A biological tissue absorption coefficient reconstruction method, device and medium

Abstract

This application discloses a method, device, and medium for reconstructing the absorption coefficient of biological tissue, relating to the field of tissue optical parameter measurement. The method includes: segmenting a time-resolved analog signal and a measured signal according to a preset time window segmentation rule; constructing a depth-time window coupling weight matrix based on the time window and photon path information; determining the overall error based on the simulated value sequence, the measured value sequence, and the depth-time window coupling weight matrix; determining whether the overall error meets a preset iteration termination condition; if not, updating the sensitivity matrix based on the photon path information, and iteratively updating the current absorption coefficient using the depth-time window coupling weight matrix and the sensitivity matrix; determining a new time-resolved analog signal based on the updated absorption coefficient and photon path information; and continuing until the preset iteration termination condition is met. This application can improve the reliability, accuracy, and practicality of absorption coefficient reconstruction.

Description

Technical Field

[0001] This application relates to the field of tissue optical parameter measurement, and in particular to a method, device and medium for reconstructing the absorption coefficient of biological tissue. Background Technology

[0002] Tissue optical parameters (such as absorption coefficient, scattering coefficient, and refractive index) are important physical quantities characterizing the interaction between light and biological tissues in the field of biomedical photonics. Among them, the absorption coefficient, as one of the most critical parameters, is crucial for applications such as blood oxygen monitoring, early tumor diagnosis, near-infrared brain functional imaging, and photodynamic therapy. Therefore, accurately and rapidly acquiring these parameters is fundamental to promoting the clinical translation and development of related technologies. To this end, numerous measurement methods have been developed, such as near-infrared spectroscopy and optical coherence tomography. Near-infrared spectroscopy, due to its advantages of speed and accuracy, is widely used for optical parameter measurement.

[0003] In existing near-infrared optical parameter measurement techniques, steady-state measurement methods are widely used due to their simplicity and speed. However, steady-state measurement methods can only obtain information on the attenuation of light intensity as it varies spatially; the measurement results are essentially the integral effect of internal optical parameters along the photon path. For the inverse problem of reconstructing the absorption coefficient of multilayer media, the information provided by steady-state measurements is severely insufficient, leading to a highly ill-conditioned problem—different parameter combinations may produce nearly identical surface light intensity distributions. Compared to steady-state measurements, time-domain measurement techniques, by acquiring time-resolved information of emitted photons, can enrich the dimensions of measurement information. This provides far more constraints for solving the inverse problem than steady-state data, thus effectively improving the ill-conditioned nature of the reconstruction problem.

[0004] In recent years, Monte Carlo simulations have been widely used for forward modeling due to their accuracy in handling optical transmission problems in complex media. The inverse reconstruction method (perturbation Monte Carlo method), which combines perturbation theory with Monte Carlo simulations, has shown the potential to simultaneously invert the absorption coefficients of multilayer media and has attracted considerable attention due to its high accuracy.

[0005] However, most existing research based on Monte Carlo simulation methods still relies on steady-state information obtained from multiple detectors, failing to fully utilize the detection results acquired by each detector. Improving the utilization rate of detection data and achieving stable and accurate reconstruction of the absorption coefficient has become a technical challenge of significant practical value.

[0006] Therefore, in order to fully realize the potential of the perturbation Monte Carlo method in parameter reconstruction, it is urgent to develop a new method that can reconstruct the absorption coefficient of biological tissues with high precision based on time-domain probe data, so as to solve the problems of low data utilization, high pathologicalness and system complexity of existing steady-state-based tissue absorption coefficient measurement methods. Summary of the Invention

[0007] The purpose of this application is to provide a method, device, and medium for reconstructing the absorption coefficient of biological tissues, which can improve the reliability, accuracy, and practicality of absorption coefficient reconstruction.

[0008] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for reconstructing the absorption coefficient of biological tissues, including: An optical model of the hierarchical structure was constructed, and the initial absorption coefficients corresponding to different parts were determined. Based on the initial absorption coefficients, Monte Carlo simulation was used to obtain the initial time-resolved simulation signal and the corresponding photon path information. The time-resolved analog signal and the experimentally acquired measurement signal are divided according to a preset time window segmentation rule to obtain the analog value sequence and the measured value sequence for each time window. Based on the time window and the photon path information, a depth-time window coupling weight matrix is ​​constructed; The overall error is determined based on the simulated value sequence, the measured value sequence, and the depth-time window coupling weight matrix. Determine whether the overall error meets the preset iteration termination condition; If the conditions are met, the current absorption coefficient will be used as the reconstructed absorption coefficient. If the conditions are not met, the sensitivity matrix is ​​updated based on the photon path information, and the current absorption coefficient is iteratively updated using the depth-time window coupling weight matrix and the sensitivity matrix. Based on the updated absorption coefficient and the photon path information, a new time-resolved analog signal is determined. The process then returns to the step of dividing the time-resolved analog signal and the experimentally acquired measurement signal according to a preset time window segmentation rule to obtain the analog value sequence and the measured value sequence of each time window, until the preset iteration termination condition is met.

