Acupuncture point stimulation monitoring device and monitoring method based on multi-frequency thermoacoustic imaging technology

Abstract

The invention discloses an acupoint stimulation monitoring device and method based on a multi-frequency thermoacoustic imaging technology, and relates to the field of microwave thermoacoustic imaging. The system comprises a microwave excitation module, an acupoint stimulation module, a data acquisition module and a stimulation visualization module. Wherein the microwave excitation module is used for exciting a monitoring target to generate a thermoacoustic signal, the acupoint stimulation module is used for applying stimulation to one or more acupoints of the monitoring target, the data acquisition module is used for detecting and storing the thermoacoustic signal, and the stimulation visualization module comprises a real-time monitoring sub-module and a data post-processing sub-module. And the image reconstruction module is respectively used for performing qualitative image real-time reconstruction and quantitative saliency and correlation calculation on the acquired data. The acupoint stimulation monitoring device and monitoring method based on the multi-frequency thermoacoustic imaging technology are expected to provide a new thought for acupuncture mechanism research from the perspective of dielectric properties.

Description

Technical Field

[0001] The present invention relates to the field of microwave thermoacoustic imaging and traditional Chinese medicine research, and in particular to a multi-frequency microwave thermoacoustic imaging acupoint monitoring device and a monitoring method. Background Art

[0002] Acupoint stimulation, as an important method in traditional Chinese medicine treatment, was first recorded in the Yellow Emperor's Classic of Internal Medicine and has a history of more than 2,000 years. Acupoint stimulation mainly refers to the stimulation of acupoints by acupuncture, moxibustion, acupoint injection, acupoint thread embedding, bloodletting and cupping to regulate the nervous system to achieve therapeutic effects. It has become one of the most common complementary and alternative therapies in the world, and its efficacy has been affirmed. However, the specific mechanism of action of acupoint stimulation is still largely unclear. Exploring the mechanism of action of acupoint stimulation, providing modern scientific explanations and verifications for traditional Chinese medicine, and revealing its deep mechanism of action are of great significance to promoting the application and development of traditional Chinese medicine in the global medical field.

[0003] With the development of non-invasive medical imaging technology, people are able to explore the mechanism of acupoint stimulation more deeply. At present, positron emission tomography (PET), functional magnetic resonance imaging (fMRI), electroencephalography (EEG) and photoacoustic imaging (PAI) are widely used in the exploration of the mechanism of acupoint stimulation. Although these examination methods have their own advantages, they also have shortcomings. For example, although PET can reflect the activity of nerve cells in specific brain regions by recording brain glucose metabolism and blood perfusion parameters, it requires the injection of radioactive isotopes as contrast agents into patients, which poses a hazard to patients due to ionizing radiation. fMRI can monitor the paramagnetism of oxyhemoglobin and deoxyhemoglobin without the aid of exogenous contrast agents and thus observe changes in cerebral blood flow associated with neural activity in specific regions, and can achieve submillimeter spatial resolution. However, fMRI has a slow imaging speed and is difficult to achieve rapid real-time monitoring. EEG can directly detect millisecond-level neuronal discharge activity and is currently the most direct means of studying neural electrical activity in clinical practice. However, EEG has the disadvantage of low spatial resolution. PAI is also used to monitor hemoglobin content and blood oxygen levels under acupoint stimulation due to the functional property of hemoglobin with high absorption of light within a specific wavelength range. However, the penetration depth of light in tissues limits the use of PAI in the field of brain imaging to small animal research. In summary, there is currently a lack of a new biomedical imaging tool with no ionizing radiation risk, high spatiotemporal resolution, and deep imaging depth for exploring the mechanism of acupoint stimulation.

[0004] Microwave-Induced Thermoacoustic Imaging (MITAI) is an emerging non-destructive medical imaging technology that combines the high contrast and deep penetration depth of microwave imaging (MWI) and the high resolution of ultrasound imaging (US). It can reflect the structural information of tissues while reflecting the microwave dielectric functional absorption. In MITAI, biological tissues absorb short microwave pulse energy, generate thermal expansion, and radiate ultrasound waves outward. In recent years, MITAI has made outstanding progress in application fields such as breast imaging, joint imaging, liver imaging, and brain imaging. In the field of brain imaging, it mainly focuses on brain structure imaging and cerebral hemorrhage detection, and has not yet involved the study of brain activity monitoring when the body is stimulated by external stimuli. After acupoints are stimulated by external stimuli, the neural activity in the brain functional area will change, which will cause changes in the dielectric properties of the tissue. Based on this, microwave thermoacoustic imaging technology is expected to become a new non-invasive biomedical imaging tool for acupoint stimulation monitoring. Summary of the invention

[0005] The present invention aims to solve the above problems of the prior art. A device and a method for monitoring acupoint stimulation based on multi-frequency thermoacoustic imaging technology are proposed. The technical solution of the present invention is as follows:

[0006] An acupoint stimulation monitoring device based on multi-frequency thermoacoustic imaging technology, comprising:

[0007] Microwave excitation module, acupoint stimulation module, data acquisition module and stimulation visualization module, wherein

[0008] The microwave excitation module includes a computer, a frequency-adjustable microwave source, a T-shaped waveguide and a broadband antenna connected in sequence. The computer controls the frequency-adjustable microwave source to generate pulse microwaves with rapidly switchable frequencies. The pulse microwaves are divided into two paths via the T-shaped waveguide and radiated to the monitoring targets including brain tissue and organ tissues by a pair of identical broadband antennas. The tissues absorb microwave energy to generate ultrasonic waves, i.e., thermoacoustic signals.

