High-resolution magneto-acoustic-electric imaging processing method and system

Abstract

The invention provides a high-resolution magneto-acoustic-electric imaging processing method and system, and relates to the technical field of magneto-acoustic-electric imaging, and the method comprises the steps: carrying out the decoding and reconstruction of magneto-acoustic-electric imaging information, and obtaining a decoded and reconstructed magneto-acoustic-electric signal of a to-be-detected target body; constructing a pulse compression image of the to-be-detected area according to the conductivity gradient; performing matched filtering on the pulse compression image to obtain superposition density information during magneto-acoustic-electric imaging; determining an excitation conversion factor of the pulse excitation source during linear scanning imaging of the to-be-detected target, and determining coded pulse excitation during magneto-acoustic-electric imaging through the excitation conversion factor and the electric parameter image; and performing tomography processing on the magnetoacoustic-electric image during magnetoacoustic-electric imaging according to the superposition density information and the coded pulse excitation, and reconstructing the processed magnetoacoustic-electric image. According to the invention, the influence of the side lobe of the magnetoacoustic electric signal in magnetoacoustic electric imaging can be eliminated, so that the positioning precision of the to-be-detected target body in the boundary of the magnetoacoustic electric image is improved.

Description

Technical Field

[0001] The present application relates to the technical field of magnetoacoustic and electric imaging, and more specifically, to a high-resolution magnetoacoustic and electric imaging processing method and system. Background Art

[0002] Magnetoacoustic-electric imaging is an advanced imaging technology that combines the interaction of magnetic field, electric field and acoustic field, and is mainly used to obtain information about the electrical and mechanical properties inside an object. The principle is to use the coupling effect between magnetic field, electric field and acoustic field, detect the magnetoacoustic-electric signals generated by the external excitation source through specific sensors, and perform imaging and analysis on the target based on these signals. In this technology, the electromagnetic waves generated by the external pulse excitation source will trigger the acoustic wave and electrical signal response inside the target. Through the high-resolution sensor array, the system can collect echo signals from different positions, and then analyze the parameters such as the conductivity, dielectric properties and density inside the target. Magnetoacoustic-electric imaging technology has the advantages of high spatial resolution and non-invasiveness, and is widely used in medical imaging, material testing, underground detection and other fields.

[0003] Existing high-resolution magnetoacoustic-electric imaging processing mainly achieves accurate target imaging through multimodal signal processing and optimization algorithms. First, the system collects magnetoacoustic-electric signals reflected from the target area through a high-resolution magnetoacoustic-electric sensor array. Magnetoacoustic-electric signals usually include sound waves and electric field responses caused by electromagnetic waves, and these signals carry information about the electrical and physical properties inside the target. The sensor converts these complex signals into digital data through precise signal acquisition and amplification. Next, advanced decoding and reconstruction algorithms are used to decode the collected signals and extract magnetoacoustic-electric imaging features. These imaging features reflect the physical properties of the target area, such as conductivity and density. Subsequently, based on the conductivity gradient and imaging characteristics of the target, the system reconstructs the image through back projection, tomography and other techniques. During the image reconstruction process, noise suppression, signal enhancement and image optimization processing are also required to improve imaging accuracy and resolution. However, in the existing high-resolution magnetoacoustic and electrical imaging processing, there is signal attenuation, noise interference and inaccurate adjustment of imaging parameters during signal acquisition and image reconstruction, which makes it difficult to accurately extract detailed information of the target area during the imaging process, resulting in limited imaging quality, especially in complex environments and high-noise backgrounds. The influence of magnetoacoustic and electrical signal sidelobes will be generated. Therefore, how to eliminate the influence of magnetoacoustic and electrical signal sidelobes in magnetoacoustic and electrical imaging to improve the positioning accuracy of the boundary of the target object to be measured in the magnetoacoustic and electrical image is a problem faced by the industry. Summary of the invention

[0004] The present application provides a high-resolution magnetoacoustic-electric imaging processing method and system, which can eliminate the influence of magnetoacoustic-electric signal side lobes in magnetoacoustic-electric imaging to improve the positioning accuracy of the boundary of the target body to be measured in the magnetoacoustic-electric image.

[0005] In a first aspect, the present application provides a high-resolution magnetoacoustic-electric imaging processing method, the imaging processing method comprising the following steps:

[0006] When the high-resolution magnetoacoustic-electric imaging system is in working state, magnetoacoustic-electric imaging information of the target body to be measured in the magnetoacoustic-electric imaging scan is obtained;

[0007] Decoding and reconstructing the magnetoacoustic-electric imaging information to obtain the magnetoacoustic-electric signal of the target body to be measured after decoding and reconstruction; determining the electrical parameter image during magnetoacoustic-electric imaging according to the magnetoacoustic-electric signal;

[0008] Acquire the conductivity gradient of the high-resolution magnetoacoustic-electric imaging system during imaging, and construct a pulse compression image of the area to be measured based on the conductivity gradient; then perform matched filtering on the pulse compression image to obtain the stacking density information during magnetoacoustic-electric imaging;

[0009] Acquiring a pulse excitation source of a high-resolution magnetoacoustic-electric imaging system during imaging, determining an excitation conversion factor of the pulse excitation source during linear scanning imaging of the target to be measured, and then determining a coded pulse excitation during magnetoacoustic-electric imaging through the excitation conversion factor and the electrical parameter image;

[0010] The magnetoacoustic-electric image during magnetoacoustic-electric imaging is subjected to tomographic imaging processing according to the stacked density information and the coded pulse excitation, and the processed magnetoacoustic-electric image is reconstructed.

[0011] In this embodiment, the magnetoacoustic-electric imaging information refers to the comprehensive data of the acoustic wave signal, the electric signal and the electromagnetic wave signal generated by the target under the excitation of the external magnetic field in the magnetoacoustic-electric imaging system.