[0009] Secondly, this application provides a biological tissue absorption coefficient reconstruction device, comprising: The data acquisition module is used to construct an optical model of the hierarchical organization and determine the initial absorption coefficients corresponding to different parts; based on the initial absorption coefficients, Monte Carlo simulation is used to acquire the initial time-resolved simulation signal and the corresponding photon path information. The segmentation module is used to segment the time-resolved analog signal and the experimentally acquired measurement signal according to a preset time window segmentation rule to obtain the analog value sequence and the measured value sequence of each time window. The weight matrix construction module is used to construct a depth-time window coupled weight matrix based on the time window and the photon path information; The overall error determination module is used to determine the overall error based on the simulated value sequence, the measured value sequence, and the depth-time window coupling weight matrix. The judgment module is used to determine whether the overall error meets the preset iteration termination condition. If it does, the current absorption coefficient is used as the reconstructed absorption coefficient. If it does not, the sensitivity matrix is ​​updated based on the photon path information, and the current absorption coefficient is iteratively updated using the depth-time window coupling weight matrix and the sensitivity matrix. Based on the updated absorption coefficient and the photon path information, a new time-resolved analog signal is determined. The process then returns to the segmentation module until the preset iteration termination condition is met.

[0010] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the biological tissue absorption coefficient reconstruction method.

[0011] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the biological tissue absorption coefficient reconstruction method.

[0012] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a method, device, and medium for reconstructing the absorption coefficient of biological tissue. First, based on the initial absorption coefficient, Monte Carlo simulation is used to generate an initial time-resolved analog signal (detection reference value) and photon path information. The overall error, based on the simulated value sequence, the measured value sequence, and the depth-time window coupling weight matrix, is considered a local perturbation. A sensitivity matrix characterizing the absorption coefficient and photon intensity is constructed using the photon path information. Then, the absorption coefficient is reconstructed by inverting the overall error. The overall error is introduced into the depth-time window coupling weight matrix to constrain the contribution of data from different time windows to the absorption coefficient at different depths. This application can simultaneously reconstruct the absorption coefficient of multi-layered tissues using time-resolved data (time-resolved analog signal) acquired by a time-correlated single-photon counter. Compared with traditional steady-state methods, it significantly improves the ill-conditioned nature of the solution process, enhances the reliability and practicality of measurements, and has broad application potential in fields such as brain functional imaging and phototherapy. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of a biological tissue absorption coefficient reconstruction method in one embodiment of this application; Figure 2 The model structure diagram used in the simulation experiment; Figure 3 The image shows the reconstruction results and the actual values. Detailed Implementation

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] For near-infrared imaging applications in layered tissues, the target tissue is approximated as a layered planar model. In actual simulations, further voxel segmentation can be performed as needed for increased accuracy. Regarding forward modeling, traditional diffusion-based models struggle to meet the diffusion approximation conditions when the source-detector distance is short, leading to distorted simulation results. Therefore, the Monte Carlo method is employed for forward simulation of photon propagation. Based on the photon path information obtained from the forward model, perturbative Monte Carlo simulation is used to achieve inverse iterative reconstruction of the absorption coefficient. This approach significantly improves simulation accuracy and reconstruction reliability under small source-detector spacing conditions.

[0018] In one exemplary embodiment, such as Figure 1 As shown, a method for reconstructing the absorption coefficient of biological tissues is provided, which includes the following steps S101 to S107. Wherein: S101, Construct an optical model of the layered structure and determine the initial absorption coefficients for different parts. Based on the initial absorption coefficient Monte Carlo simulation was used to obtain the initial time-resolved simulation signal and the corresponding photon path information; Wherein, the initial absorption coefficient Since the absorption coefficient is much smaller than the scattering coefficient, changing the absorption coefficient can be approximated as not affecting the outgoing photon distribution. Therefore, the absorption coefficient can be reconstructed using perturbative Monte Carlo simulation. For the tissue under test with an unknown absorption coefficient, the average or estimated value of the absorption coefficient of similar tissues can be used as the initial absorption coefficient.