[0009] The acupoint stimulation module includes a monitoring target and an acupoint stimulation module;

[0010] The data acquisition module includes an array ultrasonic transducer, a multi-channel amplifier and a multi-channel data acquisition card; the array ultrasonic transducer is used to quickly detect the thermal acoustic signal and convert it into an electrical signal; the multi-channel amplifier amplifies the electrical signal output by the array ultrasonic transducer in phase and outputs it to the multi-channel data acquisition card; the multi-channel data acquisition card collects the electrical signal amplified by the multi-channel amplifier and stores it in a computer;

[0011] The stimulation visualization module includes a computer, a real-time monitoring submodule and a data post-processing submodule. In the computer, the electrical signals collected by the multi-channel data acquisition card are reconstructed and analyzed, that is, the data are qualitatively and quantitatively expressed through the real-time monitoring submodule and the data post-processing submodule; the real-time monitoring submodule includes an image reconstruction algorithm module, an image registration algorithm module and an image difference algorithm module, wherein the image reconstruction algorithm module is used for real-time display of brain function thermoacoustic images and organ thermoacoustic images, and the image registration algorithm module is used for extracting brain functional areas and organ tissue boundaries; the image difference algorithm module is used for qualitatively describing the intensity changes of brain function thermoacoustic images and organ thermoacoustic images; the data post-processing submodule includes a significance algorithm module and a correlation algorithm module, wherein the significance algorithm module is used to measure the changes of brain functional areas and organ thermoacoustic signals over time, and the correlation algorithm module is used to quantitatively calculate the correlation between thermoacoustic signals of different brain functional areas, between thermoacoustic signals of brain functional areas and organs, and between thermoacoustic signals of the same brain functional area and organ under microwave excitation of different frequencies during acupoint stimulation.

[0012] Furthermore, the frequency tunability in the frequency tunable microwave source refers to the rapid switching of the microwave center frequency within 0.4GHz-10GHz, and it is necessary to ensure that other parameters of the microwave source including pulse width, pulse power and pulse repetition frequency remain unchanged during the monitoring experiment; the broadband antenna is a broadband antenna that is sufficient to cover the frequency range of 0.4GHz-10GHz.

[0013] Furthermore, the array ultrasonic transducer is a concave array ultrasonic transducer or a linear array ultrasonic transducer; the array ultrasonic transducer cooperates with an ultrasonic coupling medium to complete the detection of the thermoacoustic signal, and the ultrasonic coupling medium is used to exclude the air between the array ultrasonic transducer and the monitored target, so that the thermoacoustic signal can be detected by the array ultrasonic transducer with minimal attenuation.

[0014] Furthermore, the ultrasonic coupling medium refers to a medium including a medical ultrasonic coupling agent, paraffin oil and a low-conductivity ultrasonic coupling pad that can reduce the attenuation of thermal acoustic signals on the tissue surface.

[0015] Furthermore, the real-time monitoring submodule is used to reflect in real time the changes in the thermoacoustic signals of the brain functional areas and organs during the acupoint stimulation process; specifically, the image reconstruction algorithm module, the image registration algorithm module and the image difference algorithm module are combined to perform image reconstruction, image registration and image difference on the thermoacoustic signals in turn, judge whether the monitoring targets, including the thermoacoustic signals of the brain functional areas and organs, have changed, and qualitatively judge the strength of the changes; wherein,

[0016] The image reconstruction algorithm module reconstructs the thermoacoustic images of brain functional areas and organs using a delayed superposition algorithm based on the relationship between the time domain information and the spatial position of the thermoacoustic signal;

[0017] The image registration algorithm module automatically reads the coordinate information of different brain functional areas in the brain function localization map and the boundary coordinates of the internal organs in the anatomical map, and scales them according to a suitable ratio to register the thermoacoustic image of brain tissue and the thermoacoustic image of internal organs reconstructed by the image reconstruction module, and sets the thermoacoustic signal outside the brain functional area or the internal organ area in the registered image to 0;

[0018] The image difference algorithm module is obtained by subtracting the registered brain function image and organ image obtained at the initial monitoring time from the registered brain function image and organ image obtained at the monitoring time, which can be expressed as:

[0019] ΔP=P x -P0

[0020] (x=0min,3min,6min,…,60min)

[0021] Among them, P0 represents the image matrix of the brain functional area or organ after registration at the initial moment, P x represents the image matrix of the functional area or organ of the brain after registration at time x, and ΔP represents the differential image matrix obtained at time x.

[0022] Furthermore, the image reconstruction algorithm module uses a delayed superposition algorithm to reconstruct thermoacoustic images of brain functional areas and organs based on the relationship between the time domain information and the spatial position of the thermoacoustic signal, specifically including:

[0023] The delayed superposition algorithm takes the position of each chip of the array ultrasonic transducer as the center, and reversely projects the thermoacoustic signal detected by each chip to the imaging area for image reconstruction. The signal amplitude at any point in the reconstructed image is the superposition of the thermoacoustic signal amplitude detected by each chip.

