that the data state is determined to be an abnormal state based on the signal disturbance comprehensive index, filtering and baseline correction are carried out on the channel data based on the signal disturbance comprehensive index to obtain optimized data; obtaining tissue transmission impedance based on the channel data; on the basis of tissue transmission impedance, respiratory impedance physiological characteristic indexes are obtained; determining a mutation frame in the optimization data based on the respiratory impedance physiological characteristic index; and obtaining valid data based on the mutation frame. According to the invention, the problem of low data validity in the electrical impedance imaging system is solved.

The invention relates to a magnetic resonance passive high-impedance imaging gain system, a transmitting coil is used for transmitting a magnetic resonance signal to a to-be-imaged target part, and a pickup coil is used for receiving an electromagnetic signal returned by the to-be-imaged target part. A passive high-impedance gain coil is arranged at the position of a target to be imaged, the single-channel high-impedance coil is connected with the resonance circuit and the detuning circuit in parallel, and the single-channel high-impedance coil is used for limiting flowing of internal current. When a magnetic resonance signal is received, the resonance circuit and the detuning circuit operate at the same time, and under the action of the detuning circuit, the passive high-impedance gain coil does not affect the magnetic resonance signal at the moment; when the electromagnetic signal is received, only the resonance circuit operates in the circuit, and due to the effect of the resonance circuit, the passive high-impedance gain coil is in a resonance state at the moment, so that the effect of enhancing the electromagnetic signal is achieved. According to the magnetic resonance passive high-impedance imaging gain system disclosed by the invention, the purpose of improving the imaging quality requirement can be achieved.

The present application relates to a kind of electrode belt and electrode assembly for electrical impedance imaging, it is related to biomedical electrical impedance imaging technical field.The electrode belt for electrical impedance imaging of the present application, including band body, the band body includes multiple electrode segments arranged along length, and elastic segment between adjacent electrode segments, the electrode segment can be used to set electrode assembly, the thickness of the electrode segment is greater than the thickness of the elastic segment, so that the elastic segment is more likely to occur elastic deformation compared with the electrode segment.Band body can be more conveniently fixed around the body of the object to be measured, and the fixing mode is simple, can make electrode and the surface of the object to be measured always keep good contact.

The invention discloses a bioelectrical impedance cancer detection method based on deep learning, and the method comprises the following steps: S1, obtaining electrical signal data of a benign sample and a malignant sample of a cancer tissue through an electrical impedance imaging system, and combining the electrical signal data with physiological data; s2, performing global feature extraction on the electric signal by using a matrix decomposition module; s3, taking a global feature extraction result and original electric signal data as new input, and further extracting signal features by using a data dependency relationship; and S4, mapping and integrating the extracted features into a final classification result. The electrical signal data of the cancer tissue is acquired by using the electrical impedance imaging system, global feature extraction is performed through the matrix decomposition module, the result of global feature extraction and the original electrical signal data are used as new input, and the signal features are further extracted by using the data dependency relationship, so that the utilization rate of the data can be improved; and the accuracy and reliability of cancer detection are further improved.

The utility model relates to the technical field of electrical impedance imaging, in particular to an electrical impedance imaging signal acquisition circuit and an acquisition device, the electrical impedance imaging signal acquisition circuit comprises an excitation signal generation circuit and a measurement signal detection circuit, a first voltage follower circuit is arranged in the measurement signal detection circuit in series, and a second voltage follower circuit is arranged in the measurement signal detection circuit. The first voltage following circuit can increase the input impedance of the measurement signal detection circuit, so that the measurement signal detection circuit is equivalent to an ideal voltmeter, and almost does not draw current from a motor of the excitation signal generation circuit, thereby enabling the voltage of a motor signal not to be remarkably changed, reducing the influence on the motor signal, and improving the reliability of the motor. And the authenticity and integrity of the measurement signal are ensured.

The invention provides a floating photovoltaic power station grounding resistor fault positioning method and device, the method is applied to a controller in a fault positioning system, and the fault positioning system further comprises a grounding grid of a floating photovoltaic power station, a sensing network formed by deploying sensors at key nodes of the grounding grid, and a signal generator. The method comprises the following steps: in response to a received fault positioning instruction, triggering a signal generator to inject a high-frequency test current signal into a grounding grid; acquiring a current-voltage data set through a voltage response signal of each key node acquired by a sensing network; based on the current-voltage data set, resistivity distribution reconstruction is carried out by adopting an impedance imaging algorithm, and a three-dimensional resistivity distribution diagram of the grounding grid is generated; and according to the three-dimensional resistivity distribution diagram, positioning an abnormal region with a resistance value exceeding a preset threshold value in the grounding grid. According to the method, three-dimensional visual imaging of resistivity distribution in the grounding grid and accurate positioning of an abnormal area are realized.

