A simulation method, device, and computer for optical focusing through biological tissue

Abstract

An embodiment of the present invention discloses a simulation, device and computer for optical focusing through biological tissues, including: obtaining three-dimensional parameters of a fractal propagation model, and constructing a biological tissue with scattering characteristics based on the three-dimensional parameters; obtaining a transfer matrix from the biological tissue, and solving the transfer matrix by using the least squares method to obtain a pseudo-inverse matrix of the biological tissue; solving a two-dimensional incident light field according to the pseudo-inverse matrix. The present invention can provide a virtual biological tissue simulation platform for achieving good optical focusing effects, accurately and quickly simulate the wave effect of light and the propagation process of light inside a biological tissue body, solve and test the complex amplitude of the incident light field required, thereby reducing the cost of biological optical experiments and improving the test efficiency.

Description

Technical Field

[0001] The present invention relates to the field of computer simulation technology, and particularly to a simulation method, device and computer for optical focusing through biological tissues. Background Art

[0002] With the increasing attention of people to medical health, biomedical imaging technology, as a research and monitoring means capable of obtaining physiological and pathological information of organisms, has been widely used. In this technical field, compared with current conventional imaging methods such as nuclear magnetic resonance imaging and CT imaging, optical imaging technology has attracted much attention for its advantages of non-contact, rapidity, convenience, and high spatio-temporal resolution. Researchers are promoting the development of optical imaging technology in the field of biomedical imaging. In actual use, biological tissues are generally scattering media. When incident light waves pass through biological tissues, their phases will introduce random distortions, and the light transmission paths mainly show forward "snake-like" or diffused shapes, which will greatly reduce the focusing effect of the incident light, making it difficult to find the accurate focal plane or the energy at the focus becomes weak, resulting in the decline of key indicators such as imaging signal-to-noise ratio, imaging resolution, and penetration depth.

[0003] In order to quantitatively study and describe the transmission characteristics of light in biological tissues, some numerical models have been developed, which can be used for numerical simulation of the light propagation process at different scales (macroscopic, mesoscopic, microscopic) in biological tissues. Commonly used numerical models include Monte Carlo model (MC), Radiative Transfer Equation (RTE), finite-difference time-domain (FDTD), beam propagation model (BPM), etc. Compared with experimental research, numerical simulation can control experimental conditions singly to study a specific influencing factor. On the other hand, due to the complexity of the optical physical process, it is difficult to determine the boundary conditions. Even if some simplified conditions are used and the analytical solution of the light wave equation is directly obtained, the difficulty is still very high. Compared with obtaining the analytical solution, numerical simulation can not only maintain relatively high accuracy and precision but also be relatively convenient, and is one of the effective methods for studying the light conduction process in organisms.

[0004] However, when the above models are applied to simulate the propagation process of light inside biological tissues, they all exhibit their respective limitations. For example, the Monte Carlo (MC) model simulates the propagation behavior of a large number of photons inside an organism and tracks the propagation path of each photon. Therefore, the calculation is time-consuming, and this model is not suitable for simulating the wave characteristics of light. The radiative transfer equation (RTE) can analyze the diffusion propagation process of light energy in tissues. However, its first-order approximation solution is only applicable to describe the light radiation distribution at the macroscopic scale, such as leg muscles, arm muscles, pectoralis major, etc. To apply it at the microscopic scale, higher-order approximations can also be used. However, the computational amount will increase non-linearly with the increase in the order. The finite-difference time-domain (FDTD) simulation also has the problems of large computational amount and long computational time, and its computational accuracy is significantly limited by the computer memory capacity. Compared with the above models, the beam propagation model (BPM) has relatively less computational time. Its disadvantage is that it ignores the backscattering propagation effect of the medium. However, for biological tissues, since forward scattering propagation dominates in biological tissues, BPM simulation is also applicable to the optical simulation of biological tissues.

[0005] There have been quite a number of reports on the application research of BPM simulation in tissue optics, and a series of variants have been developed. The Biobeam simulation model is a newly developed variant in recent years. It uses the idea of BPM to numerically simulate the imaging of a scattering tissue through an optical microscope, and can reproduce the wave optical effects occurring inside the sample, such as optical speckles. In terms of computational time, due to the use of parallel programming technology based on the Pyopencl library, its computational speed has been greatly improved. The fractal propagation model (FPM) is also a variant developed using the idea of BPM in recent years. Its highlight is to use a refractive index distribution with fractal characteristics to simulate complex biological tissues. Compared with the traditional simulation method that uses spherical particles of arbitrary granularity to represent tissues, the former is more in line with the experimental results of forming speckle images through biological tissues. Using the FPM-Biobeam joint simulation can effectively utilize their respective advantages to simulate the biological tissue imaging process with a relatively fast computational speed and high accuracy. However, there is currently no application research on biological imaging in solving the incident light wavefront to achieve optical focusing. Summary of the Invention

[0006] Embodiments of the present invention provide a simulation method, device, and computer device for optical focusing through biological tissues.