[0024] Furthermore, the data post-processing submodule is used to quantitatively determine the impact of acupoint stimulation on brain functional areas and internal organs; the quantitative determination rules for the strength of changes in thermoacoustic signals of monitored targets including internal organs and brain functional areas caused by acupoint stimulation, as well as the correlation of changes are as follows: the significance of changes in thermoacoustic signals of brain functional areas and internal organs over time under microwave excitation of the same frequency is calculated using the significance algorithm module, and the correlation algorithm module is used to calculate the correlation of changes between thermoacoustic signals of different brain functional areas under microwave excitation of the same frequency, between thermoacoustic signals of brain functional areas and internal organs under microwave excitation of the same frequency, and between thermoacoustic signals of the same brain functional area under microwave excitation of different frequencies, and between thermoacoustic signals of internal organs under microwave excitation of different frequencies.

[0025] Furthermore, the significance algorithm module extracts pixels of different brain functional areas or organs in each differential image to characterize the intensity of the thermoacoustic signal, and calculates the significance of the changes of the thermoacoustic signals of the brain functional areas and organs over time through variance analysis;

[0026] The correlation algorithm module calculates the pixel mean of different brain functional areas or organs in each differential image to characterize the intensity of the thermoacoustic signal, by calculating the Pearson correlation coefficient or Spearman correlation coefficient between the thermoacoustic signals of different brain functional areas under the same frequency microwave excitation, between the thermoacoustic signals of brain functional areas and organs under the same frequency microwave excitation, and between the thermoacoustic signals of the same brain functional area under different frequency microwave excitation and between the thermoacoustic signals of organs under different frequency microwave excitation.

[0027] An acupoint monitoring method based on any of the above systems, comprising the following steps:

[0028] S1. Turn on the equipment, determine the order of each excitation frequency point of the microwave source, and other system working parameters, determine the total number of monitoring times and each monitoring time point, and the monitoring time interval must be greater than the time required for a complete multi-frequency microwave excitation;

[0029] S2, fixing the imaging part, and fully coupling the array ultrasonic transducer with the imaging part through the ultrasonic coupling medium;

[0030] S3, timing, the computer triggers a single-frequency microwave pulse to excite the monitoring target, and performs thermoacoustic signal detection, collection and storage. The computer displays in real time the thermoacoustic image of brain function and organs and the corresponding differential thermoacoustic image under the single-frequency microwave excitation pulse;

[0031] S4, judging whether the multi-frequency microwave excitation is completed, if not, quickly switching to the next microwave excitation point, repeating S3-S4, if yes, proceeding to S5;

[0032] S5, determine whether the total number of monitoring times is met, if not, execute S6, if yes, execute S8;

[0033] S6, initializing the microwave source excitation frequency, and maintaining other experimental conditions unchanged, determining whether to start or continue acupoint stimulation, if not, performing S3-S4 at the monitoring time point, if yes, performing S7;

[0034] S7, starting acupoint stimulation at the nearest monitoring time point or continuing acupoint stimulation, and performing S3-S4 at each monitoring time point;

[0035] S8, stop monitoring, and perform data post-processing on the data obtained during the monitoring process, that is, perform significance and correlation analysis;

[0036] S9. Output of monitoring results.

[0037] The advantages and beneficial effects of the present invention are as follows:

[0038] 1. Compared with the existing medical imaging technology for acupoint stimulation monitoring, the microwave thermoacoustic imaging technology in the present invention has the advantages of being non-invasive, real-time imaging, deep penetration depth, high resolution, and able to reflect the internal structural characteristics and dielectric functional characteristics of the measured tissue. The acupoint stimulation module connects the acupoints with the monitoring targets including brain functional areas and organs. According to the specific implementation steps, it can realize the continuous and real-time monitoring of the changes in thermoacoustic signals of brain functional areas and organs, and analyze the mechanism of action of acupoint stimulation from the perspective of dielectric properties.

[0039] 2. From a qualitative perspective, the method proposed in the present invention and the improvement of the microwave thermoacoustic imaging system can monitor the changes of thermoacoustic signals of monitoring targets including brain functional areas and organs over time under acupoint stimulation in real time through a real-time monitoring submodule integrating an image reconstruction algorithm module, an image registration algorithm module and an image difference algorithm module, thereby realizing visualization of the entire monitoring process and evaluating the impact of acupoint stimulation in real time. From a quantitative perspective, the thermoacoustic data can be post-processed to a certain extent through a data post-processing submodule integrating a significance algorithm module and a correlation algorithm module, and the effect of acupoint stimulation on the monitoring target can be quantitatively analyzed from the perspective of dielectric properties by relying on statistical methods.