The invention discloses a meat quality detection method and a meat quality detection system based on flexible biological impedance imaging. The meat quality detection method comprises the following steps: acquiring impedance original values, PH, chromatic aberration and tenderness of a to-be-detected meat sample measured according to a preset measurement principle at a plurality of time points; correcting the impedance original value of the to-be-detected meat sample by using the correction coefficient; reconstructing the corrected impedance original value to obtain a reconstructed impedance value; obtaining the time point of meat deterioration according to the PH, color difference and tenderness at different time points; obtaining the average impedance value of all the reconstructed impedance values of the meat sample to be measured at the deterioration time point and the initial time point, calculating the impedance change rate of the meat sample to be measured at the deterioration time point and the initial measurement time point by using the reconstructed average impedance value, and comparing the impedance change rate with the pre-calculated impedance change rate of the frozen meat sample, if the impedance change rate of the to-be-detected meat sample is greater than the freezing impedance change rate, the to-be-detected meat sample is an unfrozen fish, otherwise, the to-be-detected meat sample is an iced fish. The method is simple in step, easy to detect and accurate in detection result.

The invention discloses a cross-hole sound wave reflection imaging method, and relates to the field of geotechnical engineering reconnaissance, and the method comprises the steps: employing a coding sound wave emission assembly to generate a group of coding sound wave signals with different frequencies, and transmitting the coding sound wave signals to the surrounding stratum of an emission drill hole; a coding sound wave receiving assembly is adopted to receive direct transmission wave signals of all coding sound waves transmitted to a receiving drill hole through a stratum and all coding reflection wave signals reflected by geologic bodies in the stratum, corresponding coding electric signals are generated through induction, and the coding electric signals are converted into coding sound wave digital signals through a coding sound wave transmitting assembly; the transmission sound wave signal and the reflection sound wave signal are analyzed through data analysis processing software; and if the total number of times of coded sound wave detection reaches the preset number of times, determining a three-dimensional formation wave impedance imaging graph of the formation outside the connecting line between the transmitting drill hole and the receiving drill hole according to the time and the amplitude in the reflected sound wave signal corresponding to any frequency. According to the invention, large-span and high-resolution reflection imaging can be realized.

This invention discloses a method for measuring cranial thickness based on bioelectrical impedance analysis (BIA), comprising: dividing the target region into subdivisions using the finite element method (FEM) to establish a finite element model; acquiring surface voltage signals of the skull using a four-electrode method on a brain electrical impedance imaging platform; obtaining the conductivity distribution of each node of the triangular element by solving the inverse voltage problem; inputting the conductivity distribution into the finite element model and calculating the distance between the node with the largest resistivity change and the corresponding boundary voltage node by solving the forward problem, thus obtaining the skull width at a set of signal locations; measuring the node voltages and calculating the corresponding skull thickness using a sixteen-electrode cyclic excitation method; and finally, assembling all node voltages and skull thicknesses into a plane to obtain the overall skull thickness. This invention designs a method for measuring cranial thickness based on bioelectrical impedance analysis (BIA), providing an accurate method for calculating cranial thickness.

The invention belongs to the technical field of medical instruments, and particularly relates to a control system and method for guiding a monitoring sickbed to turn over based on pulmonary electrical impedance tomography, and the system comprises EIT equipment, an EIT information collection unit, an automatic turning-over parameter determination unit, a control unit and a monitoring sickbed; the EIT information acquisition unit acquires lung ventilation or perfusion state data provided by EIT equipment in real time; the automatic turn-over parameter determination unit compares the data with a preset threshold range; if the data is within the threshold range, the system keeps the original turning parameters; if the data exceed the threshold range, the system adjusts the turning parameters to achieve the positive treatment effect; the control unit instructs the monitoring sickbed to carry out corresponding body position change operation according to the adjusted turning-over parameters; by means of the mode, the system can dynamically adjust the body position of a patient, lung ventilation and perfusion are optimized, and then the breathing condition of the patient is improved; therefore, turning over of the sickbed is more accurate and effective, and the treatment effect of the patient can be improved.

The present invention belongs to the field of respiratory therapy equipment control technology. The present invention discloses a multi-band airway vibration expectoration adaptive optimization system. The acoustic impedance distribution data of each micro-segment of the patient's airway inner wall is obtained through acoustic impedance imaging technology, the abnormal tissue interface area is accurately identified, and the reflection enhancement coefficient and transmission attenuation coefficient of the vibration wave in these areas are calculated. Based on the multi-path interference model and the expectoration demand priority matrix, a multi-band vibration wave composite field with phase modulation characteristics is generated to achieve effective compensation for the energy transfer efficiency of the abnormal tissue interface area. By real-time monitoring of the spectral characteristics of the pressure pulsation signal in the airway, the characteristic resonance peak is identified and the vibration wave parameters are dynamically adjusted accordingly to form a closed-loop feedback control system. The present invention can optimize the expectoration parameters in real time according to the individual differences of the patients and the characteristics of the sputum, significantly improve the expectoration efficiency, and reduce the discomfort of the patients.