[0019] Specifically, the photon path information includes the photon weights at which the photon arrives at the single-photon counter, the total number of interactions between the photon and each layer of medium or voxel, and the total flight time of the photon at the single-photon counter.

[0020] In Monte Carlo simulations, after each movement, the photon interacts with the medium, including scattering and absorption. The photon then continues the next "motion-interaction" cycle until it is completely absorbed by the medium or eventually exits it. The scattering process only changes the photon's direction of motion, not its weight, while the absorption process changes the photon's weight as follows: ; in, , The photon weights before and after the absorption process. The initial value of the absorption coefficient of the target medium layer was set for the simulation experiment. This represents the distance the photon travels during the absorption process.

[0021] Therefore, when a photon enters a single-photon counter, its weight... for: ; Starting from the probabilistic model of photon transmission, and based on the physical meaning of the absorption coefficient and extinction coefficient, the above equation can be approximated as: ; in, Let be the scattering coefficient of each voxel in the photon path. Let be the path length of the photon in each voxel. It can be approximated as: ; For photons in the first The total number of interactions occurring in individual elements. The cumulative multiplication symbol is used. It represents the extinction coefficient of each layer of the medium through which the photon passes (or expressed as the extinction coefficient of the i-th layer of the medium). is the absorption coefficient of each voxel in the photon pathway; In time-domain Monte Carlo simulations, the physical quantities required for updating the perturbation-enhanced single-photon counter signal estimate and calculating the sensitivity matrix are the weights of photons arriving at the single-photon counter, the total number of interactions in each medium, and the time it takes for the photon to reach the counter. If the weights of photon absorption are calculated based on the photon's travel distance, then the total distance the photon travels in each voxel also needs to be statistically analyzed. S102, the time-resolved analog signal and the experimentally acquired measurement signal are divided according to a preset time window segmentation rule to obtain the analog value sequence and the measured value sequence of each time window; Specifically, the preset time window segmentation rules include: equal-interval segmentation and adaptive segmentation based on photon quantity statistical balance.

[0022] As a specific implementation, the adaptive segmentation based on photon number statistical balance dynamically divides the total flight time of photons arriving at the single photon counter according to a set number threshold, so that the number of photons in each segmented time window is the set number threshold, and photons arriving in the last time period that are more affected by noise are discarded.

[0023] When the number of photons used in the forward simulation is large or the detection depth is shallow, equal intervals can be selected; when the number of photons used in the forward simulation is small or the detection depth is deep, the time window width can be appropriately increased or an adaptive segmentation based on the statistical balance of photon numbers can be adopted.

[0024] S103, construct a depth-time window coupling weight matrix based on the time window and the photon path information; S103 specifically includes: Using formula or formula Determine the first The photons obtained in the first time window are related to the first... Weight contributed by the reconstruction of the absorption coefficient of the layered medium ; Construct a depth-time window coupled weight matrix by reconstructing the weights contributed by each layer of medium based on the absorption coefficient of each layer. That is, the depth-time window coupling weight matrix is: ; in, For all time windows, photons in the 1st... The maximum total number of interactions between the layers. For all time windows, photons in the 1st... The total number of interactions between the layers. For the photon in the j-th time window at the th time... The total number of interactions between the layers, where k is the total number of time windows. The total number of dielectric layers. The photon obtained in the k-th time window is for the first time window. The weight contributed by the reconstruction of the absorption coefficient of the layer medium.

[0025] That is, the weight value in the depth-time window coupling weight matrix is ​​the first... The total number of times a photon interacts with the i-th layer of the medium within a time window. The value obtained after normalization; S104, determine the overall error based on the simulated value sequence, the measured value sequence, and the depth-time window coupling weight matrix; S104 specifically includes: Using formula Determine the total error ; in, and The first The single-photon counter in the... Simulated value sequence and measured value sequence in a time window.

[0026] S105, determine whether the total error meets the preset iteration termination condition; specifically, the preset iteration termination condition is that the total error is less than a preset threshold, or the number of iterations reaches a preset maximum value; S106, if satisfied, then the current absorption coefficient is used as the reconstructed absorption coefficient, which is the final inversion result; S107, if not satisfied, update the sensitivity matrix based on the photon path information, and iteratively update the current absorption coefficient using the depth-time window coupling weight matrix and the sensitivity matrix; determine a new time-resolved analog signal based on the updated absorption coefficient and the photon path information; and return to execute the step of dividing the time-resolved analog signal and the experimentally acquired measurement signal according to the preset time window segmentation rule to obtain the analog value sequence and the measured value sequence of each time window, until the preset iteration termination condition is met.