[0012] In this embodiment, decoding and reconstructing the magnetoacoustic and electrical imaging information to obtain the magnetoacoustic and electrical signals of the target object after decoding and reconstruction specifically includes:

[0013] Determining the magnetoacoustic-electrical imaging state of the target object to be measured before decoding and reconstruction according to the magnetoacoustic-electrical imaging information;

[0014] Decoding the magnetoacoustic-electric imaging state to obtain magnetoacoustic-electric imaging characteristics;

[0015] The magnetoacoustic-electrical signal of the target body to be measured after decoding and reconstruction is determined according to the magnetoacoustic-electrical imaging feature.

[0016] In this embodiment, determining the electrical parameter image during magnetoacoustic-electrical imaging according to the magnetoacoustic-electrical signal specifically includes:

[0017] Extracting the noise suppression amount during magnetoacoustic-electric imaging from the magnetoacoustic-electric signal;

[0018] The noise suppression amount is added to the magnetoacoustic-electrical image during magnetoacoustic-electrical imaging to obtain the electrical parameter image during magnetoacoustic-electrical imaging.

[0019] In this embodiment, the conductivity gradient refers to the rate of change of the conductivity distribution in the target body in space.

[0020] In this embodiment, the pulse compression image refers to an image processed by the pulse compression technology.

[0021] In this embodiment, matching filtering is performed on the pulse compression image to obtain the stacking density information during magnetoacoustic electroacoustic imaging, which specifically includes:

[0022] Determining imaging excitation factors during magnetoacoustic-electrical imaging according to the pulse compression image;

[0023] Determine the density of the imaging medium during magnetoacoustic-electrical imaging;

[0024] The superimposed density information during magnetoacoustic-electric imaging is determined according to the imaging excitation factor and the imaging medium density.

[0025] In this embodiment, it is characterized in that the superimposed density information represents the comprehensive physical density distribution of the target area.

[0026] In this embodiment, determining the excitation conversion factor of the pulse excitation source when the target to be measured performs linear scanning imaging specifically includes:

[0027] Obtaining imaging constraints when the pulse excitation source performs linear scanning imaging on the target to be measured;

[0028] An excitation conversion factor when the target to be measured performs linear scanning imaging is determined according to the imaging constraint amount.

[0029] In a second aspect, the present application provides a high-resolution magnetoacoustic-electric imaging processing system for executing a high-resolution magnetoacoustic-electric imaging processing method, wherein the imaging processing system comprises:

[0030] An acquisition module is used to obtain magnetoacoustic and electric imaging information of the target body to be measured during magnetoacoustic and electric imaging scanning when the high-resolution magnetoacoustic and electric imaging system is in working state;

[0031] A decoding module is used to decode and reconstruct the magnetoacoustic-electric imaging information to obtain the magnetoacoustic-electric signal of the target body to be measured after decoding and reconstruction; and to determine the electrical parameter image of the magnetoacoustic-electric imaging according to the magnetoacoustic-electric signal;

[0032] A matching module is used to obtain the conductivity gradient of the high-resolution magnetoacoustic-electric imaging system during imaging, and to construct a pulse compression image of the area to be measured based on the conductivity gradient; and then to perform matched filtering on the pulse compression image to obtain the stacking density information during magnetoacoustic-electric imaging;

[0033] A conversion module is used to collect the pulse excitation source of the high-resolution magnetoacoustic-electric imaging system during imaging, determine the excitation conversion factor of the pulse excitation source when the target to be measured is subjected to linear scanning imaging, and then determine the coded pulse excitation during magnetoacoustic-electric imaging through the excitation conversion factor and the electrical parameter image;

[0034] The tomography module is used to perform tomography processing on the magnetoacoustic-electric image during magnetoacoustic-electric imaging according to the stacked density information and the coded pulse excitation, and to reconstruct the processed magnetoacoustic-electric image.

[0035] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0036] When the high-resolution magnetoacoustic-electric imaging system is in working state, magnetoacoustic-electric imaging information of the target body to be measured in the magnetoacoustic-electric imaging scan is obtained; the magnetoacoustic-electric imaging information is decoded and reconstructed to obtain the magnetoacoustic-electric signal of the target body to be measured after decoding and reconstruction; the electrical parameter image during magnetoacoustic-electric imaging is determined according to the magnetoacoustic-electric signal; the conductivity gradient of the high-resolution magnetoacoustic-electric imaging system during imaging is obtained, and a pulse compression image of the area to be measured is constructed according to the conductivity gradient; then the pulse compression image is matched filtered to obtain the superposition density information during magnetoacoustic-electric imaging; the pulse excitation source of the high-resolution magnetoacoustic-electric imaging system during imaging is collected, and the excitation conversion factor of the pulse excitation source when the target body to be measured is linearly scanned for imaging is determined, and then the coded pulse excitation during magnetoacoustic-electric imaging is determined according to the excitation conversion factor and the electrical parameter image; the magnetoacoustic-electric image during magnetoacoustic-electric imaging is subjected to tomographic imaging processing according to the superposition density information and the coded pulse excitation, and the processed magnetoacoustic-electric image is reconstructed.