The invention belongs to the technical field of medical rehabilitation assessment, and provides a urinary surgery postoperative wound rehabilitation assessment method based on artificial intelligence, electronic equipment and a storage medium. The method comprises the steps of multi-source data acquisition, data preprocessing, spatial alignment, normalization processing, three-branch network feature extraction, feature integration, feature compression, feature mapping and wound rehabilitation grade matching. Through three-mode objective data of microwave imaging, electrical impedance imaging and bioelectrical impedance, the whole data acquisition process is free of invasion and interference, and errors caused by individual differences are reduced; through a three-branch feature extraction network and a deep fusion process, different types of data characteristics are adapted, cross-modal information complementation is realized, and wound rehabilitation multi-dimensional key signals are captured.

The invention provides wearable non-invasive lung function monitoring equipment, system and device based on impedance imaging and a medium. The wearable non-invasive lung function monitoring equipment, system and device based on impedance imaging have the advantages that limitation of traditional equipment is overcome, complex operation of a patient is not needed, subjective interference is reduced, and imaging and function testing can be integrated to complete comprehensive lung function evaluation; 2, the device is highly portable and wearable, is extremely miniaturized and is suitable for multiple scenes, and the flexible sensor belt is comfortable and stable to wear; intelligence and interconnection are achieved, real-time remote medical treatment can be achieved through cloud data sharing, and the early diagnosis and management efficiency of diseases is improved in combination with the AI technology; the method can be applied to multiple scenes, and a high-value scheme is provided for chronic disease management, postoperative rehabilitation, health screening and the like; intelligent interaction and platform integration are supported, real-time online communication between doctors and patients is facilitated, and the hospital is assisted to improve diagnosis and treatment efficiency; and 6, personalized health management and diagnosis optimization are promoted, precise health prediction and intervention suggestions are given by depending on an AI algorithm, and a dynamic analysis tool is also provided to jointly manage the health trend and improve the treatment effect.

The invention relates to the technical field of biomedicine, and provides an electromagnetic imaging method, device, system and equipment and a medium, and the method comprises the steps: constructing an electrical impedance imaging model and an electromagnetic imaging model; generating a source domain data set by using the electrical impedance imaging model, and generating a target domain data set by using the electromagnetic imaging model; training a preset auxiliary model according to the source domain data set to obtain an improved auxiliary model; performing adaptation layer training and whole network fine tuning on the improved auxiliary model by using the target domain data set to obtain an electromagnetic imaging optimization model; and inputting to-be-tested data in the target domain data set into the electromagnetic imaging optimization model to output a reconstructed image of craniocerebral lesion signal distribution. By means of the scheme, the advantage that a large number of data sets are easy to generate during electrical impedance imaging model construction is utilized, under limited resources, the optimal balance between calculation time and imaging precision is achieved, and generalization of the model in similar problems is verified.

The invention relates to the technical field of electrical impedance imaging, and discloses a high-frame-rate electrical impedance imaging method and system based on sparse representation. The method comprises the steps of collecting original electrical impedance data, and performing preprocessing to obtain a training sample matrix; carrying out dictionary learning algorithm training by utilizing the training sample matrix to obtain an optimized dictionary matrix; setting a mask matrix for electrical impedance data acquisition, and performing sparse sampling on the original electrical impedance data according to the mask matrix to obtain incomplete data frames; and carrying out sparse representation on the incomplete data frame by using the optimized dictionary matrix, solving a corresponding sparse vector, and recovering the original electrical impedance data by using the dictionary matrix and the sparse vector. The method can effectively improve the system frame rate, is suitable for different electrical impedance imaging platforms, and does not depend on specific hardware.

A two-level foreign matter detection system (Figure 2) is used to detect foreign matters (1004, 1104) that affect a ground transceiver component (GTA) (406) of a wireless power transfer (WPT) station (Figure 4). In a first stage, at least one camera device (201, 202) is positioned to observe a charging location of the GTA and an area around the charging location. In a second stage, an impedance measurement circuit (Fig. 12) measures an impedance of a wireless power transfer coil of the GTA. An imaging processor (204) analyzes the image from the at least one camera device to identify a change or feature indicative of introduction of foreign matter on the exposed surface of the GTA or in an area around the charging location. A foreign object detection (FOD) controller (205) triggers an impedance check of the GTA by the impedance measurement circuit and initiates a fail-safe operation upon detecting object movement to cross-border into or beyond the charging position.