[0027] When updating the single-photon counter estimate, the perturbation Monte Carlo method can be used to recalculate based on the statistical photon path information, thus quickly obtaining the single-photon counter estimate under the current absorption coefficient. The perturbation Monte Carlo method assumes that the photon path remains unchanged before and after the optical parameter perturbation. It calculates the expected result of the single-photon counter received signal under the new optical parameters after the perturbation by correcting the photon weights and the probability of the photon passing through each path.

[0028] The correction of photon weights involves recalculating the remaining weights of photons after absorption by the medium in the corresponding region; the photon weight correction factor is used. for: ; in, The scattering coefficient of the medium, This is an estimate of the extinction coefficient of the medium. The extinction coefficient of the medium, This represents the number of times photons interact in this medium.

[0029] The correction for the probability of a photon traversing each path is the ratio of the probability density of the photon along the original path before and after the change in the absorption coefficient. This correction can be derived from the definition of the mean free path of photon motion. for: ; Therefore, the estimated weight of the photon entering the single-photon counter after the perturbation is: ; Differentiating the above equation with respect to the absorption coefficient yields the expression for the sensitivity matrix derived from a single photon: ; in, Let be the sensitivity of the absorption coefficient of the j-th photon to the i-th layer of the medium. The photon weight estimate is obtained based on photon path information from simulation data. Let be the absorption coefficient of the i-th layer of medium.

[0030] For all photons within the time window, the macroscopic sensitivity matrix is ​​the sum of the sensitivities of all photons, i.e., using the formula... Determine the elements in the sensitivity matrix ; in, The photon weights for photons arriving at the single-photon counter. The absorption coefficient of the i-th layer of medium is set for the simulation experiment. This is an estimate of the absorption coefficient of the i-th layer of medium. Let be the distance traveled by each photon in the i-th layer of the medium. is the base of the natural logarithm.

[0031] Elements in the sensitivity matrix This is used to characterize the effect of changes in the absorption coefficient of the corresponding medium (voxel) on the detection results of a single-photon counter under the current absorption coefficient. Meanwhile, the single-photon counter estimate It can be updated using the following formula: ; in, For the first Estimates of the absorption coefficient of individual elements.

[0032] S107 specifically includes: The current absorption coefficient is iteratively updated using a nonlinear optimization method that couples the weight matrix and sensitivity matrix with the depth-time window. The nonlinear optimization method includes, but is not limited to, the Levenberg-Marquardt method, Newton's method, and the conjugate gradient method.

[0033] If the Levenberg-Marquardt method is used, the updated formula is: ; If we use Newton's law to update the formula, it becomes: ; in, This is the sensitivity matrix. The depth-time window coupling weight matrix is... For damping parameters, It is the identity matrix. This refers to the detector's received signal (i.e., the measurement signal) obtained after the actual experiment. This is the estimated value of the detector received signal (i.e., the time-resolved analog signal) calculated based on the photon path information and the absorption coefficient of the current iteration. This represents the change in the absorption coefficient.

[0034] The following is an illustration through specific examples: This embodiment uses multilayer reconstruction of the absorption coefficient as an example. Such applications are widely used in fields such as blood oxygen saturation detection and near-infrared brain functional imaging. Figure 2 The three-layer structure shown has its optical parameters constantly changing for the first layer (medium 1), while the absorption coefficients for the second and third layers (medium 2 and 3) vary depending on the number of simulations. This experiment uses a single-photon counter for detection and employs time window segmentation to increase effective constraint and improve ill-conditioning. First, a Monte Carlo simulation is performed using the preset parameters in Table 1, with a 10... 7 A single photon counter is set up at a distance of 1 cm from the source and detector to receive the signal and save the path information of the photon entering the single photon counter.

[0035] Table 1

[0036] Subsequently, the absorption coefficients of medium 2 and medium 3 were changed simultaneously. After each change in optical parameters, a Monte Carlo simulation was performed again. The simulation results only recorded the time-resolved intensity information received by the single-photon counter.

[0037] After reconstructing the absorption coefficient using the simulation results with the altered absorption coefficient, the reconstructed absorption coefficient results are as follows: Figure 3 As shown, the relative error of the absorption coefficient reconstruction for medium 2 in the shallower second layer is 0.86%, while for medium 3 in the deeper third layer, the information carried by photons is relatively reduced, and the relative error of the absorption coefficient reconstruction for medium 3 is 7.59%.