[0037] It can be seen that in the present application, the influence of the side lobes of the magnetoacoustic and electric signals in magnetoacoustic and electric imaging can be eliminated; wherein, by real-time acquisition of the magnetoacoustic and electric imaging signals of the target area, the electrical and physical property information of the target body is provided, ensuring that the accurate state and details of the target body can be reflected during the imaging process, and by decoding and reconstruction technology, key information in the magnetoacoustic and electric signals, such as conductivity and density characteristics, can be effectively extracted, providing accurate electrical parameters for subsequent image analysis, thereby improving the accuracy and quality of the imaging results; by analyzing the magnetoacoustic and electric signals, an electrical parameter image of the target area, such as a distribution map of conductivity and dielectric properties, can be generated, thereby improving the target recognition and quantitative analysis capabilities during the imaging process, especially in complex materials and high-noise environments; by analyzing the conductivity gradient, a more accurate pulse compression image can be obtained, the signal focusing and target differentiation during the imaging process are optimized, the imaging resolution is improved and the error is reduced, and it is particularly suitable for application scenarios with high-precision requirements; by tomographic imaging and image reconstruction technology, combined with superimposed density information and coded pulse excitation, the spatial resolution and accuracy of the image are significantly improved, the signal attenuation and noise interference in the traditional method are effectively overcome, high-quality target imaging is achieved, and the image clarity and detail restoration capabilities are improved.

[0038] In summary, the technical solution adopted in the present application can eliminate the influence of the side lobes of the magnetoacoustic-electric signal in magnetoacoustic-electric imaging, so as to improve the positioning accuracy of the boundary of the target body to be measured in the magnetoacoustic-electric image. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0040] Figure 1 is a flow chart of a high-resolution magnetoacoustic-electric imaging processing method provided in the present application;

[0041] Figure 2 It is a module structure diagram of a high-resolution magnetoacoustic-electric imaging processing system provided in this application. DETAILED DESCRIPTION

[0042] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0043] The embodiment of the present application provides a high-resolution magnetoacoustic-electric imaging processing method and system, the core of which is to obtain magnetoacoustic-electric imaging information of a target body to be measured in a magnetoacoustic-electric imaging scan when a high-resolution magnetoacoustic-electric imaging system is in a working state; decode and reconstruct the magnetoacoustic-electric imaging information to obtain a magnetoacoustic-electric signal of the target body to be measured after decoding and reconstruction; determine an electrical parameter image during magnetoacoustic-electric imaging according to the magnetoacoustic-electric signal; obtain the conductivity gradient of the high-resolution magnetoacoustic-electric imaging system during imaging, and construct a pulse compression image of the target area to be measured according to the conductivity gradient; then perform matched filtering on the pulse compression image to obtain superposition density information during magnetoacoustic-electric imaging; collect a pulse excitation source of the high-resolution magnetoacoustic-electric imaging system during imaging, determine the excitation conversion factor of the pulse excitation source when the target body to be measured is subjected to linear scanning imaging, and then determine the coded pulse excitation during magnetoacoustic-electric imaging according to the excitation conversion factor and the electrical parameter image; perform tomographic imaging processing on the magnetoacoustic-electric image during magnetoacoustic-electric imaging according to the superposition density information and the coded pulse excitation, and reconstruct the processed magnetoacoustic-electric image. The above scheme can eliminate the influence of the side lobes of the magnetoacoustic-electrical signal in magnetoacoustic-electrical imaging, so as to improve the positioning accuracy of the boundary of the target body to be measured in the magnetoacoustic-electrical image.

[0044] Embodiment 1

[0045] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG. 1 , this figure is an exemplary flow chart of a high-resolution magnetoacoustic-electric imaging processing method according to this embodiment of the present application. The imaging processing method includes the following steps:

[0046] In step S1, when the high-resolution magnetoacoustic-electrical imaging system is in working state, magnetoacoustic-electrical imaging information of the target body to be measured in the magnetoacoustic-electrical imaging scan is obtained.

[0047] In specific implementation, when the high-resolution magnetoacoustic-electric imaging system is in working state, the magnetoacoustic-electric imaging information of the target to be measured in the magnetoacoustic-electric imaging scan can be obtained in the following way: First, the system applies a magnetic field of specific frequency and intensity to the target through a high-precision magnetic field excitation device to stimulate the change of conductivity gradient in the target area. This change is related to the internal structure of the target and can trigger the coupling response of electromagnetic waves and sound waves. At the same time, the multimodal sensor network configured in the system starts working, including piezoelectric sensors, magnetic field detection coils and electrical signal detectors, etc., which respectively capture the acoustic signals, electrical signals and electromagnetic waves generated by the target. These signals are amplified and filtered by a dedicated signal acquisition module to remove environmental noise and system interference to ensure the clarity and integrity of the signal. In addition, the system adjusts the sampling frequency and sensitivity of the sensor in real time to adapt to the complexity of different targets. The results of signal acquisition are converted into high-precision digital data and transmitted to the central processing unit for preliminary analysis, which will not be repeated here.

[0048] It should be noted that, in the present application, magnetoacoustic-electric imaging information refers to the comprehensive data of acoustic wave signals, electrical signals and electromagnetic wave signals generated by the target body under the excitation of an external magnetic field in the magnetoacoustic-electric imaging system.

[0049] In step S2, the magnetoacoustic-electric imaging information is decoded and reconstructed to obtain magnetoacoustic-electric signals of the target body to be measured after decoding and reconstruction; and the electrical parameter image during magnetoacoustic-electric imaging is determined according to the magnetoacoustic-electric signals.

[0050] In this embodiment, the magnetoacoustic-electrical imaging information is decoded and reconstructed to obtain the magnetoacoustic-electrical signal of the target body to be measured after decoding and reconstruction, which can be achieved by using the following steps:

[0051] Determining the magnetoacoustic-electrical imaging state of the target object to be measured before decoding and reconstruction according to the magnetoacoustic-electrical imaging information;

[0052] Decoding the magnetoacoustic-electric imaging state to obtain magnetoacoustic-electric imaging characteristics;

[0053] The magnetoacoustic-electrical signal of the target body to be measured after decoding and reconstruction is determined according to the magnetoacoustic-electrical imaging feature.