[0040] 3. The acupuncture point monitoring method of the multi-frequency microwave thermoacoustic imaging device proposed in the present invention, the microwave excitation module can quickly realize the microwave switching of different frequencies, so as to quickly obtain multiple groups of single-frequency thermoacoustic differential images. And the data post-processing submodule can be used to perform correlation analysis on each group of single-frequency thermoacoustic differential images, including the thermoacoustic signals between brain functional areas, and between brain functional areas and organ tissues. It is also possible to perform correlation analysis between thermoacoustic signals under microwave excitation of different frequencies on brain functional areas or organ tissues. Thereby making full use of the spectrum dependence characteristics of the dielectric properties of biological tissues.

[0041] 4. Compared with the existing photoacoustic imaging technology, the present invention can use the same ultrasonic transducer to receive thermoacoustic signals and photoacoustic signals, and the obtained thermoacoustic image and photoacoustic image are naturally aligned. Therefore, an independent laser source and optical fiber can be added on the basis of the present invention to form a dual-modal acupoint stimulation monitoring device, and the dielectric functional characteristics of microwave thermoacoustic imaging and the hemodynamic parameters of photoacoustic imaging can be used to complement each other to achieve more accurate monitoring visualization effects and mechanism analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a structural schematic diagram of an acupoint stimulation monitoring device using multi-frequency microwave thermoacoustic imaging technology according to a preferred embodiment of the present invention;

[0043] Figure 2A schematic diagram of a process of visualizing acupoint stimulation according to an embodiment of the present invention;

[0044] Figure 3 The present invention is a flowchart of a method for visualizing acupoint stimulation monitoring using multi-frequency microwave thermoacoustic imaging according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.

[0046] The technical solution of the present invention to solve the above technical problems is:

[0047] like Figure 1 As shown, this embodiment provides an acupoint stimulation monitoring device based on multi-frequency thermoacoustic imaging technology, including: a microwave excitation module, an acupoint stimulation module, a data acquisition module and a stimulation visualization module. Among them, the microwave excitation module includes a computer, a frequency-adjustable microwave source, a T-shaped waveguide and a pair of broadband antennas connected in sequence, which are used to radiate the generated multi-frequency pulse microwaves to the monitoring target, including brain tissue and organ tissue. The acupoint stimulation module includes a monitoring target and acupoint stimulation. The monitoring target includes brain tissue and organ tissue, absorbs microwave energy to generate thermoacoustic signals, and the acupoints are stimulated by methods including but not limited to acupuncture, moxibustion, acupoint injection, acupoint thread embedding, bloodletting and cupping. The data acquisition module includes an array ultrasonic transducer, a multi-channel amplifier and a multi-channel data acquisition card connected in sequence, which are used to detect and store the thermoacoustic signals generated by the monitoring target brain tissue and the corresponding organ tissue. The stimulation visualization module includes a computer, a real-time monitoring submodule and a data post-processing submodule, which are used to perform real-time reconstruction of brain function thermoacoustic images and organ thermoacoustic images and corresponding thermoacoustic differential images during acupoint monitoring, and perform significance and correlation analysis on the obtained thermoacoustic data after the acupoint monitoring is completed.

[0048] Specifically, the real-time monitoring submodule includes an image reconstruction algorithm for real-time display of brain function thermoacoustic images and organ thermoacoustic images, an image registration algorithm for extracting the boundaries of brain functional areas and organ tissues, and an image difference algorithm for qualitatively describing the intensity changes of brain function thermoacoustic images and organ thermoacoustic images. The data post-processing submodule includes a significance algorithm module for quantifying the changes of thermoacoustic signals of brain functional areas and organs over time, and an algorithm module for quantitatively calculating the correlation between thermoacoustic signals of different brain functional areas, between thermoacoustic signals of brain functional areas and organs, and between thermoacoustic signals of the same brain functional areas and organs under microwave excitation of the same frequency during acupoint stimulation.

[0049] On the basis of the above embodiments, the frequency adjustable in the frequency adjustable microwave source means that the microwave center frequency can be quickly switched within 0.4GHz-10GHz, and it is necessary to ensure that other parameters of the microwave source including pulse width, pulse power and pulse repetition frequency remain unchanged during the monitoring experiment. The broadband antenna is a broadband antenna that is sufficient to cover the frequency range of 0.4GHz-10GHz. Among them, the frequency adjustable microwave source combined with the T-shaped waveguide to output two microwaves can also be replaced by two frequency adjustable microwave sources with the same parameters.

[0050] Furthermore, based on the law that the theoretical microwave absorption coefficient between biological tissues changes with microwave frequency, different brain functional areas or organ tissues respond differently to microwaves of different frequencies; therefore, the frequency-adjustable microwave source generates pulsed microwaves of multiple frequencies, and multiple microwave frequencies are pre-set for excitation of different brain functional areas or organ tissues. During the experiment, the monitoring target is excited in turn through the broadband antenna, and the generated thermoacoustic signals are successively collected by the data acquisition module.

[0051] On the basis of the above embodiments, the array ultrasonic transducer is a concave array ultrasonic transducer or a linear array ultrasonic transducer. The array ultrasonic transducer cooperates with an ultrasonic coupling medium to complete the detection of the thermoacoustic signal. The ultrasonic coupling medium can exclude the air between the array ultrasonic transducer and the monitoring target, so that the thermoacoustic signal can be effectively detected by the array ultrasonic transducer with the maximum coupling degree and converted into an electrical signal.