[0038] The beneficial effects of this application are reflected in the following aspects: (1) This application has higher reconstruction accuracy when the source and probe are close together, and the low scattering medium or cavity inside the model has less impact on the results.

[0039] (2) This application improves the data dimension and the number of effective constraints by using the time window segmentation method, thereby reducing the pathological nature of the reconstruction problem and improving the reliability of the reconstruction results.

[0040] (3) This application can simultaneously reconstruct the absorption coefficients of multiple different media in a single experimental measurement.

[0041] (4) This application can effectively separate early-arriving photons (mainly reflecting shallow tissue information) and late-arriving photons (carrying more deep tissue information), thereby improving the sensitivity and accuracy of deep tissue information.

[0042] (5) This application introduces a depth-time window coupling weight matrix to differentiate the weighted constraints on the contribution of measurement data from different time windows to the inversion of optical parameters at different depth layers. Compared with the traditional method of assigning the same weight to all time window data, this application can effectively suppress the crosstalk of shallow tissue information to the inversion of deep parameters, further reduce the ill-conditioned nature of the inverse problem solution, and significantly improve the reconstruction accuracy of the deep tissue absorption coefficient.

[0043] (6) The generation of the depth-time window coupling weight matrix in this application directly utilizes the statistical data of the number of photons interacting in each medium layer during the Monte Carlo simulation, without the need for additional computational overhead of the forward simulation. The weight matrix can be adaptively generated according to the actual optical properties of the tissue under test, rather than relying on artificially preset empirical parameters, thus exhibiting stronger adaptability and robustness.

[0044] Based on the same inventive concept, this application also provides a biological tissue absorption coefficient reconstruction device for implementing the above-described biological tissue absorption coefficient reconstruction method. The solution provided by this device is similar to the implementation described in the above method; therefore, the specific limitations of one or more biological tissue absorption coefficient reconstruction device embodiments provided below can be found in the limitations of the biological tissue absorption coefficient reconstruction method described above, and will not be repeated here.

[0045] In one exemplary embodiment, a biological tissue absorption coefficient reconstruction device is provided, comprising: The data acquisition module is used to construct an optical model of the hierarchical organization and determine the initial absorption coefficients corresponding to different parts; based on the initial absorption coefficients, Monte Carlo simulation is used to acquire the initial time-resolved simulation signal and the corresponding photon path information. The segmentation module is used to segment the time-resolved analog signal and the experimentally acquired measurement signal according to a preset time window segmentation rule to obtain the analog value sequence and the measured value sequence of each time window. The weight matrix construction module is used to construct a depth-time window coupled weight matrix based on the time window and the photon path information; The overall error determination module is used to determine the overall error based on the simulated value sequence, the measured value sequence, and the depth-time window coupling weight matrix. The judgment module is used to determine whether the overall error meets the preset iteration termination condition. If it does, the current absorption coefficient is used as the reconstructed absorption coefficient. If it does not, the sensitivity matrix is ​​updated based on the photon path information, and the current absorption coefficient is iteratively updated using the depth-time window coupling weight matrix and the sensitivity matrix. Based on the updated absorption coefficient and the photon path information, a new time-resolved analog signal is determined. The process then returns to the segmentation module until the preset iteration termination condition is met.

[0046] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The I / O interfaces of the computer device are used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for reconstructing the absorption coefficient of biological tissue.

[0047] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0048] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0049] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0050] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0051] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0052] In this application, all actions to acquire signals, information, or data are carried out in compliance with the relevant data protection laws and policies of the country where the location is situated, and with the authorization granted by the owner of the relevant device.

[0053] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0054] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for reconstructing the absorption coefficient of biological tissues, characterized in that, include: Construct an optical model of the hierarchical structure and determine the initial absorption coefficients for different parts; Based on the initial absorption coefficient, Monte Carlo simulation was used to obtain the initial time-resolved simulation signal and the corresponding photon path information. The time-resolved analog signal and the experimentally acquired measurement signal are divided according to a preset time window segmentation rule to obtain the analog value sequence and the measured value sequence for each time window. Based on the time window and the photon path information, a depth-time window coupling weight matrix is ​​constructed; The overall error is determined based on the simulated value sequence, the measured value sequence, and the depth-time window coupling weight matrix. Determine whether the overall error meets the preset iteration termination condition; If the conditions are met, the current absorption coefficient will be used as the reconstructed absorption coefficient. If the conditions are not met, the sensitivity matrix is ​​updated based on the photon path information, and the current absorption coefficient is iteratively updated using the depth-time window coupling weight matrix and the sensitivity matrix. Based on the updated absorption coefficient and the photon path information, a new time-resolved analog signal is determined. The process then returns to the step of dividing the time-resolved analog signal and the experimentally acquired measurement signal according to a preset time window segmentation rule to obtain the analog value sequence and the measured value sequence of each time window, until the preset iteration termination condition is met.