[0007] A simulation method for optical focusing through biological tissues, characterized by comprising:

[0008] Obtain the three-dimensional parameters of the fractal propagation model, and construct a biological tissue with scattering characteristics through the three-dimensional parameters;

[0009] Obtain the transfer matrix from the biological tissue, and use the least squares method to solve the transfer matrix to obtain the pseudo-inverse matrix of the biological tissue;

[0010] Solve the two-dimensional incident light field according to the pseudo-inverse matrix.

[0011] An optical focusing simulation device through biological tissue, characterized by comprising:

[0012] An acquisition module, configured to acquire three-dimensional parameters of a fractal propagation model, and construct a biological tissue with scattering characteristics through the three-dimensional parameters;

[0013] A processing module, configured to obtain a transfer matrix from the biological tissue, and use the least squares method to solve the transfer matrix to obtain the pseudo-inverse matrix of the biological tissue;

[0014] An execution module, configured to solve the two-dimensional incident light field according to the pseudo-inverse matrix.

[0015] A computer device, comprising a memory and a processor, wherein computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the optical focusing method through biological tissue as described above.

[0016] A storage medium storing computer-readable instructions, wherein when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the optical focusing method through biological tissue as described above.

[0017] The beneficial effects of the embodiments of the present invention are: The present invention can provide a virtual biological tissue simulation platform for achieving good optical focusing effects, accurately and quickly simulate the wave effect of light and the propagation process of light inside the biological tissue body, solve and test the complex amplitude of the incident light field required, thereby reducing the cost of biological optical experiments and improving the test efficiency. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] Figure 1 A flowchart of an optical focusing simulation method through biological tissue provided for the embodiments of the present invention;

[0020] Figure 2 A schematic diagram of a refractive index cross-sectional distribution obtained using a fractal propagation model provided for the embodiments of the present invention;

[0021] Figure 3 Schematic diagram of establishing mutually orthogonal unit column vectors required for embodiments of the present invention;

[0022] Figure 4 Schematic diagram of the principle calculated using the biobeam model for embodiments of the present invention;

[0023] Figure 5 Schematic diagram of inputting an N*N-dimensional input optical field into the Biobeam model for embodiments of the present invention;

[0024] Figure 6 and Figure 7 Schematic diagram of the process of dimension reduction processing for the N*N-dimensional output optical field for embodiments of the present invention;

[0025] Figure 8 Schematic diagram of the two-dimensional output optical field for embodiments of the present invention;

[0026] Figure 9 Schematic diagram of the two-dimensional input optical field for embodiments of the present invention. Detailed implementation manners

[0027] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0028] As Figure 1 The present invention provides a simulation method for optical focusing through biological tissues, including:

[0029] S1. Obtain three-dimensional parameters of the fractal propagation model, and construct a biological tissue with scattering characteristics through the three-dimensional parameters;

[0030] S2. Obtain a transfer matrix from the biological tissue, and solve the transfer matrix using the least squares method to obtain the pseudo-inverse matrix of the biological tissue;

[0031] S3. Solve the two-dimensional incident optical field according to the pseudo-inverse matrix.

[0032] The present invention can provide a virtual biological tissue simulation platform for achieving good optical focusing effects, accurately and quickly simulate the wave effect of light and the propagation process of light inside the biological tissue body, solve and test the complex amplitude of the incident optical field required, thereby reducing the cost of biological optical experiments and improving the test efficiency.

[0033] In one embodiment of the present invention, constructing a biological tissue with scattering characteristics through the three-dimensional parameters in step S1 includes:

[0034] Step 1: Assign values to the three-dimensional parameters;

[0035] Step 2: Use the fractal propagation model to calculate the three-dimensional parameters respectively to obtain a three-dimensional spatial distribution with different refractive indices, so as to form a biological tissue with scattering characteristics.

