Infrared optical intracranial pressure monitoring method and system for meninx thickness change

Abstract

The invention relates to the technical field of biomedical optical detection. The infrared optical intracranial pressure monitoring method and the infrared optical intracranial pressure monitoring system for providing the thickness change of the meninx comprise the following steps: generating a multi-mode polarized light compensation parameter by detecting the non-uniform deformation direction of the meninx in real time; predicting a polarization state drift parameter caused by dynamic deformation; adjusting an incident light polarization angle of the annular light source array according to the polarization state drift parameter, and generating deformation-interference-resistant multi-mode polarization absorbance time sequence data; inverting the change of the meninx thickness and generating continuous intracranial pressure monitoring data; polarization compensation parameters are adjusted to suppress signal instability, and a closed-loop feedback control signal is generated, so that the conflict problem between insufficient dynamic deformation compensation mechanism and difficult control of polarization state drift in the traditional technology is solved, and the stability and accuracy of monitoring data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical optical detection, and particularly to an infrared optical intracranial pressure monitoring method and system for detecting changes in meningeal thickness. Background Art

[0002] With the continuous development of biomedical optical detection technology, the infrared optical intracranial pressure monitoring technology for detecting changes in meningeal thickness plays an increasingly important role in fields such as neurocritical care. The polarization state drift of the optical signal caused by dynamic deformation is one of the key factors leading to inaccurate intracranial pressure monitoring.

[0003] However, there is a conflict in dynamic deformation compensation and polarization state control in related infrared optical intracranial pressure monitoring technologies. Although traditional technologies can use a fixed polarization light source to monitor the meninges, due to the lack of a dynamic deformation compensation mechanism, it is difficult to effectively control the polarization state drift, thereby affecting the stability and accuracy of monitoring data. Summary of the Invention

[0004] Based on this, it is necessary to provide an infrared optical intracranial pressure monitoring method and system for detecting changes in meningeal thickness in view of the above technical problems, so as to solve the conflict between the lack of a dynamic deformation compensation mechanism and the difficulty in controlling polarization state drift in related technologies, thereby improving the stability and accuracy of monitoring data.

[0005] In a first aspect, the present application provides an infrared optical intracranial pressure monitoring method for detecting changes in meningeal thickness, the method comprising:

[0006] Based on a circularly polarized light source array and a fiber optic gyroscope, generating multi-modal polarization light compensation parameters by real-time detecting the direction of non-uniform deformation of the meninges;

[0007] Based on the real-time deformation direction vector data in the multi-modal polarization light compensation parameters, predicting the polarization state drift parameters caused by dynamic deformation in combination with the geometric correlation model of collagen fiber arrangement;

[0008] According to the polarization state drift parameters, adjusting the incident light polarization angle of the circular light source array to generate multi-modal polarization absorbance time series data resistant to deformation interference;

[0009] Based on the stable absorbance component in the multi-modal polarization absorbance time series data, inversely calculating the change in meningeal thickness and generating continuous intracranial pressure monitoring data;

[0010] Based on the dynamic coupling relationship between the continuous intracranial pressure monitoring data and the real-time deformation direction vector data, adjusting the polarization compensation parameters to suppress signal instability and generating a closed-loop feedback control signal.

[0011] Furthermore, based on the real-time deformation direction vector data in the multimodal polarization light compensation parameters, combined with the geometric correlation model of collagen fiber arrangement, predict the polarization state drift parameters caused by dynamic deformation, including:

[0012] Extract the spatial gradient distribution characteristics of the dynamic deformation of the meninges based on the real-time deformation direction vector data in the multimodal polarization light compensation parameters;

[0013] Calculate the mapping relationship between the arrangement direction of collagen fibers and the polarization state offset of the incident light under dynamic deformation through the geometric correlation model of collagen fiber arrangement;

[0014] Combine the spatial gradient distribution characteristics and the mapping relationship to predict the polarization state drift parameters caused by dynamic deformation.

[0015] Furthermore, combine the spatial gradient distribution characteristics and the mapping relationship to predict the polarization state drift parameters caused by dynamic deformation, including:

[0016] Conduct a dynamic coupling analysis on the spatial gradient distribution characteristics and the mapping relationship to generate a polarization state drift feature vector;

[0017] Based on the polarization state drift feature vector, generate polarization state drift parameters through the polarization extinction ratio correlation model;

[0018] Dynamically calibrate the polarization state drift parameters according to the update frequency of the real-time deformation direction vector data.

[0019] Furthermore, based on the annular polarization light source array and the fiber optic gyroscope, generate multimodal polarization light compensation parameters by real-time detecting the non-uniform deformation direction of the meninges, including:

[0020] Deploy an annular polarization light source array on the surface of the patient's skull, and the annular polarization light source array integrates an adjustable polarizer model and a micro fiber optic gyroscope;

[0021] Detect the curvature change of local expansion or folding of the meninges through the micro fiber optic gyroscope to generate real-time deformation direction vector data;

[0022] Generate multimodal polarization light compensation parameters based on the initial polarization response calibration data and the real-time deformation direction vector data.

[0023] Furthermore, based on the dynamic coupling relationship between the continuous intracranial pressure monitoring data and the real-time deformation direction vector data, adjust the polarization compensation parameters to suppress signal instability and generate a closed-loop feedback control signal, including:

[0024] Conduct a dynamic coupling analysis on the continuous intracranial pressure monitoring data and the real-time deformation direction vector data to generate an evaluation result of the signal instability level;

[0025] Based on the evaluation result of the signal instability level, adjust the multi-modal polarization light compensation parameters through the polarization compensation parameter optimization model;

[0026] According to the adjusted polarization compensation parameters, dynamically calibrate the multi-modal polarization absorbance time-series data against deformation interference to generate a closed-loop feedback control signal.