2. The method for reconstructing the absorption coefficient of biological tissues according to claim 1, characterized in that, The preset time window segmentation rules include: equal interval segmentation and adaptive segmentation based on photon quantity statistical balance.

3. The method for reconstructing the absorption coefficient of biological tissues according to claim 1, characterized in that, The photon path information includes the photon weights at which the photon arrives at the single-photon counter, the total number of interactions between the photon and each layer of the medium or voxel, and the total flight time of the photon at the single-photon counter.

4. The method for reconstructing the absorption coefficient of biological tissues according to claim 1, characterized in that, The construction of the depth-time window coupling weight matrix based on the time window and the photon path information specifically includes: Using formula or formula Determine the first The photons obtained in the first time window are related to the first... Weight contributed by the reconstruction of the absorption coefficient of the layered medium ; Construct a depth-time window coupled weight matrix by reconstructing the weights contributed by each layer of medium based on the absorption coefficient of each layer. in, For all time windows, the photon in the th The maximum total number of interactions between the layers. For all time windows, the photon in the th The total number of interactions between the layers. For the photon in the j-th time window at the th time... The total number of interactions between the layers, where k is the total number of time windows.

5. The method for reconstructing the absorption coefficient of biological tissues according to claim 4, characterized in that, The determination of the overall error based on the simulated value sequence, the measured value sequence, and the depth-time window coupling weight matrix specifically includes: Using formula Determine the total error ; in, and The first The single-photon counter in the... Simulated value sequence and measured value sequence in a time window.

6. The method for reconstructing the absorption coefficient of biological tissues according to claim 1, characterized in that, The sensitivity matrix specifically includes: Using formula Determine the elements in the sensitivity matrix ; in, The photon weights for photons arriving at the single-photon counter. The initial value of the absorption coefficient of the target medium layer was set for the simulation experiment. This is an estimated value for the absorption coefficient of the target dielectric layer. This represents the total distance the photon travels within the target medium layer. is the base of the natural logarithm.

7. The method for reconstructing the absorption coefficient of biological tissues according to claim 1, characterized in that, If the above conditions are not met, the sensitivity matrix is ​​updated based on the photon path information, and the current absorption coefficient is iteratively updated using the depth-time window coupled weight matrix and the sensitivity matrix, specifically including: The current absorption coefficient is iteratively updated using the depth-time window coupling weight matrix and sensitivity matrix, and nonlinear optimization methods are employed. These nonlinear optimization methods include the Levenberg-Marquardt method, Newton's method, and conjugate gradient method.

8. A biological tissue absorption coefficient reconstruction device, characterized in that, include: The data acquisition module is used to construct an optical model of the hierarchical organization and determine the initial absorption coefficients corresponding to different parts; Based on the initial absorption coefficient, Monte Carlo simulation was used to obtain the initial time-resolved simulation signal and the corresponding photon path information. The segmentation module is used to segment the time-resolved analog signal and the experimentally acquired measurement signal according to a preset time window segmentation rule to obtain the analog value sequence and the measured value sequence of each time window. The weight matrix construction module is used to construct a depth-time window coupled weight matrix based on the time window and the photon path information; The overall error determination module is used to determine the overall error based on the simulated value sequence, the measured value sequence, and the depth-time window coupling weight matrix. The judgment module is used to determine whether the overall error meets the preset iteration termination condition; If the conditions are met, the current absorption coefficient will be used as the reconstructed absorption coefficient. If the conditions are not met, the sensitivity matrix is ​​updated based on the photon path information, and the current absorption coefficient is iteratively updated using the depth-time window coupling weight matrix and the sensitivity matrix. Based on the updated absorption coefficient and the photon path information, a new time-resolved analog signal is determined. The process then returns to the segmentation module until the preset iteration termination condition is met.

9. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the biological tissue absorption coefficient reconstruction method according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the biological tissue absorption coefficient reconstruction method according to any one of claims 1-7.

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