[0036] Specifically, the three-dimensional parameters of the fractal propagation model are the fractal dimension, the correlation length, and the refractive index fluctuation amount. By assigning values to them and substituting them into the formula of the fractal propagation model to solve the spatial refractive index distribution, please refer to Figure 2 , Figure 2 which is a refractive index cross-sectional distribution obtained by using the fractal propagation model in this embodiment.

[0037] In this embodiment, according to the basic principle of the fractal propagation model (FPM) theory, the formula of the fractal propagation model is as follows:

[0038]

[0039]

[0040]

[0041] Among them, represents the refractive index fluctuation amount, D f represents the fractal dimension, l c represents the correlation length, (x, y, z) represents a point in the spatial domain of the biological tissue model, represents the second-kind Bessel function, (k x , k y , k z ) represents a point in the frequency domain, and R represents a three-dimensional number set subject to a Gaussian 0-1 distribution. Among them, φ(k x , k y , k z ) represents the power spectral density, and Δn(x, y, z) represents the three-dimensional spatial distribution of the refractive index difference.

[0042] In an embodiment of the present invention, in step S2, obtaining the transmission matrix from the biological tissue includes:

[0043] Step 1: Establish mutually orthogonal unit column vectors, and perform dimension elevation processing on the unit column vectors to obtain a plurality of input optical fields;

[0044] Step 2: Input the plurality of input optical fields into the Biobeam model to obtain an output optical field;

[0045] Step 3: Perform dimensionality reduction on the obtained multiple output optical fields to obtain multiple column vectors, and form the transmission matrix of the biological tissue with the obtained multiple column vectors, where the transmission matrix represents the optical propagation characteristics of the biological tissue.

[0046] Specifically, please refer to Figure 3 to establish the required mutually orthogonal unit column vectors The expression is as follows:

[0047]

[0048] Specifically, can be: …,

[0049] Specifically, perform dimensionality increase on to obtain an N×N-dimensional input optical field U in (m) as the input of the Biobeam model. Among them, the dimensionality increase method used needs to pass through the following steps:

[0050] (1) Cut the used N²×1-dimensional unit column vector into N sub-columns of the same length of N×1;

[0051] (2) Re-piece together the obtained sub-columns into an N×N-dimensional input optical field U in (m) ; and so on to obtain N² two-dimensional input optical fields U in (m) .

[0052] Please refer to Figure 5 , and use the obtained N² N×N-dimensional input optical fields U in (m) as the input of the Biobeam model, and call the API interface Bpm3d.propagate of the Biobeam software development kit to simulate the propagation process of the coherent light beam passing through the scattering medium, so as to obtain an N×N-dimensional output optical field Record the N×N-dimensional output optical field obtained by each calculation of the Biobeam model Finally, N² two-dimensional output optical fields will be recorded

[0053] Among them, for the used biobeam model, the schematic diagram of its principle is as shown in Figure 4 .

[0054] Specifically, the applicable conditions of the biobeam model used are monochromatic light illumination and relatively small refractive index differences. The scattered light mainly propagates forward in the medium, and in this regard, biological tissues generally meet the requirements.

[0055] Specifically, the main theoretical formula of the Biobeam model used is as follows:

[0056]

[0057] Among them, u(x, y, z + Δz) is the transverse distribution of the complex amplitude of the light field corresponding to the ordinate z + Δz, and u(x, y, z) is the transverse distribution of the complex amplitude of the light field corresponding to the ordinate z. k x 、k y are the x and y components of the wave vector, and are the Fourier transform operator and the inverse Fourier transform operator respectively.

[0058] In an embodiment of the present invention, the software program package of the biobeam model includes:

[0059] (1) Main input parameters: two-dimensional input light field U in (m) 、wavelength λ, objective NA parameter;

[0060] (2) Main call function: API interface Bpm3d.propagate;

[0061] (3) Main output parameter: two-dimensional output light field

[0062] Please refer to Figure 6 、 7 and perform dimensionality reduction processing on to obtain an N^2 * 1-dimensional column vector as each column t of the N^2 * N^2-dimensional transmission matrix T, i specifically including the following methods:

[0063] (1) Cut the obtained two-dimensional output light field into N N * 1-dimensional sub-columns with the same length;

[0064] (2) According to the principle of connecting head to tail, in the adjacent two columns of , always connect the head of the latter sub-column to the tail of the former sub-column to obtain an N^2 * 1-dimensional complex column vector as the output light field column vector And so on, finally obtaining N^2 N^2 * 1-dimensional output light field column vectors

[0065] (3) The obtained from the above steps Concatenate them in sequence, and for each output optical field column vector serve as each column t of the transmission matrix T i , and concatenate t i to finally obtain a complete N^2*N^2 dimensional transmission matrix T, representing the optical propagation characteristics of the said biological tissue.