[0027] Furthermore, based on the evaluation result of the signal instability level, adjust the multi-modal polarization light compensation parameters through the polarization compensation parameter optimization model, including:

[0028] Based on the evaluation result of the signal instability level, load the polarization compensation parameter optimization model;

[0029] Perform dynamic weight allocation processing on the multi-modal polarization light compensation parameters through the polarization compensation parameter optimization model to generate a parameter adjustment strategy;

[0030] Update the polarization angle offset and extinction ratio correction factor in the multi-modal polarization light compensation parameters according to the parameter adjustment strategy.

[0031] Furthermore, based on the stable absorbance component in the multi-modal polarization absorbance time-series data, invert the change in meninges thickness and generate continuous intracranial pressure monitoring data, including:

[0032] Perform polarization noise separation processing on the multi-modal polarization absorbance time-series data to extract the stable absorbance component independent of deformation;

[0033] Based on the stable absorbance component and the optical absorption characteristic data of the meninges tissue, calculate the dynamic change value of the meninges thickness through the absorbance-thickness inversion model;

[0034] Dynamically calibrate the absorbance-thickness inversion model according to the real-time deformation direction vector data to generate continuous intracranial pressure monitoring data.

[0035] In a second aspect, the present application also provides an infrared optical intracranial pressure monitoring system for the change in meninges thickness, and the system includes:

[0036] A dynamic deformation tracking and compensation parameter generation module, configured to generate multi-modal polarization light compensation parameters based on an annular polarization light source array and a fiber optic gyroscope by real-time detecting the non-uniform deformation direction of the meninges;

[0037] A polarization state drift prediction and modeling module, configured to predict the polarization state drift parameters caused by dynamic deformation based on the real-time deformation direction vector data in the multi-modal polarization light compensation parameters and in combination with the geometric correlation model of the collagen fiber arrangement;

[0038] A multi-modal polarization state dynamic compensation module, configured to adjust the incident light polarization angle of the annular light source array according to the polarization state drift parameters to generate multi-modal polarization absorbance time-series data resistant to deformation interference;

[0039] The meninges thickness - intracranial pressure inversion module is used to invert the change of meninges thickness and generate continuous intracranial pressure monitoring data based on the stable absorbance component in the multi - modal polarization absorbance time - series data;

[0040] The closed - loop feedback optimization module is used to adjust the polarization compensation parameters to suppress signal instability and generate a closed - loop feedback control signal based on the dynamic coupling relationship between the continuous intracranial pressure monitoring data and the real - time deformation direction vector data.

[0041] In a third aspect, the present application also provides a computer device, including a memory and a processor. The memory stores a computer program. It is characterized in that when the processor executes the computer program, the steps of any method in the first aspect of the present application are implemented.

[0042] In a fourth aspect, the present application also provides a computer - readable storage medium, on which a computer program is stored. It is characterized in that when the computer program is executed by the processor, the steps of any method in the first aspect of the present application are implemented.

[0043] The technical solutions provided by the present application include the following technical effects: By providing an infrared optical intracranial pressure monitoring method and system for the change of meninges thickness, the method includes: Based on an annular polarization light source array and a fiber optic gyroscope, by detecting the non - uniform deformation direction of the meninges in real time, generating multi - modal polarization light compensation parameters; Based on the real - time deformation direction vector data in the multi - modal polarization light compensation parameters, combined with the geometric correlation model of collagen fiber arrangement, predicting the polarization state drift parameters caused by dynamic deformation; According to the polarization state drift parameters, adjusting the incident light polarization angle of the annular light source array to generate multi - modal polarization absorbance time - series data resistant to deformation interference; Based on the stable absorbance component in the multi - modal polarization absorbance time - series data, inverting the change of meninges thickness and generating continuous intracranial pressure monitoring data; Based on the dynamic coupling relationship between the continuous intracranial pressure monitoring data and the real - time deformation direction vector data, adjusting the polarization compensation parameters to suppress signal instability and generating a closed - loop feedback control signal, so as to solve the conflict problem between the insufficient dynamic deformation compensation mechanism and the difficult - to - control polarization state drift in the traditional technology, thereby improving the stability and accuracy of the monitoring data. Description of the Drawings

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1Flow chart of the infrared optical intracranial pressure monitoring method for the change in meninges thickness in an embodiment of the present invention;

[0046] Figure 2 Structural diagram of the infrared optical intracranial pressure monitoring system for the change in meninges thickness in an embodiment of the present invention. Detailed implementation manners

[0047] In order to make the above objects, features and advantages of the present application more obvious and understandable, the following describes the specific implementation manners of the present application in detail with reference to the accompanying drawings. Many specific details are set forth in the following description in order to fully understand the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the application. Therefore, the present application is not limited by the specific embodiments disclosed below.

[0048] As Figure 1 shown, the present application provides an infrared optical intracranial pressure monitoring method for the change in meninges thickness, and the method includes:

[0049] S101: Based on the circularly polarized light source array and the fiber optic gyroscope, generate multi-modal polarized light compensation parameters by detecting the non-uniform deformation direction of the meninges in real time.

[0050] Specifically, deploy a circularly polarized light source array on the surface of the patient's skull, and the array integrates an adjustable polarizer model and a micro fiber optic gyroscope. The circularly polarized light source array is used to emit infrared light with a specific polarization state, and the fiber optic gyroscope is used to detect the dynamic deformation direction of the meninges. The micro fiber optic gyroscope is used to detect the curvature change of the local expansion or folding of the meninges in real time, and generate real-time deformation direction vector data. The above vector data reflects the spatial gradient distribution characteristics of the meninges under dynamic deformation. In the initialization stage, collect the polarization response data of the meninges in the static state, and establish the initial polarization response calibration data. Combine the real-time deformation direction vector data with the initial polarization response calibration data, and analyze the influence of dynamic deformation on the propagation path of polarized light. Through the geometric correlation model, calculate the mapping relationship between the arrangement direction of collagen fibers and the polarization state offset of the incident light under dynamic deformation. Based on the real-time deformation direction vector data and the mapping relationship, generate multi-modal polarized light compensation parameters. The above compensation parameters are used to dynamically adjust the polarization angle of the incident light of the circularly polarized light source array to suppress the polarization state drift caused by dynamic deformation.