[0052] Specifically, the ultrasonic coupling medium refers to a medium that can reduce the attenuation of thermal acoustic signals on the tissue surface, including medical ultrasonic coupling agent, paraffin oil and low-conductivity ultrasonic coupling pad. During the monitoring experiment, the array ultrasonic transducer can be immersed in a polyethylene film filled with paraffin oil, and a small amount of medical ultrasonic coupling agent can be evenly applied on the tissue surface; or a low-conductivity ultrasonic coupling pad can be embedded between the array ultrasonic transducer and the monitoring target instead.

[0053] On the basis of the above-mentioned embodiment, the real-time monitoring submodule can reflect the changes of the thermoacoustic signals of the brain functional areas and organs in real time during the acupoint stimulation process. That is, the thermoacoustic signals are reconstructed, registered and differentiated in turn by combining the image reconstruction algorithm, the image registration algorithm and the image difference algorithm, and the monitoring targets, including whether the thermoacoustic signals of the brain functional areas and organs have changed, are judged qualitatively, and the strength of the changes is judged.

[0054] Specifically, the image reconstruction algorithm can reconstruct thermoacoustic images of brain functional areas and organs using a delayed superposition algorithm based on the relationship between the time domain information and spatial position of the thermoacoustic signal.

[0055] Specifically, the image registration algorithm automatically reads the coordinate information of different brain functional areas in the brain function localization map and the boundary coordinates of the internal organs in the anatomical map, and scales them according to an appropriate ratio to align the brain tissue thermoacoustic image and the internal organ thermoacoustic image reconstructed by the image reconstruction algorithm, and sets the thermoacoustic signal outside the brain functional area or the internal organ area in the registered image to 0.

[0056] Specifically, the image difference algorithm can be expressed as follows:

[0057] ΔP=P x -P0

[0058] (x=0min,3min,6min,…,60min)

[0059] Among them, P0 represents the image matrix of the brain functional area or organ after registration at the initial moment, P x represents the image matrix of the functional area or organ of the brain after registration at the monitoring time point x, and ΔP represents the differential image matrix obtained at the time x.

[0060] On the basis of the above embodiments, the data post-processing submodule can quantitatively determine the impact of acupoint stimulation on brain functional areas and internal organs; the quantitative determination rules of the strength of changes in thermoacoustic signals of monitored targets including internal organs and brain functional areas caused by acupoint stimulation, as well as the correlation of changes are as follows: the significance algorithm module is used to calculate the significance of changes in thermoacoustic signals of brain functional areas and internal organs over time under microwave excitation of the same frequency, and the correlation algorithm module is used to calculate the correlation of changes between thermoacoustic signals of different brain functional areas under microwave excitation of the same frequency, between thermoacoustic signals of brain functional areas and internal organs under microwave excitation of the same frequency, and between thermoacoustic signals of the same brain functional area under microwave excitation of different frequencies, and between thermoacoustic signals of internal organs under microwave excitation of different frequencies.

[0061] Specifically, the significance algorithm module can extract pixels of different brain functional areas or organs in each differential image to characterize the intensity of thermoacoustic signals, and calculate the significance of the changes of thermoacoustic signals of brain functional areas or organs over time through variance analysis. More specifically, based on the extracted pixels, it is proposed that the intensity of thermoacoustic signals at different time points has no significant difference (null hypothesis H0) and the pixels at different monitoring time points have significant difference (mutually exclusive hypothesis H1); then the sum of squares of pixels at all monitoring time points (between-group sum of squares) and the sum of squares of pixels at each monitoring time point (within-group sum of squares) are calculated; on this basis, the mean square error of pixels at all monitoring times (between-group mean square error) and the mean square error of pixels at a single monitoring time (within-group mean square error) are calculated; finally, the F statistic is calculated based on the sum of the mean square error between groups and the mean square error within groups, and the null hypothesis H0 is rejected based on the comparison of the calculated results with the critical value. If the null hypothesis H0 is rejected, it is considered that the intensity of the thermoacoustic signal at different monitoring time points has significant differences; otherwise, it is considered that the intensity of the thermoacoustic signal at different monitoring time points has no significant differences. The formula is as follows:

[0062]

[0063] Among them, SSB represents the sum of squares between groups, and k represents the total number of monitoring time points n j represents the number of pixels at the jth monitoring time point, represents the pixel mean at the jth monitoring time point, represents the overall pixel mean, SSW represents the sum of squares within the group, and X ij It represents the i-th pixel at the j-th monitoring time point, MSB represents the mean square error between groups, n represents the number of pixels at all monitoring time points, and MSW represents the mean square error within the group.

[0064] Specifically, the correlation algorithm module can calculate the pixel mean in different brain functional areas or organs in each differential image to characterize the average thermoacoustic signal intensity, and calculate the Pearson correlation coefficient or Spearman correlation coefficient between the thermoacoustic signals of different brain functional areas under the same frequency microwave excitation, between the thermoacoustic signals of brain functional areas and organs under the same frequency microwave excitation, and between the thermoacoustic signals of the same brain functional area under different frequency microwave excitation and between the thermoacoustic signals of organs under different frequency microwave excitation. Taking the correlation calculation between the thermoacoustic signals of the primary motor cortex brain functional area and the caudate nucleus brain functional area as an example, the Pearson correlation coefficient calculation formula is as follows:

[0065]

[0066] Where ρ represents the Pearson correlation coefficient, X i and Y irepresents the calculated pixel mean of the corresponding area of ​​the primary motor cortex brain function area and the caudate nucleus brain function area at the i-th monitoring time point, and X and Y represent the average values ​​of X and Y.