[0054] In the specific implementation, first, extract the signal characteristics (such as amplitude, phase, and spectrum distribution) from the collected magnetoacoustic-electric imaging information. Apply the signal separation algorithm (such as independent component analysis, ICA) to separate the target signal and background noise; use a mathematical model (such as finite element analysis or a physical field model based on acoustic-electromagnetic coupling) to simulate the imaging state of the target to be measured. The model parameters (such as conductivity and dielectric constant) are obtained by inverting the characteristic signals in the magnetoacoustic-electric information. Then, the time domain signal is transformed into the frequency domain (such as Fourier transform) to extract the characteristic frequency band; for complex signals, the time-frequency analysis method (such as short-time Fourier transform or wavelet transform) is used to decompose the local features of the signal; characteristic parameters such as the main frequency, power spectrum density and time domain envelope of the signal are extracted; feature selection algorithms (such as principal component analysis, PCA) are used to screen out key features and reduce redundant information; finally, the extracted magnetoacoustic and electrical imaging features are used as input, and an inversion algorithm (such as Tikhonov regularization) is applied to construct a physical signal distribution model of the target body; combined with deep learning methods (such as signal prediction models based on convolutional neural networks), the signal reconstruction accuracy is optimized, and the features are mapped to the physical space, and continuous signals are generated through interpolation and fitting techniques (such as B-spline fitting), and the reconstructed signals are post-processed (such as smoothing filtering or edge enhancement) to improve the signal quality.

[0055] It should be noted that in the present application, the magnetoacoustic-electric imaging state refers to the characterization of the internal characteristics (such as conductivity) and signal distribution of the target under specific external magnetic field and signal acquisition conditions; the magnetoacoustic-electric imaging feature refers to the key attributes of the target signal (such as frequency, amplitude, phase); the magnetoacoustic-electric signal refers to the signal obtained by decoding and feature reconstruction, which truly reflects the physical properties and internal structural characteristics of the target.

[0056] In this embodiment, determining the electrical parameter image during magnetoacoustic-electrical imaging according to the magnetoacoustic-electrical signal can be achieved by using the following steps:

[0057] Extracting the noise suppression amount during magnetoacoustic-electric imaging from the magnetoacoustic-electric signal;

[0058] The noise suppression amount is added to the magnetoacoustic-electrical image during magnetoacoustic-electrical imaging to obtain the electrical parameter image during magnetoacoustic-electrical imaging.

[0059] In the specific implementation, first, the spectrum analysis of the magnetic, acoustic and electrical signals is performed (such as fast Fourier transform, FFT) to separate the target signal and noise, and the low-frequency or high-frequency noise is band-pass filtered to retain the effective signal within the specific frequency band. The noise intensity is then evaluated by signal-to-noise ratio (SNR) calculation, and the background noise characteristic quantity is estimated using the noise power spectral density (PSD). If the signal is non-stationary, the empirical mode decomposition (EMD) can be used to separate different noise components and extract the suppression factor. The noise suppression strategy is dynamically adjusted according to the signal characteristics of different imaging areas (such as high conductivity areas and low conductivity areas). Adaptive noise suppression algorithms (such as Wiener filtering) are used to further optimize noise extraction; then, the noise suppression amount is used as a weighting factor and added to the pixel value calculation of the magnetic, acoustic and electrical image. If the noise suppression amount is large (indicating low signal purity), the corresponding image pixel value is corrected (such as gain adjustment), and the noise suppression amount is mapped to the pixel points of the magnetic, acoustic and electrical image. The pixel intensity value is corrected by weighted averaging or direct superposition method, and the fused result is converted into an intuitive electrical parameter image in combination with pseudo-color mapping technology. The generated electrical parameter image is subjected to denoising (such as Gaussian filtering) and edge enhancement (such as Canny operator) to highlight regional features, and histogram equalization is used to improve contrast and enhance the visualization effect of the electrical parameter image.

[0060] It should be noted that in the present application, the noise suppression amount refers to the degree of signal purification after eliminating environmental and system noise in the magnetoacoustic electrical imaging process, which is used to improve the image reconstruction quality; the electrical parameter image refers to the image generated after integrating the noise suppression information, which intuitively reflects the conductivity distribution and physical properties of the target body.

[0061] In step S3, the conductivity gradient of the high-resolution magnetoacoustic-electric imaging system during imaging is obtained, and a pulse compression image of the area to be measured is constructed according to the conductivity gradient; then, matched filtering is performed on the pulse compression image to obtain the stacking density information during magnetoacoustic-electric imaging.

[0062] In specific implementation, obtaining the conductivity gradient of the high-resolution magnetoacoustic-electric imaging system during imaging can be achieved in the following way: First, extract the initial conductivity distribution of the target body from the magnetoacoustic-electric signal obtained during the magnetoacoustic-electric imaging process, which is usually completed by an inversion algorithm (such as multi-physics field coupling inversion based on the finite element method). At this time, the distribution information of the conductivity is usually stored in a discrete form as the conductivity value of pixels or voxels. Next, the core of calculating the conductivity gradient is to perform spatial gradient operations on the distribution information. By calculating the partial derivatives of the conductivity distribution data in two-dimensional or three-dimensional space, the rate of change of the conductivity of each point in the adjacent direction is obtained. In order to improve the accuracy of the calculation, numerical difference methods (such as central difference method) or interpolation techniques are used to fit the discrete data. At the same time, for irregular changes in complex tissue structures, smooth gradient calculation based on Gaussian kernel can be introduced to avoid local noise interference. In order to ensure the physical meaning of the gradient information, the calculation results need to be physically constrained and optimized to remove pseudo gradients that do not meet the imaging conditions. Finally, the conductivity gradient is presented in the form of a tensor field, which will not be repeated here.

[0063] It should be noted that in this application, the conductivity gradient refers to the rate of change of the conductivity distribution in the target body in space, using a vector or tensor field to describe the magnitude and direction of the change of conductivity at each spatial point with position. It reflects the discontinuity of conductivity or the intensity of the transition zone between different tissues or materials in the target area.