[0051] Feed back the generated compensation parameters in real time, and dynamically calibrate the multi-modal polarized absorbance time series data against deformation interference. Ensure the stability and accuracy of the monitoring data, and provide a reliable basis for subsequent meninges thickness inversion and intracranial pressure monitoring. Through the above steps, the non-uniform deformation direction of the meninges can be detected in real time, and multi-modal polarized light compensation parameters can be generated, thus effectively solving the problem of polarization state drift caused by dynamic deformation.

[0052] S102: Based on the real-time deformation direction vector data in the multimodal polarization light compensation parameters, combined with the geometric correlation model of collagen fiber arrangement, predict the polarization state drift parameters caused by dynamic deformation.

[0053] Specifically, extract the spatial gradient distribution characteristics of the meningeal dynamic deformation from the multimodal polarization light compensation parameters. The above vector data reflects the direction and degree of the meninges under dynamic deformation. Using the geometric correlation model of collagen fiber arrangement, calculate the mapping relationship between the collagen fiber arrangement direction and the incident light polarization state offset under dynamic deformation. This model is based on the arrangement characteristics of collagen fibers in different deformation states, and establishes the correlation between deformation and polarization state change through geometric analysis. Combining the spatial gradient distribution characteristics and the mapping relationship, predict the polarization state drift parameters caused by dynamic deformation. This step generates a polarization state drift feature vector through dynamic coupling analysis, and generates polarization state drift parameters based on the polarization extinction ratio correlation model. According to the update frequency of the real-time deformation direction vector data, dynamically calibrate the polarization state drift parameters to ensure the accuracy and real-time nature of the prediction results. Through the above steps, it is possible to effectively predict the polarization state drift caused by dynamic deformation, providing a basis for subsequent dynamic polarization compensation and stable monitoring data.

[0054] S103: According to the polarization state drift parameters, adjust the incident light polarization angle of the annular light source array to generate multimodal polarization absorbance time series data resistant to deformation interference.

[0055] Specifically, obtain the polarization state drift parameters caused by dynamic deformation from the prediction model. The above parameters reflect the polarization state change caused by meningeal deformation. According to the obtained polarization state drift parameters, use a polarization controller to dynamically adjust the incident light polarization angle of the annular light source array. The polarization controller makes the polarization state of the incident light match the desired polarization state by changing the angle of the wave plate or using the birefringence effect, thereby compensating for the polarization state drift caused by deformation. Under the illumination of the adjusted polarized light, detect the absorbance of the meninges. Since the polarization state has been effectively compensated, the absorbance data is no longer affected by deformation interference, thus generating stable and accurate multimodal polarization absorbance time series data. Feed back the generated multimodal polarization absorbance time series data to further optimize the prediction and compensation of the polarization state drift parameters, ensuring the stability and accuracy of long-term monitoring. Through the above steps, it is possible to effectively suppress the polarization state drift caused by dynamic deformation, improving the accuracy and reliability of infrared optical intracranial pressure monitoring.

[0056] S104: Based on the stable absorbance component in the multimodal polarization absorbance time series data, invert the change in meningeal thickness and generate continuous intracranial pressure monitoring data.

[0057] Specifically, perform polarization noise separation processing on the multi-modal polarization absorbance time-series data to extract the stable absorbance component that is independent of deformation. This step ensures that the data in the subsequent inversion process is not interfered by dynamic deformation. Based on the stable absorbance component and the optical absorption characteristic data of the meninges tissue, calculate the dynamic change value of the meninges thickness through the absorbance-thickness inversion model. This model utilizes the absorption characteristics of the meninges tissue for infrared light of a specific wavelength to establish a quantitative relationship between absorbance and meninges thickness. According to the real-time deformation direction vector data, dynamically calibrate the absorbance-thickness inversion model to ensure the accuracy and real-time nature of the inversion result. Combine the meninges thickness change value calculated by the inversion model with the physical relationship between the meninges thickness and intracranial pressure to generate continuous intracranial pressure monitoring data. The above data can reflect the changes in intracranial pressure in real time, providing an important basis for clinical diagnosis and treatment. Through the above steps, it is possible to extract the stable absorbance component from the multi-modal polarization absorbance time-series data, and use it to invert the change in meninges thickness, thereby generating continuous intracranial pressure monitoring data, providing a reliable monitoring means for fields such as neurocritical care.

[0058] S105: Based on the dynamic coupling relationship between the continuous intracranial pressure monitoring data and the real-time deformation direction vector data, adjust the polarization compensation parameters to suppress signal instability and generate a closed-loop feedback control signal.

[0059] Specifically, perform dynamic coupling analysis on the continuous intracranial pressure monitoring data and the real-time deformation direction vector data to extract the characteristics of signal instability. By analyzing the correlation between the two, determine the level and trend of signal instability. Based on the results of the dynamic coupling analysis, generate an evaluation result of the signal instability level. This step quantifies the degree of signal instability through a comparator and a controller, providing a basis for subsequent parameter adjustment. According to the evaluation result of the signal instability level, dynamically adjust the multi-modal polarization light compensation parameters through the polarization compensation parameter optimization model. The optimization model can perform dynamic weight allocation on the polarization angle offset and the extinction ratio correction factor to generate a parameter adjustment strategy. According to the adjusted polarization compensation parameters, dynamically calibrate the multi-modal polarization absorbance time-series data against deformation interference to ensure the stability and accuracy of the monitoring data. Finally, generate a closed-loop feedback control signal for real-time adjustment of system parameters to suppress signal instability and ensure the reliability of long-term monitoring. Through the above steps, it is possible to effectively suppress signal instability, improve the stability and accuracy of intracranial pressure monitoring, and provide a reliable monitoring means for fields such as neurocritical care.