[0067] The formula for calculating the Spearman correlation coefficient is as follows:

[0068]

[0069] Among them, ρ s represents the Spearman correlation coefficient, x i and i represents the calculated pixel mean of the corresponding area of ​​the primary motor cortex brain function area and the caudate nucleus brain function area at the i-th monitoring time. The n groups of pixel means are sorted from high to low, then R(x i ) and R(y i ) represents x i and i The ranking among n groups of thermoacoustic signal intensities, R(x) and R(y) represent the average ranking of x and y.

[0070] Furthermore, for multiple experimental animals that have undergone the same experimental means and the same implementation steps, significance analysis and correlation analysis can also be performed through the significance algorithm module and the correlation algorithm module, so as to more accurately quantify and analyze the effect of acupoint stimulation on the monitoring target from the perspective of dielectric properties. The specific steps are generally consistent with the above-mentioned significance and correlation analysis steps. The difference is that for multiple experimental animals under the same experimental conditions, at any monitoring time point, the significance algorithm module calculates the pixel mean of different brain functional areas or organs of each experimental animal to characterize the intensity of the thermoacoustic signal; the correlation algorithm module calculates the pixel mean of all monitoring target brain functional areas or organs, and averages the calculated pixel means to characterize the average thermoacoustic signal intensity.

[0071] Based on the above embodiment, for single-frequency thermoacoustic data i (i=1, 2, . . . , n), the data visualization processing flow chart is as follows: Figure 2 As shown:

[0072] Specifically, the single-frequency thermoacoustic data i includes structural information and dielectric function information of brain tissue and organ tissue, and image reconstruction is performed through an image reconstruction algorithm to obtain a thermoacoustic image of the brain tissue and a thermoacoustic image of the organ tissue.

[0073] Furthermore, the brain function atlas and the anatomical atlas cooperate with the image registration algorithm to register the reconstructed thermoacoustic images of brain tissue and organ tissue, and extract the corresponding thermoacoustic images of brain functional areas and organ tissues.

[0074] Furthermore, the brain function thermoacoustic image and organ thermoacoustic image at the monitoring time point are differentiated from the brain function thermoacoustic image and organ thermoacoustic image at the beginning of monitoring by using an image difference algorithm to obtain the brain function thermoacoustic difference image and organ thermoacoustic difference image at the corresponding monitoring time point.

[0075] Furthermore, the brain function thermoacoustic difference image is analyzed by a saliency algorithm module.

[0076] The significance of the changes of thermoacoustic signals of different brain functional areas with monitoring time under the same microwave excitation frequency is calculated; the significance of the changes of thermoacoustic signals of organs with monitoring time is calculated by the significance algorithm module of the organ differential image.

[0077] Furthermore, the Pearson correlation coefficient or Spearman correlation coefficient of the thermoacoustic signals between different brain functional areas and between brain functional areas and organs under the same microwave excitation frequency is calculated through the correlation algorithm module.

[0078] Furthermore, the Pearson correlation coefficient or Spearman correlation coefficient between the thermoacoustic signals generated by the same brain functional area at n microwave excitation frequencies and between the thermoacoustic signals generated by organs at n microwave excitation frequencies is calculated through a correlation algorithm module.

[0079] like Figure 3 As shown, the visual method for monitoring acupoint stimulation using multi-frequency microwave thermoacoustic imaging technology of the present invention comprises the following steps:

[0080] S1. Turn on the equipment, determine the order of each excitation frequency point of the microwave source, and other system working parameters, determine the total number of monitoring times and each monitoring time point, and the monitoring time interval must be greater than the time required for a complete multi-frequency microwave excitation;

[0081] S2, fixing the imaging part, and fully coupling the array ultrasonic transducer with the imaging part through the ultrasonic coupling medium;

[0082] S3, timing, the computer triggers a single-frequency microwave pulse to excite the monitoring target, and performs thermoacoustic signal detection, collection and storage. The computer displays in real time the thermoacoustic image of brain function and organs and the corresponding differential thermoacoustic image under the single-frequency microwave excitation pulse;

[0083] S4, judging whether the multi-frequency microwave excitation is completed, if not, quickly switching to the next microwave excitation point, repeating S3-S4, if yes, proceeding to S5;

[0084] S5, determine whether the total number of monitoring times is met, if not, execute S6, if yes, execute S8;

[0085] S6, initializing the microwave source excitation frequency, and maintaining other experimental conditions unchanged, determining whether to start or continue acupoint stimulation, if not, performing S3-S4 at the monitoring time point, if yes, performing S7;

[0086] S7, starting acupoint stimulation at the nearest monitoring time point or continuing acupoint stimulation, and performing S3-S4 at each monitoring time point;

[0087] S8, stop monitoring, and perform data post-processing on the data obtained during the monitoring process, that is, perform significance and correlation analysis;

[0088] S9. Output of monitoring results.