[0064] In specific implementation, the pulse compression image of the area to be measured can be constructed according to the conductivity gradient in the following manner: First, the conductivity distribution gradient field of the target area is established by using the conductivity gradient information through a spatial modeling method (such as a multi-layer finite element model). The gradient field describes the direction and intensity of the conductivity transition in different regions, providing a basis for the simulation of the pulse response. Next, according to the principle of pulse compression, a wide bandwidth excitation signal (such as a linear frequency modulation pulse signal) is applied to the target area, and the echo signal after it passes through the target area is collected. Combined with the conductivity gradient field, the echo signal is pulse compressed. Specifically, pulse compression significantly improves the time resolution and spatial positioning accuracy of the signal by convolving the received signal with the matched filter of the transmitted signal. In actual implementation, the matched filtering process of the signal needs to weight the gradient information to highlight the characteristics of the high gradient area, which can be achieved by setting the gradient weight function. In addition, the pulse compression result will be affected by noise, so it is necessary to adopt a subsequent filtering algorithm (such as adaptive filtering) to enhance the reliability of the signal. Finally, the processed pulse compression image presents a high-resolution conductivity distribution in the target area, which will not be repeated here.

[0065] It should be noted that in this application, pulse compression images refer to images processed by pulse compression technology, which present the spatial distribution characteristics of the internal physical properties of the target area (such as conductivity and dielectric distribution) in a high-resolution form. Pulse compression technology significantly improves the time resolution and spatial positioning ability of the signal by matching the echo of the excitation signal, thereby more clearly distinguishing the fine structure and boundaries of the target body in the image.

[0066] In this embodiment, matching filtering is performed on the pulse compression image to obtain the stacking density information during magnetoacoustic-electrical imaging, which can be achieved by the following steps:

[0067] Determining imaging excitation factors during magnetoacoustic-electrical imaging according to the pulse compression image;

[0068] Determine the density of the imaging medium during magnetoacoustic-electrical imaging;

[0069] The superimposed density information during magnetoacoustic-electric imaging is determined according to the imaging excitation factor and the imaging medium density.

[0070] In specific implementation, first, a spectrum analysis is performed on the pulse compression image obtained from the magnetoacoustic-electric imaging system to extract the amplitude, frequency and phase information of the excitation signal, and a time domain or frequency domain analysis method (such as short-time Fourier transform, power spectral density analysis, etc.) is used to identify the frequency characteristics of the imaging excitation source. By comparing the difference between the reflected signal and the original excitation signal, the propagation effect of the excitation signal is extracted, especially the changes caused by the medium properties (such as conductivity and density). Through the difference between the known excitation signal and the echo signal, an inversion algorithm (such as a model-based inversion method) is used to calculate the spatial distribution and intensity of the excitation source. For example, the propagation path of the signal can be estimated based on the phase change of the pulse signal, thereby determining the role of the excitation source in space; then, a physical model of the target area is constructed, considering the influence of parameters such as conductivity, dielectric constant and density on signal propagation, the electromagnetic characteristics of the target area (such as biological tissue or material) are modeled, and its refraction, reflection and attenuation effects on electromagnetic wave propagation are calculated, and the density information is extracted from the pulse compression image using an inversion technique. For example, the spatial distribution of medium density is estimated by combining conductivity gradient, echo time and propagation speed, and inversion calculation is performed through optimization algorithm (such as least square method or Newton method) to obtain the density distribution of the target area; finally, the effect of the excitation source on the target body and the influence of the medium density on signal propagation are integrated, and the superposition density of each pixel is obtained through weighted processing. Weighted calculation is performed on each point in the target area, and the correction effect of its surrounding environment on the signal is considered to generate superposition density data. Combined with imaging excitation factors, medium density and conductivity gradient, physical modeling technology (such as finite element method or difference method) is used to further optimize the superposition density calculation to ensure the physical consistency of imaging. For example, if the density of a certain area is higher, the signal attenuation is greater, and vice versa. According to these physical characteristics, the superposition result of the signal is adjusted.

[0071] It should be noted that in this application, the imaging excitation factor refers to the influence of the excitation signal on the imaging process, including the frequency, amplitude and propagation characteristics of the excitation source; the imaging medium density refers to the influence of the medium in the imaging area on the signal propagation, especially the propagation speed and attenuation of electromagnetic waves in different substances. The medium density directly affects the clarity and signal-to-noise ratio of the image; the superimposed density information represents the comprehensive physical density distribution of the target area. It reflects the distribution and characteristics of the material in the target area and is an important parameter for generating high-resolution imaging images.

[0072] In step S4, the pulse excitation source of the high-resolution magnetoacoustic-electric imaging system during imaging is collected, and the excitation conversion factor of the pulse excitation source during linear scanning imaging of the target to be measured is determined, and then the coded pulse excitation during magnetoacoustic-electric imaging is determined through the excitation conversion factor and the electrical parameter image.

[0073] In specific implementation, the pulse excitation source of the high-resolution magnetoacoustic and electric imaging system during imaging can be realized in the following way: First, the excitation source in the imaging system needs to be configured. Usually, the high-resolution magnetoacoustic and electric imaging system uses linear frequency modulation pulse (LFM) or single pulse as the excitation signal. The frequency range and waveform characteristics of these pulse excitation sources are customized according to the imaging requirements (such as resolution, imaging depth, etc.). During the imaging process, the electromagnetic waves emitted by the excitation source are emitted through the sensor or transducer and irradiated onto the target to be measured. After receiving the reflected signal, the sensor will collect these signals. In order to ensure the accuracy of the collected excitation signal, high-frequency sampling technology and signal processing algorithms are required to ensure that the accurate signals emitted by the excitation source at different time points are captured. In addition, the intensity, duration and frequency characteristics of the pulse excitation source have a crucial impact on the imaging results. Therefore, it is necessary to monitor the signal characteristics of the excitation source in real time during the acquisition process to ensure the stability and consistency of the signal and avoid imaging distortion caused by signal attenuation or interference, which will not be repeated here.