The utility model relates to an electrode band which is used for application scenes such as human body electrical impedance imaging needing a multi-point monitoring human body impedance imaging technology, due to the fact that an avoiding section is arranged between a first electrode set and a second electrode set, contact between electrodes and the spine is avoided through the design of the avoiding section; in this way, signal interference caused by poor attaching contact between the protruding uneven structures of the spine of the body and the electrodes is reduced, and the problem that signal transmission is unstable or insufficient in accuracy is solved. Moreover, the electrodes of the electrode bands protrude towards the side, making contact with the skin of the patient, of the flexible band body, so that stable contact with the skin is ensured, and the quality and stability of signal collection are further improved.

The present application relates to the field of electrical impedance imaging, and discloses a regional lung perfusion delay time analysis method, an electronic device and a storage medium. A dynamic image of conductivity change caused by blood perfusion in a human thoracic cavity is obtained. A blood perfusion image of a heart region and a blood perfusion image of a lung region are separated. A reference curve of conductivity change of the heart region is obtained according to the dynamic image of blood perfusion conductivity change and the blood perfusion image of the heart region. A conductivity change curve of each pixel point in the blood perfusion image of the lung region is obtained according to the dynamic image of blood perfusion conductivity change and the blood perfusion image of the lung region. The conductivity change curve is divided by a sliding time window, and a regional lung perfusion delay time is calculated in each time window. The time resolution advantage of electrical impedance imaging technology is fully utilized, and the time delay of the time when the local blood volume of the lung reaches the maximum relative to the end of the ventricular diastole is reflected by calculating the regional lung perfusion delay time.

The present invention relates to an electrical impedance imaging assembly (10), an electrical impedance imaging device, an electrical impedance imaging set, and a method for manufacturing the electrical impedance imaging assembly. The electrical impedance imaging assembly (10) includes at least one electrode patch (11, 21) externally applied to a patient. The electrode patch includes multiple electrodes (12, 22) for measuring the impedance of a target object located between the multiple electrodes (12, 22). The electrical impedance imaging assembly further includes a first interface (13) for transmitting electrical signals between the electrode patches (11, 21) and an external device (100). The external device calculates a feature of the target object, particularly a three-dimensional tomographic image (101) of the target object, based on the electrical signals from the electrodes (12, 22). At least the first electrode patch (11) includes at least 12 electrodes (12), preferably 16 to 32 electrodes, and the electrodes (12) are arranged in a two-dimensional pattern, preferably a matrix, on a substrate (14) of the first electrode patch. The first electrode patch (11) comprises an adhesive (15) for fixing the first electrode patch (11) to the patient's skin, and preferably the ratio of the area of ​​the adhesive surface (16) for contact with the skin to the area of ​​the total surface (17) of the electrode (12) facing the skin is between 0.5 and 2.

The present application relates to a kind of embedded carbon nanotube film fiber composite component damage resistance impedance imaging online accurate reconstruction method, comprising preparation including carbon nanotube film core sensing element composite component;Build acquisition platform including power supply, matrix switch, voltage acquisition instrument;The forward mathematical model of carbon nanotube film-fiber composite component considering conductivity orthogonal anisotropy is constructed;Calibrate the conductivity tensor of healthy component and contact impedance;Using one-step Gauss-Newton imaging algorithm, establish damage area priori;B-spline curve control point matrix is constructed, and damage area is mapped;Solve the jacobian matrix of voltage response to the change of control point weight, iteratively calculate control point weight, using line search to update damage area, until residual convergence.The present application can accurately reconstruct the specific position, area size, geometric characteristics of composite material structure damage, significantly reduce imaging artifact, provide technical guidance for composite material structure health monitoring.

The present invention relates to a magnetic resonance passive high-impedance imaging gain system. A transmitting coil is used to transmit a magnetic resonance signal to a target site to be imaged, and a receiving coil is used to receive an electromagnetic signal returned from the target site to be imaged. At a target location to be imaged, a passive high-impedance gain coil is disposed, a single-channel high-impedance coil being in parallel connection with a resonant circuit and a detuning circuit, and the single-channel high-impedance coil being used to limit the flow of internal current. When a magnetic resonance signal is received, both the resonant circuit and the detuning circuit operate simultaneously. Due to the effect of the detuning circuit, the passive high-impedance gain coil does not affect the magnetic resonance signal at this time. Meanwhile, when an electromagnetic signal is received, only the resonant circuit operates in the circuit. Under the action of the resonant circuit, the passive high-impedance gain coil enters a resonant state, thereby enhancing the electromagnetic signal. The magnetic resonance passive high-impedance imaging gain system disclosed in the present application can achieve the objective of improving imaging quality.