[0060] The infrared optical intracranial pressure monitoring method for the change in meninges thickness provided by the embodiments of the present application, based on an annular polarized light source array and a fiber optic gyroscope, generates multimodal polarized light compensation parameters by detecting the direction of non-uniform deformation of the meninges in real time; based on the real-time deformation direction vector data in the multimodal polarized light compensation parameters, combined with the geometric correlation model of the arrangement of collagen fibers, predicts the polarization state drift parameters caused by dynamic deformation; according to the polarization state drift parameters, adjusts the incident light polarization angle of the annular light source array to generate multimodal polarization absorbance time series data resistant to deformation interference; based on the stable absorbance component in the multimodal polarization absorbance time series data, inversely calculates the change in meninges thickness and generates continuous intracranial pressure monitoring data; based on the dynamic coupling relationship between the continuous intracranial pressure monitoring data and the real-time deformation direction vector data, adjusts the polarization compensation parameters to suppress signal instability and generates a closed-loop feedback control signal to solve the conflict problem between the insufficient dynamic deformation compensation mechanism and the difficult control of polarization state drift in the traditional technology, thereby improving the stability and accuracy of the monitoring data.

[0061] Further, based on the real-time deformation direction vector data in the multimodal polarized light compensation parameters, combined with the geometric correlation model of the arrangement of collagen fibers, predicting the polarization state drift parameters caused by dynamic deformation includes:

[0062] Based on the real-time deformation direction vector data in the multimodal polarized light compensation parameters, extracts the spatial gradient distribution characteristics of the dynamic deformation of the meninges;

[0063] Through the geometric correlation model of the arrangement of collagen fibers, calculates the mapping relationship between the arrangement direction of collagen fibers and the polarization state offset of the incident light under dynamic deformation;

[0064] Combines the spatial gradient distribution characteristics and the mapping relationship to predict the polarization state drift parameters caused by dynamic deformation.

[0065] Specifically, extracts the spatial gradient distribution characteristics of the dynamic deformation of the meninges from the multimodal polarized light compensation parameters. The above vector data reflects the direction and degree of the meninges under dynamic deformation. Using the geometric correlation model of the arrangement of collagen fibers, calculates the mapping relationship between the arrangement direction of collagen fibers and the polarization state offset of the incident light under dynamic deformation. This model is based on the arrangement characteristics of collagen fibers in different deformation states and establishes the correlation between deformation and polarization state change through geometric analysis. Combines the spatial gradient distribution characteristics and the mapping relationship, and generates a polarization state drift eigenvector through dynamic coupling analysis. Based on the polarization state drift eigenvector, generates polarization state drift parameters through a polarization extinction ratio correlation model, and dynamically calibrates the drift parameters according to the update frequency of the real-time deformation direction vector data. Through the above steps, it is possible to effectively predict the polarization state drift caused by dynamic deformation, providing a basis for subsequent dynamic polarization state compensation and the stability of monitoring data.

[0066] Furthermore, by combining the spatial gradient distribution characteristics with the mapping relationship, the polarization state drift parameters caused by dynamic deformation are predicted, including:

[0067] Conduct dynamic coupling analysis on the spatial gradient distribution characteristics and the mapping relationship to generate a polarization state drift feature vector;

[0068] Based on the polarization state drift feature vector, generate the polarization state drift parameters through the polarization extinction ratio correlation model;

[0069] According to the update frequency of the real-time deformation direction vector data, dynamically calibrate the polarization state drift parameters.

[0070] Specifically, extract the spatial gradient distribution characteristics of the meninges dynamic deformation from the multi-modal polarization light compensation parameters. The above vector data reflects the direction and degree of the meninges under dynamic deformation. Use the geometric correlation model of collagen fiber arrangement to calculate the mapping relationship between the collagen fiber arrangement direction and the incident light polarization state offset under dynamic deformation. This model is based on the arrangement characteristics of collagen fibers in different deformation states, and establishes the correlation between deformation and polarization state change through geometric analysis. Combine the spatial gradient distribution characteristics with the mapping relationship, and generate a polarization state drift feature vector through dynamic coupling analysis. Based on the polarization state drift feature vector, generate the polarization state drift parameters through the polarization extinction ratio correlation model, and dynamically calibrate the drift parameters according to the update frequency of the real-time deformation direction vector data. Through the above steps, the polarization state drift caused by dynamic deformation can be effectively predicted, providing a basis for subsequent dynamic compensation of the polarization state and the stability of monitoring data.

[0071] Furthermore, based on the circularly polarized light source array and the fiber optic gyroscope, by real-time detecting the non-uniform deformation direction of the meninges, multi-modal polarization light compensation parameters are generated, including:

[0072] Deploy a circularly polarized light source array on the patient's skull surface. The circularly polarized light source array integrates an adjustable polarizer model and a micro fiber optic gyroscope;

[0073] Detect the curvature change of local expansion or folding of the meninges through the micro fiber optic gyroscope to generate real-time deformation direction vector data;

[0074] Generate multi-modal polarization light compensation parameters based on the initial polarization response calibration data and the real-time deformation direction vector data.

[0075] Specifically, an annular polarized light source array is deployed on the surface of the patient's skull. This array integrates an adjustable polarizer model and a micro fiber optic gyroscope. The annular polarized light source array is used to emit infrared light with a specific polarization state, and the fiber optic gyroscope is used to detect the dynamic deformation direction of the meninges. By the micro fiber optic gyroscope, the curvature changes of local expansion or folding of the meninges are detected in real time, generating real-time deformation direction vector data. The above vector data reflects the direction and degree of the meninges under dynamic deformation. In the initialization stage, the polarization response data of the meninges in the static state is collected to establish the initial polarization response calibration data. This step ensures a reliable benchmark for dynamic deformation compensation. The real-time deformation direction vector data is combined with the initial polarization response calibration data to analyze the influence of dynamic deformation on the propagation path of polarized light.