[0074] It should be noted that in this application, the pulse excitation source refers to a device or component used to generate and transmit electromagnetic pulse signals in a magnetoacoustic and electrical imaging system. It emits a short-term, frequency-specific pulse signal to the target to be measured to stimulate the target to generate a response wave.

[0075] In this embodiment, determining the excitation conversion factor of the pulse excitation source when the target to be measured is subjected to linear scanning imaging can be achieved by using the following steps:

[0076] Obtaining imaging constraints when the pulse excitation source performs linear scanning imaging on the target to be measured;

[0077] An excitation conversion factor when the target to be measured performs linear scanning imaging is determined according to the imaging constraint amount.

[0078] In the specific implementation, first, the spatial relationship between the pulse excitation source and the target to be measured is modeled to obtain the geometric constraints of the target during the imaging process. For example, the emission angle of the excitation source, the scanning speed, and the physical size of the target are determined. These geometric parameters will affect the signal propagation path, reflection angle, and echo intensity distribution. During the imaging process, the signal will experience attenuation during propagation. The imaging constraint needs to consider the conductivity of the target area, the absorption characteristics of the medium, and the distance factor. Due to the limited bandwidth of the imaging system, the frequency characteristics of the excitation signal will be constrained, so the trade-off between signal bandwidth and imaging accuracy must be considered. The conductivity, density, and surface characteristics of the target will affect the reflection intensity of the signal, and then affect the reception and processing of the echo signal. A signal propagation model is established. The model takes into account the electromagnetic characteristics of the target and its influence on the excitation signal, calculates the signal propagation process between the excitation source and the target, and obtains the imaging constraint of the signal on the target; then, using the obtained imaging constraint, a mathematical model is established to calculate the excitation conversion factor. Common methods include using the amplitude attenuation of the echo signal to correlate with the physical properties of the target (such as conductivity, density, etc.), and obtaining the conversion factor through model inversion. For example, if the conductivity of the target in a specific area is low, the attenuation of the signal when propagating in this area will be large, and the conversion factor will increase accordingly to compensate for the signal loss. The time delay and phase change of the excitation signal and the echo signal are corrected to ensure the accuracy of the conversion factor. The interaction between the target and the signal is simulated through physical modeling (such as finite element method, wave equation, etc.) to further improve the accuracy of the conversion factor. During the imaging process, the excitation conversion factor is dynamically adjusted according to the echo signal collected in real time. As the target and imaging conditions change, the conversion factor may need to be adaptively adjusted to ensure the stability of signal processing and image quality.

[0079] It should be noted that in this application, the imaging constraint describes the physical and geometric relationship between the pulse excitation source and the target under specific scanning conditions, covering factors such as signal attenuation, bandwidth limitation, and target reflection characteristics; the excitation conversion factor represents the proportion of the change in the pulse excitation source signal after passing through the target, which is used to compensate for the attenuation, reflection and propagation effects of the target on the signal, and provide accurate physical correction for subsequent signal processing and image reconstruction.

[0080] In this embodiment, the coded pulse excitation during magnetoacoustic-electrical imaging is determined by the excitation conversion factor and the electrical parameter image and can be implemented by the following steps:

[0081] Determining the pulse fluctuation characteristics during magnetoacoustic-electrical imaging according to the excitation conversion factor;

[0082] Extracting the imaging attenuation during magnetoacoustic-electric imaging from the electrical parameter image;

[0083] The coded pulse excitation during magnetoacoustic-electric imaging is determined according to the pulse fluctuation characteristics and the imaging attenuation.

[0084] In specific implementation. The excitation conversion factor reflects the change of the excitation source signal during the propagation process through the target area, so it is necessary to combine this factor to adjust the frequency characteristics, amplitude changes and phase fluctuations of the pulse waveform. By modeling the physical properties of the target area such as conductivity and density, the attenuation, reflection and propagation path fluctuation characteristics experienced by the pulse signal during propagation are obtained. For example, in some high conductivity areas, the signal may produce amplitude fluctuations due to strong reflection; while in low conductivity areas, the signal may decay faster. The conversion factor is used to adjust the amplitude and phase of the excitation signal to compensate for the fluctuations generated by the signal during propagation in the target area. In this way, the change of the excitation signal can be accurately controlled to improve the quality and signal-to-noise ratio of the echo signal; then, the electrical parameter image is an image generated based on the magnetoacoustic electrical signal, reflecting the physical parameters such as conductivity and dielectric constant of different regions in the target body. During the imaging process, the electrical parameter image provides information about the physical properties of the target. The imaging attenuation reflects the energy loss caused by the target medium characteristics (such as conductivity, absorptivity, etc.) during the propagation of the signal. The attenuation can be calculated by analyzing the conductivity or medium density information in the electrical parameter image. The specific method includes estimating the signal loss through the conductivity gradient or attenuation coefficient of the target area. Commonly used techniques include inversion algorithm, least squares fitting, etc. In the image, the area with higher conductivity will show greater attenuation, and vice versa. The attenuation of each area is calculated by establishing the relationship between the conductivity of the target area and the signal intensity; finally, the frequency, amplitude, duration and modulation mode of the pulse excitation signal are adjusted by combining the pulse fluctuation characteristics determined in step 1 and the imaging attenuation extracted in step 2. For example, the energy of the pulse is increased in the area with lower conductivity to compensate for the attenuation; in the area with higher conductivity, the pulse amplitude is reduced to avoid excessive reflection or distortion caused by excessive signals, and the coded pulse excitation is dynamically adjusted according to the real-time feedback echo signal and imaging results. For example, if the signal attenuation in a certain area is large, the excitation energy of the area can be increased according to the attenuation to ensure that the signal can be effectively received.