[0089] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

[0090] 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.

[0091] The above embodiments should be understood to be only used to illustrate the present invention and not to limit the protection scope of the present invention. After reading the contents of the present invention, technicians can make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. An acupoint stimulation monitoring device based on multi-frequency thermoacoustic imaging technology, characterized in that: include: Microwave excitation module, acupoint stimulation module, data acquisition module and stimulation visualization module, wherein The microwave excitation module includes a computer, a frequency-adjustable microwave source, a T-shaped waveguide and a broadband antenna connected in sequence. The computer controls the frequency-adjustable microwave source to generate pulse microwaves with rapidly switchable frequencies. The pulse microwaves are divided into two paths via the T-shaped waveguide and radiated to the monitoring targets including brain tissue and organ tissues by a pair of identical broadband antennas. The tissues absorb microwave energy to generate ultrasonic waves, i.e., thermoacoustic signals. The acupoint stimulation module includes a monitoring target and an acupoint stimulation module; The data acquisition module includes an array ultrasonic transducer, a multi-channel amplifier and a multi-channel data acquisition card; the array ultrasonic transducer is used to quickly detect the thermal acoustic signal and convert it into an electrical signal; the multi-channel amplifier amplifies the electrical signal output by the array ultrasonic transducer in phase and outputs it to the multi-channel data acquisition card; the multi-channel data acquisition card collects the electrical signal amplified by the multi-channel amplifier and stores it in a computer; The stimulation visualization module includes a computer, a real-time monitoring submodule and a data post-processing submodule. In the computer, the electrical signals collected by the multi-channel data acquisition card are reconstructed and analyzed, that is, the data are qualitatively and quantitatively expressed through the real-time monitoring submodule and the data post-processing submodule; the real-time monitoring submodule includes an image reconstruction algorithm module, an image registration algorithm module and an image difference algorithm module, wherein the image reconstruction algorithm module is used for real-time display of brain function thermoacoustic images and organ thermoacoustic images, and the image registration algorithm module is used for extracting brain functional areas and organ tissue boundaries; the image difference algorithm module is used for qualitatively describing the intensity changes of brain function thermoacoustic images and organ thermoacoustic images; the data post-processing submodule includes a significance algorithm module and a correlation algorithm module, wherein the significance algorithm module is used to measure the changes of brain functional areas and organ thermoacoustic signals over time, and the correlation algorithm module is used to quantitatively calculate the correlation between thermoacoustic signals of different brain functional areas, between thermoacoustic signals of brain functional areas and organs, and between thermoacoustic signals of the same brain functional area and organ under microwave excitation of different frequencies during acupoint stimulation.

2. The acupoint stimulation monitoring device based on multi-frequency thermoacoustic imaging technology according to claim 1, characterized in that: The frequency tunability in the frequency tunable microwave source refers to the rapid switching of the microwave center frequency within 0.4GHz-10GHz, and it is necessary to ensure that other parameters of the microwave source including pulse width, pulse power and pulse repetition frequency remain unchanged during the monitoring experiment; the broadband antenna is a broadband antenna that is sufficient to cover the frequency range of 0.4GHz-10GHz.

3. The acupoint stimulation monitoring device based on multi-frequency thermoacoustic imaging technology according to claim 1, characterized in that: The array ultrasonic transducer is a concave array ultrasonic transducer or a linear array ultrasonic transducer; the array ultrasonic transducer cooperates with an ultrasonic coupling medium to complete the detection of the thermoacoustic signal, and the ultrasonic coupling medium is used to exclude the air between the array ultrasonic transducer and the monitored target, so that the thermoacoustic signal can be detected by the array ultrasonic transducer with minimal attenuation.

4. The acupoint stimulation monitoring device based on multi-frequency thermoacoustic imaging technology according to claim 3, characterized in that: The ultrasonic coupling medium refers to a medium including a medical ultrasonic coupling agent, paraffin oil and a low-conductivity ultrasonic coupling pad that can reduce the attenuation of thermal acoustic signals on the tissue surface.

5. The acupoint stimulation monitoring device based on multi-frequency thermoacoustic imaging technology according to claim 1, characterized in that: The real-time monitoring submodule is used to reflect the changes in the thermoacoustic signals of brain functional areas and organs during acupoint stimulation in real time; specifically, the image reconstruction algorithm module, the image registration algorithm module and the image difference algorithm module are combined to perform image reconstruction, image registration and image difference on the thermoacoustic signals in turn, judge whether the monitoring targets, including the thermoacoustic signals of brain functional areas and organs, have changed, and qualitatively judge the strength of the changes; wherein, The image reconstruction algorithm module reconstructs the thermoacoustic images of brain functional areas and organs using a delayed superposition algorithm based on the relationship between the time domain information and the spatial position of the thermoacoustic signal; The image registration algorithm module automatically reads the coordinate information of different brain functional areas in the brain function localization map and the boundary coordinates of the internal organs in the anatomical map, and scales them according to a suitable ratio to register the thermoacoustic image of brain tissue and the thermoacoustic image of internal organs reconstructed by the image reconstruction module, and sets the thermoacoustic signal outside the brain functional area or the internal organ area in the registered image to 0; The image difference algorithm module is obtained by subtracting the registered brain function image and organ image obtained at the initial monitoring time from the registered brain function image and organ image obtained at the monitoring time, which can be expressed as: ΔP=P x -P0 (x=0min,3min,6min,…,60min) Among them, P0 represents the image matrix of the brain functional area or organ after registration at the initial moment, P x represents the image matrix of the functional area or organ of the brain after registration at time x, and ΔP represents the differential image matrix obtained at time x.