[0076] Through a geometric correlation model, the mapping relationship between the arrangement direction of collagen fibers and the polarization state offset of incident light under dynamic deformation is calculated. Based on the real-time deformation direction vector data and the mapping relationship, multi-modal polarized light compensation parameters are generated. The above compensation parameters are used to dynamically adjust the incident light polarization angle of the annular polarized light source array to suppress the polarization state drift caused by dynamic deformation. Through the above steps, the non-uniform deformation direction of the meninges can be detected in real time, and multi-modal polarized light compensation parameters can be generated, thus effectively solving the problem of polarization state drift caused by dynamic deformation.

[0077] Furthermore, based on the dynamic coupling relationship between continuous intracranial pressure monitoring data and real-time deformation direction vector data, the polarization compensation parameters are adjusted to suppress signal instability, generating a closed-loop feedback control signal, including:

[0078] Conduct a dynamic coupling analysis on the continuous intracranial pressure monitoring data and the real-time deformation direction vector data to generate a signal instability level evaluation result;

[0079] Based on the signal instability level evaluation result, adjust the multi-modal polarized light compensation parameters through a polarization compensation parameter optimization model;

[0080] According to the adjusted polarization compensation parameters, dynamically calibrate the multi-modal polarized absorbance time series data against deformation interference to generate a closed-loop feedback control signal.

[0081] Specifically, dynamic coupling analysis is performed on continuous intracranial pressure monitoring data and real-time deformation direction vector data to extract the characteristics of signal instability. By analyzing the correlation between the two, the level and trend of signal instability are determined. Based on the results of the dynamic coupling analysis, an evaluation result of signal instability level is generated. This step quantifies the degree of signal instability through a comparator and a controller, providing a basis for subsequent parameter adjustment. According to the evaluation result of signal instability level, the multi-modal polarization light compensation parameters are dynamically adjusted through a polarization compensation parameter optimization model. The optimization model can perform dynamic weight allocation on the polarization angle offset and extinction ratio correction factor to generate a parameter adjustment strategy. According to the adjusted polarization compensation parameters, the multi-modal polarization absorbance time-series data against deformation interference is dynamically calibrated to ensure the stability and accuracy of the monitoring data. Then a closed-loop feedback control signal is generated for real-time adjustment of system parameters to suppress signal instability and ensure the reliability of long-term monitoring. Through the above steps, signal instability can be effectively suppressed, the stability and accuracy of intracranial pressure monitoring can be improved, and a reliable monitoring method can be provided for fields such as neurocritical care.

[0082] Furthermore, based on the evaluation result of signal instability level, the multi-modal polarization light compensation parameters are adjusted through a polarization compensation parameter optimization model, including:

[0083] Based on the evaluation result of signal instability level, the polarization compensation parameter optimization model is loaded;

[0084] Through the polarization compensation parameter optimization model, dynamic weight allocation processing is performed on the multi-modal polarization light compensation parameters to generate a parameter adjustment strategy;

[0085] According to the parameter adjustment strategy, the polarization angle offset and extinction ratio correction factor in the multi-modal polarization light compensation parameters are updated.

[0086] Specifically, perform dynamic coupling analysis on continuous intracranial pressure monitoring data and real-time deformation direction vector data to extract the characteristics of signal instability. By analyzing the correlation between the two, determine the level and trend of signal instability. Based on the results of the dynamic coupling analysis, generate an evaluation result of the signal instability level. This step quantifies the degree of signal instability through a comparator and a controller, providing a basis for subsequent parameter adjustment. According to the evaluation result of the signal instability level, dynamically adjust the multi-modal polarization light compensation parameters through a polarization compensation parameter optimization model. The optimization model can perform dynamic weight allocation on the polarization angle offset and extinction ratio correction factor to generate a parameter adjustment strategy. According to the adjusted polarization compensation parameters, dynamically calibrate the multi-modal polarization absorbance time-series data against deformation interference to ensure the stability and accuracy of the monitoring data. Then generate a closed-loop feedback control signal for real-time parameter adjustment to suppress signal instability and ensure the reliability of long-term monitoring. Through the above steps, signal instability can be effectively suppressed, the stability and accuracy of intracranial pressure monitoring can be improved, and a reliable monitoring method can be provided for fields such as neurocritical care.

[0087] Further, based on the stable absorbance component in the multi-modal polarization absorbance time-series data, invert the change in meningeal thickness and generate continuous intracranial pressure monitoring data, including:

[0088] Perform polarization noise separation processing on the multi-modal polarization absorbance time-series data to extract the stable absorbance component independent of deformation;

[0089] Based on the stable absorbance component and the optical absorption characteristic data of the meningeal tissue, calculate the dynamic change value of the meningeal thickness through an absorbance-thickness inversion model;

[0090] Dynamically calibrate the absorbance-thickness inversion model according to the real-time deformation direction vector data to generate continuous intracranial pressure monitoring data.

[0091] Specifically, perform polarization noise separation processing on the multi-modal polarization absorbance time-series data to extract the stable absorbance component independent of deformation. This step ensures that the data in the subsequent inversion process is not interfered by dynamic deformation. Based on the stable absorbance component and the optical absorption characteristic data of the meningeal tissue, calculate the dynamic change value of the meningeal thickness through an absorbance-thickness inversion model. This model uses the absorption characteristics of the meningeal tissue for infrared light of a specific wavelength to establish a quantitative relationship between absorbance and meningeal thickness. According to the real-time deformation direction vector data, dynamically calibrate the absorbance-thickness inversion model to ensure the accuracy and real-time nature of the inversion result. The change value of the meningeal thickness calculated by the inversion model, combined with the physical relationship between the meningeal thickness and intracranial pressure, generates continuous intracranial pressure monitoring data. The above data can reflect the change of intracranial pressure in real time, providing an important basis for clinical diagnosis and treatment.