[0085] It should be noted that in this application, pulse fluctuation characteristics refer to the changing characteristics of pulse signals in the time and frequency domains, including parameters such as frequency, amplitude, and phase. It has a direct impact on signal attenuation, transmission speed, and reflection characteristics during the imaging process; imaging attenuation refers to the energy loss caused by the physical properties of the target medium such as conductivity and density during the propagation of the signal; coded pulse excitation refers to the characteristics of the pulse signal that are adjusted according to the target physical properties and signal attenuation characteristics during the imaging process.

[0086] In step S5, a tomographic imaging process is performed on the magnetoacoustic-electric image during magnetoacoustic-electric imaging according to the stacked density information and the coded pulse excitation, and the processed magnetoacoustic-electric image is reconstructed.

[0087] In specific implementation, the magnetoacoustic-electric image during magnetoacoustic-electric imaging is processed by tomography according to the stacked density information and the coded pulse excitation, and the processed magnetoacoustic-electric image is reconstructed in the following manner: First, the stacked density information provides the density distribution characteristics of the target area, and the coded pulse excitation optimizes the excitation characteristics of the signal. By combining the two, the system can obtain the spatial distribution information and physical characteristics of the target during the imaging process. Based on this information, tomography processing is performed, that is, a series of detection data (such as echo signals) are used to reconstruct the image. The tomography process integrates the echo signals collected at multiple angles and multiple frequencies into a two-dimensional or three-dimensional imaging data set through a back projection or filtered back projection algorithm (such as the common Fourier transform technology). In this process, the combined effect of pulse excitation and stacked density information ensures uniform excitation of the signal in the target area, and compensates for the signal attenuation in different areas by adjusting the signal fluctuation characteristics. Through multiple inversion and iterative algorithms, the system can accurately restore the internal structure of the target. Finally, the image reconstruction removes noise and enhances contrast by numerically optimizing the processed tomographic data to obtain a clear magnetoacoustic-electric image. This process may use denoising, image filtering and other techniques to improve image quality and resolution, which will not be discussed here.

[0088] It can be seen that in the present application, the influence of the side lobes of the magnetoacoustic and electric signals in magnetoacoustic and electric imaging can be eliminated; wherein, by real-time acquisition of the magnetoacoustic and electric imaging signals of the target area, the electrical and physical property information of the target body is provided, ensuring that the accurate state and details of the target body can be reflected during the imaging process, and by decoding and reconstruction technology, key information in the magnetoacoustic and electric signals, such as conductivity and density characteristics, can be effectively extracted, providing accurate electrical parameters for subsequent image analysis, thereby improving the accuracy and quality of the imaging results; by analyzing the magnetoacoustic and electric signals, an electrical parameter image of the target area, such as a distribution map of conductivity and dielectric properties, can be generated, thereby improving the target recognition and quantitative analysis capabilities during the imaging process, especially in complex materials and high-noise environments; by analyzing the conductivity gradient, a more accurate pulse compression image can be obtained, the signal focusing and target differentiation during the imaging process are optimized, the imaging resolution is improved and the error is reduced, and it is particularly suitable for application scenarios with high-precision requirements; by tomographic imaging and image reconstruction technology, combined with superimposed density information and coded pulse excitation, the spatial resolution and accuracy of the image are significantly improved, the signal attenuation and noise interference in the traditional method are effectively overcome, high-quality target imaging is achieved, and the image clarity and detail restoration capabilities are improved. In summary, the technical solution adopted in the present application can eliminate the influence of the side lobes of the magnetoacoustic-electric signal in magnetoacoustic-electric imaging, so as to improve the positioning accuracy of the boundary of the target body to be measured in the magnetoacoustic-electric image.

[0089] Embodiment 2

[0090] This application provides a high-resolution magnetoacoustic-electric imaging processing system, referring to Figure 2 As shown, this figure is a schematic diagram of a high-resolution magnetoacoustic-electric imaging processing system according to this embodiment of the present application, and the imaging processing system includes:

[0091] The acquisition module 100 is used to obtain the magnetoacoustic-electrical imaging information of the target body to be measured during the magnetoacoustic-electrical imaging scan when the high-resolution magnetoacoustic-electrical imaging system is in working state;

[0092] The decoding module 200 is used to decode and reconstruct the magnetoacoustic-electric imaging information to obtain the magnetoacoustic-electric signal of the target body to be measured after decoding and reconstruction; and determine the electrical parameter image of the magnetoacoustic-electric imaging according to the magnetoacoustic-electric signal;

[0093] The matching module 300 is used to obtain the conductivity gradient of the high-resolution magnetoacoustic-electric imaging system during imaging, and to construct a pulse compression image of the area to be measured according to the conductivity gradient; and then to perform matched filtering on the pulse compression image to obtain the stacking density information during magnetoacoustic-electric imaging;

[0094] The conversion module 400 is used to collect the pulse excitation source of the high-resolution magnetoacoustic-electric imaging system during imaging, determine the excitation conversion factor of the pulse excitation source when the target to be measured is subjected to linear scanning imaging, and then determine the coded pulse excitation during magnetoacoustic-electric imaging through the excitation conversion factor and the electrical parameter image;

[0095] The tomography module 500 is used to perform tomography processing on the magnetoacoustic-electric image during magnetoacoustic-electric imaging according to the stacked density information and the coded pulse excitation, and to reconstruct the processed magnetoacoustic-electric image.