6. The acupoint stimulation monitoring device based on multi-frequency thermoacoustic imaging technology according to claim 5, characterized in that: The image reconstruction algorithm module uses a delayed superposition algorithm to reconstruct the thermoacoustic images of brain functional areas and organs according to the relationship between the time domain information and the spatial position of the thermoacoustic signal, and specifically includes: The delayed superposition algorithm takes the position of each chip of the array ultrasonic transducer as the center, and reversely projects the thermoacoustic signal detected by each chip to the imaging area for image reconstruction. The signal amplitude at any point in the reconstructed image is the superposition of the thermoacoustic signal amplitude detected by each chip.

7. The acupoint stimulation monitoring device based on multi-frequency thermoacoustic imaging technology according to claim 1, characterized in that: The data post-processing submodule is used to quantitatively determine the impact of acupoint stimulation on brain functional areas and internal organs; the quantitative determination rules for the strength of changes in thermoacoustic signals of monitored targets including internal organs and brain functional areas caused by acupoint stimulation, as well as the correlation of changes are as follows: using the significance algorithm module to calculate the significance of changes over time in thermoacoustic signals of brain functional areas and internal organs under microwave excitation of the same frequency, and using the correlation algorithm module to calculate the correlation of changes between thermoacoustic signals of different brain functional areas under microwave excitation of the same frequency, between thermoacoustic signals of brain functional areas and internal organs under microwave excitation of the same frequency, between thermoacoustic signals of the same brain functional area under microwave excitation of different frequencies, and between thermoacoustic signals of internal organs under microwave excitation of different frequencies.

8. The acupoint stimulation monitoring device based on multi-frequency thermoacoustic imaging technology according to claim 7, characterized in that: The significance algorithm module extracts the pixel values ​​of different brain functional areas or organs in each differential image to characterize the intensity of the thermoacoustic signal, and calculates the significance of the changes of the thermoacoustic signals of the brain functional areas and organs over time through variance analysis; The correlation algorithm module calculates the pixel mean of different brain functional areas or organs in each differential image to characterize the intensity of the thermoacoustic signal, by calculating the Pearson correlation coefficient or Spearman correlation coefficient between the thermoacoustic signals of different brain functional areas under the same frequency microwave excitation, between the thermoacoustic signals of brain functional areas and organs under the same frequency microwave excitation, and between the thermoacoustic signals of the same brain functional area under different frequency microwave excitation and between the thermoacoustic signals of organs under different frequency microwave excitation.

9. The acupoint stimulation monitoring device based on multi-frequency thermoacoustic imaging technology according to claim 8, characterized in that: The significance algorithm module extracts the pixel values ​​of different brain functional areas or organs in each differential image, and calculates the significance of the changes of thermal acoustic signals of brain functional areas and organs over time through variance analysis; The correlation algorithm module calculates the pixel mean of different brain functional areas or organs in each differential image by calculating the Pearson correlation coefficient or Spearman correlation coefficient between the thermoacoustic signals of different brain functional areas under the same frequency microwave excitation, between the thermoacoustic signals of brain functional areas and organs under the same frequency microwave excitation, and between the thermoacoustic signals of the same brain functional area under different frequency microwave excitation and between the thermoacoustic signals of organs under different frequency microwave excitation.

10. An acupoint monitoring method based on the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Turn on the equipment, determine the order of each excitation frequency point of the microwave source, and other system working parameters, determine the total number of monitoring times and each monitoring time point, and the monitoring time interval must be greater than the time required for a complete multi-frequency microwave excitation; S2, fixing the imaging part, and fully coupling the array ultrasonic transducer with the imaging part through the ultrasonic coupling medium; S3, timing, the computer triggers a single-frequency microwave pulse to excite the monitoring target, and performs thermoacoustic signal detection, collection and storage. The computer displays in real time the thermoacoustic image of brain function and organs and the corresponding differential thermoacoustic image under the single-frequency microwave excitation pulse; S4, judging whether the multi-frequency microwave excitation is completed, if not, quickly switching to the next microwave excitation point, repeating S3-S4, if yes, proceeding to S5; S5, determine whether the total number of monitoring times is met, if not, execute S6, if yes, execute S8; S6, initializing the microwave source excitation frequency, and maintaining other experimental conditions unchanged, determining whether to start or continue acupoint stimulation, if not, performing S3-S4 at the monitoring time point, if yes, performing S7; S7, starting acupoint stimulation at the nearest monitoring time point or continuing acupoint stimulation, and performing S3-S4 at each monitoring time point; S8, stop monitoring, and perform data post-processing on the data obtained during the monitoring process, that is, perform significance and correlation analysis; S9. Output of monitoring results.

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