[0092] In one embodiment, a head-mounted cerebral blood oxygen and intracranial pressure detection is used, in which the core component module contacts the base in a magnetic attraction manner, and the base provides a rechargeable battery and a wireless module to supply power to the core component module, receive the host light source control signal, and transmit the sensor data back to the host. The back of the head-mounted cerebral blood oxygen and intracranial pressure detection device is provided with a hidden magnetic charging interface, and the base is flexible and can completely fit the forehead without causing a fitting gap to result in light leakage.

[0093] Through the above steps, a stable absorbance component can be extracted from the multi-modal polarization absorbance time-series data, and the change of the meninges thickness can be inversely calculated by using it, so as to generate continuous intracranial pressure monitoring data and provide a reliable monitoring means for fields such as neurocritical care.

[0094] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indication of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0095] In one embodiment, as Figure 2 shown, the present application also provides an infrared optical intracranial pressure monitoring system 200 for the change of the meninges thickness, and the system includes:

[0096] A dynamic deformation tracking and compensation parameter generation module 201, configured to generate multi-modal polarization light compensation parameters by detecting the non-uniform deformation direction of the meninges in real time based on a circularly polarized light source array and a fiber optic gyroscope;

[0097] A polarization state drift prediction and modeling module 202, configured to predict the polarization state drift parameters caused by dynamic deformation based on the real-time deformation direction vector data in the multi-modal polarization light compensation parameters and in combination with the geometric correlation model of the collagen fiber arrangement;

[0098] A multi-modal polarization state dynamic compensation module 203, configured to adjust the incident light polarization angle of the circular light source array according to the polarization state drift parameters to generate multi-modal polarization absorbance time-series data resistant to deformation interference;

[0099] A meninges thickness - intracranial pressure inversion module 204, configured to inversely calculate the change of the meninges thickness and generate continuous intracranial pressure monitoring data based on the stable absorbance component in the multi-modal polarization absorbance time-series data;

[0100] The closed-loop feedback optimization module 205 is used to adjust the polarization compensation parameters to suppress signal instability and generate a closed-loop feedback control signal based on the dynamic coupling relationship between continuous intracranial pressure monitoring data and real-time deformation direction vector data.

[0101] Specifically, the dynamic deformation tracking and compensation parameter generation module 201 is used to detect the non-uniform deformation direction of the meninges in real time and generate compensation parameters; the polarization state drift prediction and modeling module 202 predicts the polarization state drift based on the compensation parameters; the multi-modal polarization state dynamic compensation module 203 adjusts the polarization angle of the incident light to generate stable data; the meninges thickness-intracranial pressure inversion module 204 inverses the meninges thickness and generates intracranial pressure data; and the closed-loop feedback optimization module 205 ensures the stability and accuracy of the monitoring data. Through the hardware support such as the circularly polarized light source array and the fiber optic gyroscope, combined with the dynamic calibration and feedback mechanism, the problem of polarization state drift caused by dynamic deformation is effectively solved, and the accuracy and reliability of intracranial pressure monitoring are improved.

[0102] The polarization state drift prediction and modeling module 202 is also used for:

[0103] Extracting the spatial gradient distribution characteristics of the dynamic deformation of the meninges based on the real-time deformation direction vector data in the multi-modal polarization light compensation parameters;

[0104] Calculating the mapping relationship between the arrangement direction of collagen fibers and the polarization state offset of the incident light under dynamic deformation through the geometric correlation model of collagen fiber arrangement;

[0105] Combining the spatial gradient distribution characteristics and the mapping relationship to predict the polarization state drift parameters caused by dynamic deformation.

[0106] The polarization state drift prediction and modeling module 202 is also used for:

[0107] Performing dynamic coupling analysis on the spatial gradient distribution characteristics and the mapping relationship to generate a polarization state drift feature vector;

[0108] Generating polarization state drift parameters based on the polarization state drift feature vector through the polarization extinction ratio correlation model;

[0109] Dynamically calibrating the polarization state drift parameters according to the update frequency of the real-time deformation direction vector data.

[0110] The dynamic deformation tracking and compensation parameter generation module 201 is also used for:

[0111] Deploying a circularly polarized light source array on the patient's skull surface, and the circularly polarized light source array integrates an adjustable polarizer model and a micro fiber optic gyroscope;

[0112] Detect the curvature change of local meningeal expansion or folding through a micro-optic fiber gyroscope to generate real-time deformation direction vector data;

[0113] Generate multi-modal polarization light compensation parameters based on the initial polarization response calibration data and the real-time deformation direction vector data.

[0114] The closed-loop feedback optimization module 205 is further configured to:

[0115] Perform dynamic coupling analysis on the continuous intracranial pressure monitoring data and the real-time deformation direction vector data to generate a signal instability level evaluation result;

[0116] Based on the signal instability level evaluation result, adjust the multi-modal polarization light compensation parameters through the polarization compensation parameter optimization model;

[0117] Dynamically calibrate the multi-modal polarization absorbance time-series data against deformation interference according to the adjusted polarization compensation parameters to generate a closed-loop feedback control signal.

[0118] The closed-loop feedback optimization module 205 is further configured to:

[0119] Load the polarization compensation parameter optimization model based on the signal instability level evaluation result;

[0120] Perform dynamic weight allocation processing on the multi-modal polarization light compensation parameters through the polarization compensation parameter optimization model to generate a parameter adjustment strategy;

[0121] Update the polarization angle offset and extinction ratio correction factor in the multi-modal polarization light compensation parameters according to the parameter adjustment strategy.

[0122] The meningeal thickness-intracranial pressure inversion module 204 is further configured to:

[0123] Perform polarization noise separation processing on the multi-modal polarization absorbance time-series data to extract the stable absorbance component independent of deformation;

[0124] Based on the stable absorbance component and the meningeal tissue optical absorption characteristic data, calculate the dynamic change value of the meningeal thickness through the absorbance-thickness inversion model;

[0125] Dynamically calibrate the absorbance-thickness inversion model according to the real-time deformation direction vector data to generate continuous intracranial pressure monitoring data.