[0096] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] Those skilled 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 related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0098] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A high-resolution magnetoacoustic-electric imaging processing method, characterized in that: The height limit control method comprises the following steps: When the high-resolution magnetoacoustic-electric imaging system is in working state, magnetoacoustic-electric imaging information of the target body to be measured in the magnetoacoustic-electric imaging scan is obtained; Decoding and reconstructing the magnetoacoustic-electric imaging information to obtain the magnetoacoustic-electric signal of the target body to be measured after decoding and reconstruction; determining the electrical parameter image during magnetoacoustic-electric imaging according to the magnetoacoustic-electric signal; Acquire the conductivity gradient of the high-resolution magnetoacoustic-electric imaging system during imaging, and construct a pulse compression image of the area to be measured based on the conductivity gradient; then perform matched filtering on the pulse compression image to obtain the stacking density information during magnetoacoustic-electric imaging; Acquiring a pulse excitation source of a high-resolution magnetoacoustic-electric imaging system during imaging, determining an excitation conversion factor of the pulse excitation source during linear scanning imaging of the target to be measured, and then determining a coded pulse excitation during magnetoacoustic-electric imaging through the excitation conversion factor and the electrical parameter image; The magnetoacoustic-electric image during magnetoacoustic-electric imaging is subjected to tomographic imaging processing according to the stacked density information and the coded pulse excitation, and the processed magnetoacoustic-electric image is reconstructed.

2. A high-resolution magnetoacoustic-electric imaging processing method as claimed in claim 1, characterized in that: Magnetoacoustic-electric imaging information refers to the comprehensive data of acoustic wave signals, electrical signals and electromagnetic wave signals generated by the target object under the stimulation of external magnetic field in the magnetoacoustic-electric imaging system.

3. A high-resolution magnetoacoustic-electric imaging processing method as claimed in claim 1, characterized in that: Decoding and reconstructing the magnetoacoustic and electrical imaging information to obtain the magnetoacoustic and electrical signals of the target object after decoding and reconstruction specifically includes: Determining the magnetoacoustic-electrical imaging state of the target object to be measured before decoding and reconstruction according to the magnetoacoustic-electrical imaging information; Decoding the magnetoacoustic-electric imaging state to obtain magnetoacoustic-electric imaging characteristics; The magnetoacoustic-electrical signal of the target body to be measured after decoding and reconstruction is determined according to the magnetoacoustic-electrical imaging feature.

4. A high-resolution magnetoacoustic-electric imaging processing method as claimed in claim 1, characterized in that: Determining the electrical parameter image during magnetoacoustic-electrical imaging according to the magnetoacoustic-electrical signal specifically includes: Extracting the noise suppression amount during magnetoacoustic-electric imaging from the magnetoacoustic-electric signal; The noise suppression amount is added to the magnetoacoustic-electrical image during magnetoacoustic-electrical imaging to obtain the electrical parameter image during magnetoacoustic-electrical imaging.

5. A high-resolution magnetoacoustic-electric imaging processing method as claimed in claim 1, characterized in that: The conductivity gradient refers to the rate of change of the conductivity distribution in space within the target body.

6. A high-resolution magnetoacoustic-electric imaging processing method as claimed in claim 1, characterized in that: Pulse compression images refer to images processed by pulse compression technology.

7. A high-resolution magnetoacoustic-electric imaging processing method as claimed in claim 1, characterized in that: Performing matched filtering on the pulse compression image to obtain the stacked density information during magnetoacoustic-electrical imaging specifically includes: Determining imaging excitation factors during magnetoacoustic-electrical imaging according to the pulse compression image; Determine the density of the imaging medium during magnetoacoustic-electrical imaging; The superimposed density information during magnetoacoustic-electric imaging is determined according to the imaging excitation factor and the imaging medium density.

8. A high-resolution magnetoacoustic-electric imaging processing method as claimed in claim 1, characterized in that: The overlay density information represents the comprehensive physical density distribution of the target area.

9. A high-resolution magnetoacoustic-electric imaging processing method as claimed in claim 1, characterized in that: Determining the excitation conversion factor of the pulse excitation source when performing linear scanning imaging on the target to be measured specifically includes: Obtaining imaging constraints when the pulse excitation source performs linear scanning imaging on the target to be measured; An excitation conversion factor when the target to be measured performs linear scanning imaging is determined according to the imaging constraint amount.

10. A high-resolution magnetoacoustic-electric imaging processing system, used to execute a high-resolution magnetoacoustic-electric imaging processing method according to any one of claims 1 to 9, characterized in that: The imaging processing system comprises: An acquisition module is used to obtain magnetoacoustic and electric imaging information of the target body to be measured during magnetoacoustic and electric imaging scanning when the high-resolution magnetoacoustic and electric imaging system is in working state; A decoding module is used to decode and reconstruct the magnetoacoustic-electric imaging information to obtain the magnetoacoustic-electric signal of the target body to be measured after decoding and reconstruction; and to determine the electrical parameter image of the magnetoacoustic-electric imaging according to the magnetoacoustic-electric signal; A matching module is used to obtain the conductivity gradient of the high-resolution magnetoacoustic-electric imaging system during imaging, and to construct a pulse compression image of the area to be measured based on the conductivity gradient; and then to perform matched filtering on the pulse compression image to obtain the stacking density information during magnetoacoustic-electric imaging; A conversion module is used to collect the pulse excitation source of the high-resolution magnetoacoustic-electric imaging system during imaging, determine the excitation conversion factor of the pulse excitation source when the target to be measured is subjected to linear scanning imaging, and then determine the coded pulse excitation during magnetoacoustic-electric imaging through the excitation conversion factor and the electrical parameter image; The tomography module is used to perform tomography processing on the magnetoacoustic-electric image during magnetoacoustic-electric imaging according to the stacked density information and the coded pulse excitation, and to reconstruct the processed magnetoacoustic-electric image.

Images