[0126] In one embodiment, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0127] In one embodiment, the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0128] In one embodiment, photons are emitted from a light source and received by a detector. The path shape of the photons passing through the brain tissue is similar to a banana. The light emitted by the light source passes through the scalp and skull, then reaches the brain tissue, and after a series of absorption and scattering in the brain tissue, it is detected by the detector at the end of the optical path. According to the derivation of the Lambert-Beer law, the changes in hemoglobin, blood oxygen, etc. can be obtained. The Lambert law states that the proportion of photons absorbed by a transparent medium is independent of the intensity of the light entering the medium. Along the photon transmission path, the proportion of photons absorbed by the same thickness of the medium is the same. The Beer law states that the number of photons absorbed by the medium is proportional to the number of molecules that can absorb photons in the entire optical path. The Lambert-Beer law requires that the medium has no scattering, is evenly distributed, and has a single composition. It is defined as the ratio of the intensity of the incident light to the intensity of the transmitted light, and its expression is as follows:

[0129] I = I0e -∈CL

[0130] where I represents the intensity of the light emerging after the incident light passes through the medium, I0 is the light emitted by the light source, that is, the intensity of the light entering the medium, ∈ represents the molecular extinction coefficient of this medium, C represents the concentration of the medium, and L represents the optical path, that is, the optical path that the incident light passes through the medium.

[0131] The absorbance is defined as the base-10 logarithm of the ratio of the intensity of the incident light before the light passes through a solution or a certain substance to the intensity of the transmitted light after the light passes through the medium:

[0132]

[0133] Optical density (OD) is a commonly used object in the current research on biological tissue spectroscopy. It is the reciprocal of the absorbance. The following formula describes the magnitude of the attenuation of energy when light passes through biological tissue:

[0134]

[0135] Also, because it is found in the research that the light intensity of the light emitted by the light source is not easy to detect, scientific researchers usually detect the change in the optical density (△OD) of biological tissue. First, the intensity of the transmitted light detected under a certain specific condition is selected as the reference state, and then the light intensity detected under other conditions is compared with the light intensity in the reference state. This change value is △OD:

[0136]

[0137] where represents the intensity of the emitted light detected by the detector at time t0, I t represents the intensity of the emitted light detected at time t. By this means, the detection of the incident light, which is a difficult object to accurately detect, is avoided.

[0138] For intracranial pressure detection, only the change in the thickness of the dura mater needs to be obtained. Therefore, the distances between the two photosensitive sensors and the light source are reasonably set to eliminate the loss of light energy by the remaining tissues and further improve the accuracy.

[0139] The absorption coefficient of melanin in the human brain monitoring area for 700 nm red light is much greater than that of hemoglobin. Therefore, when constructing a cerebral blood oxygen monitoring model, it is approximately considered that the change in the optical density of the 700 nm emitted light is caused by the absorption of melanin. Moreover, within the above-mentioned "optical window" band, as the wavelength increases, although the absorption coefficient of melanin for near-infrared light decreases, the trend is relatively stable. Therefore, the change in optical density at 700 nm, that is, the absorption amount of melanin at 700 nm wavelength, is used to replace the influence of melanin on the change in optical density of near-infrared light at 660 nm and 940 nm wavelengths to reduce the complexity during model construction. Based on the above principle, according to the framework of the hardware system, the model is constructed as follows:

[0140]

[0141] Among them, ΔOD λ represents the change in the optical density of the emitted light with a wavelength of λ, where the value of λ includes 700 nm, 640 nm, 805 nm, and 940 nm, DPF represents the differential path factor of a partial optical path, and Δρ represents the difference in the distances between the two photodetectors and the light, respectively represent the molar extinction coefficients of reduced hemoglobin for the wavelength light sources of 660 nm, 805 nm, and 940 nm, respectively represent the molar extinction coefficients of oxyhemoglobin for the wavelength light sources of 660 nm, 805 nm, 940 nm, and 700 nm, represents the protein concentration value in the human brain monitoring area, C Hb represents the concentration value of reduced hemoglobin in the human monitoring area, C M represents the melanin concentration in the human brain monitoring area. Solving the above equations gives the following:

[0142]

[0143]

[0144] rSO2 = kx 2 + bx + c

[0145] Among them, CtHb It represents the total serum protein concentration value of the monitored area of ​​the human brain. Since HbO2 and Hb have equal absorption capacity for near-infrared wavelength, the extinction coefficient G of HbO2 to 805nm source is used in the above formula to represent the absorption coefficient of the two hemoglobins in this environment, and x is the calculated rSO2.

[0146] The accurate k, b, and c are obtained by fitting clinical data. At this point, the cerebral blood oxygen value has been calculated.

[0147] Calculation of intracranial pressure values:

[0148] Intracranial pressure is mainly calculated by detecting the change in dura mater thickness using a 780nm-830nm light source. The data received by the two photosensors are A1 and A2 respectively. The following formula is used to establish a mathematical model and calculate the accurate value of IPC:

[0149] ΔOD=lg(A1 / A2)

[0150] ICP=ΔOD*a+b

[0151] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can refer to the partial description of the method embodiments. The device embodiments described above are only schematic, wherein the components described as separate parts may or may not be physically separated, and the parts displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the disclosed solution. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0152] The above-mentioned embodiments only express several implementation methods of the embodiments of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the embodiments of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the embodiments of the present application, and these all belong to the protection scope of the embodiments of the present application.

Claims

1. An infrared optical intracranial pressure monitoring method based on changes in meningeal thickness, characterized in that: The method comprises: Based on the circular polarization light source array and fiber gyroscope, the multi-modal polarization light compensation parameters are generated by real-time detection of the non-uniform deformation direction of the brain membrane; Based on the real-time deformation direction vector data in the multimodal polarization light compensation parameters, combined with the geometric correlation model of collagen fiber arrangement, the polarization state drift parameters caused by dynamic deformation are predicted; According to the polarization state drift parameter, the polarization angle of the incident light of the annular light source array is adjusted to generate multi-modal polarization absorbance time series data that is resistant to deformation interference; Based on the stable absorbance component in the multimodal polarization absorbance time series data, invert the change of meningeal thickness and generate continuous intracranial pressure monitoring data; Based on the dynamic coupling relationship between the continuous intracranial pressure monitoring data and the real-time deformation direction vector data, the polarization compensation parameters are adjusted to suppress signal instability and generate a closed-loop feedback control signal.

2. The infrared optical intracranial pressure monitoring method of meningeal thickness change according to claim 1 is characterized in that: The method of predicting polarization state drift parameters caused by dynamic deformation based on the real-time deformation direction vector data in the multimodal polarization light compensation parameters and combining the geometric correlation model of collagen fiber arrangement includes: Extracting spatial gradient distribution characteristics of dynamic deformation of meninges based on real-time deformation direction vector data in the multimodal polarized light compensation parameters; The mapping relationship between the arrangement direction of the collagen fibers and the polarization state deviation of the incident light under dynamic deformation is calculated by using the geometric correlation model of the collagen fiber arrangement; The polarization state drift parameter caused by dynamic deformation is predicted by combining the spatial gradient distribution characteristics with the mapping relationship.

3. The infrared optical intracranial pressure monitoring method of meningeal thickness change according to claim 2 is characterized in that: The combining the spatial gradient distribution characteristics with the mapping relationship to predict the polarization state drift parameter caused by dynamic deformation includes: Performing dynamic coupling analysis on the spatial gradient distribution characteristics and the mapping relationship to generate a polarization state drift characteristic vector; Based on the polarization state drift characteristic vector, generating the polarization state drift parameter through a polarization extinction ratio correlation model; The polarization state drift parameter is dynamically calibrated according to the updating frequency of the real-time deformation direction vector data.

4. The infrared optical intracranial pressure monitoring method of meningeal thickness change according to claim 1, characterized in that: The method based on the circular polarization light source array and the fiber optic gyroscope generates multi-modal polarization light compensation parameters by detecting the non-uniform deformation direction of the brain membrane in real time, including: Deploy the circular polarized light source array on the surface of the patient's skull, wherein the circular polarized light source array integrates an adjustable polarizer model and a miniature fiber optic gyroscope; The micro-fiber gyroscope is used to detect the curvature change of the local expansion or folding of the meninges, and to generate real-time deformation direction vector data; The multi-modal polarization light compensation parameters are generated based on the initial polarization response calibration data and the real-time deformation direction vector data.

5. The infrared optical intracranial pressure monitoring method of meningeal thickness change according to claim 1, characterized in that: The method of adjusting polarization compensation parameters to suppress signal instability and generating a closed-loop feedback control signal based on the dynamic coupling relationship between the continuous intracranial pressure monitoring data and the real-time deformation direction vector data includes: Performing dynamic coupling analysis on the continuous intracranial pressure monitoring data and the real-time deformation direction vector data to generate a signal instability level assessment result; Based on the signal instability level evaluation result, adjusting the multi-modal polarization light compensation parameter through a polarization compensation parameter optimization model; The multi-modal polarization absorbance time series data resistant to deformation interference is dynamically calibrated according to the adjusted polarization compensation parameters to generate a closed-loop feedback control signal.

6. The infrared optical intracranial pressure monitoring method of meningeal thickness change according to claim 5, characterized in that: The adjusting the multi-modal polarization light compensation parameter by using a polarization compensation parameter optimization model based on the signal instability level evaluation result includes: Based on the signal instability level evaluation result, loading the polarization compensation parameter optimization model; Performing dynamic weight allocation processing on the multi-modal polarization light compensation parameters through the polarization compensation parameter optimization model to generate a parameter adjustment strategy; The polarization angle offset and the extinction ratio correction factor in the multi-modal polarization light compensation parameters are updated according to the parameter adjustment strategy.

7. The infrared optical intracranial pressure monitoring method of meningeal thickness change according to claim 1, characterized in that: The method of inverting the change of meningeal thickness and generating continuous intracranial pressure monitoring data based on the stable absorbance component in the multimodal polarization absorbance time series data comprises: Performing polarization noise separation processing on the multimodal polarization absorbance time series data to extract a stable absorbance component that is independent of deformation; Based on the stable absorbance component and the optical absorption characteristic data of meningeal tissue, the dynamic change value of meningeal thickness is calculated by an absorbance-thickness inversion model; The absorbance-thickness inversion model is dynamically calibrated according to the real-time deformation direction vector data to generate the continuous intracranial pressure monitoring data.

8. Infrared optical intracranial pressure monitoring system for changes in meningeal thickness, characterized in that: The system comprises: Dynamic deformation tracking and compensation parameter generation module, which is used to generate multi-modal polarization light compensation parameters by real-time detection of the non-uniform deformation direction of the brain membrane based on a circular polarization light source array and a fiber optic gyroscope; A polarization state drift prediction modeling module, used to predict polarization state drift parameters caused by dynamic deformation based on real-time deformation direction vector data in the multi-modal polarization light compensation parameters and in combination with a geometric correlation model of collagen fiber arrangement; A multi-modal polarization state dynamic compensation module, used to adjust the incident light polarization angle of the annular light source array according to the polarization state drift parameter, and generate multi-modal polarization absorbance time series data that is resistant to deformation interference; A meningeal thickness-intracranial pressure inversion module, used to invert the meningeal thickness change and generate continuous intracranial pressure monitoring data based on the stable absorbance component in the multimodal polarization absorbance time series data; The closed-loop feedback optimization module is used to adjust the polarization compensation parameters to suppress signal instability and generate a closed-loop feedback control signal based on the dynamic coupling relationship between the continuous intracranial pressure monitoring data and the real-time deformation direction vector data.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the infrared optical intracranial pressure monitoring method for changes in meningeal thickness according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the infrared optical intracranial pressure monitoring method for changes in meningeal thickness according to any one of claims 1 to 7 are implemented.

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