[0066] In this embodiment, the program implementation for obtaining the transmission matrix of the said biological tissue needs to include the following parts in the Python environment: (1) Declare the input optical field column vector and define it using the numpy library function; (2) Change the dimension of the input optical field column vector to obtain a two-dimensional array and assign it to U in (m) ; (3) Import the Bpm3d class of the Biobeam software development kit and create a new object, and initialize its attributes (the main attributes include the geometric size of the object, the three-dimensional refractive index distribution, and the incident light wavelength); (4) Call the propagate method of the Bpm3d class object, assign the U in (m) obtained in the above steps to the incident optical field parameter required by this method, and return the obtained through the simulation calculation of the Biobeam model ; (5) Save the

[0067] obtained in the above steps. In this embodiment, the program implementation for obtaining the transmission matrix of the said biological tissue also needs to include the following parts in the Matlab environment: (1) Read the obtained in the previous steps ; (2) Change the dimension of to transform it into an output optical field column vector

[0068] ; (3) Concatenate all the output optical field column vectors to obtain a complete two-dimensional transmission matrix T.

[0069]

[0070] In one embodiment of the present invention, to solve the pseudo-inverse matrix of the transmission matrix, the least squares method can be used to solve the pseudo-inverse matrix of the transmission matrix T , where is its pseudo-inverse matrix, E is the identity matrix with the same dimension as T, represents the Euclidean distance.

[0071] Specifically, the program needs to go through the following steps to be implemented: Use the built-in function lsqminnorm in Matlab to solve the pseudo-inverse matrix And use the built-in function eye to represent an identity matrix E.

[0072] In one embodiment of the present invention, in step S2, solving the two-dimensional incident light field according to the pseudo-inverse matrix includes:

[0073] Step 1: Select column vectors from the pseudo-inverse matrix;

[0074] Step 2: Perform dimensionality elevation processing on the column vectors to obtain the incident light field.

[0075] Specifically, when solving the N*N-dimensional incident light field U i it includes: Select the required column t from the obtained pseudo-inverse matrix ; Perform dimensionality elevation processing on the column t j ; to obtain the required N*N-dimensional incident light field U j i .

[0076] Specifically, in this embodiment, selecting the required column t from the obtained pseudo-inverse matrix includes methods for single-point focusing applications and multi-point focusing applications. j

[0077] Among them, when the target of the simulation is single-point focusing, selecting column vectors from the pseudo-inverse matrix includes: determining the desired position of the single point to be focused in the two-dimensional output light field plane; calculating the column index of the column vector required to achieve single-point focusing in the pseudo-inverse matrix according to the desired position; extracting the corresponding column vector from the pseudo-inverse matrix according to the column index.

[0078] Specifically, (1) Determine the desired position of the single point to be focused in the two-dimensional output light field plane U o ; Calculate the column index of the column necessary to achieve single-point focusing in the obtained pseudo-inverse matrix according to the position, where the position of the single point to be focused and the column index of the required column have the following relationship:

[0079] j = N*X + Y

[0080] where j is the column index of the required column in the obtained pseudo-inverse matrix , N is the number of rows (columns) of the two-dimensional output light field U o ; X is the row index of the single point to be focused in its plane, and Y is the column index of the single point to be focused in its plane; (2) Extract the required column from the pseudo-inverse matrix according to the calculated column index j . ​​

[0081] In another embodiment, when the target of the simulation is multi-point focusing, selecting column vectors from the pseudo-inverse matrix includes: determining the desired positions of multiple points to be focused in the two-dimensional output optical field plane; calculating the column indices of the column vectors required to achieve multi-point focusing in the pseudo-inverse matrix according to the desired positions of the multiple points; extracting the corresponding first column vector and the last column vector from the pseudo-inverse matrix according to the calculated column index of the first column vector and the column index of the last column vector; and summing all the column vectors between the first column vector and the last column vector to obtain the column vector required for multi-point focusing.

[0082] Specifically, (1) Determine the desired positions of each of the multiple points to be focused in the two-dimensional output optical field plane U o inside, and calculate the column indices in the obtained pseudo-inverse matrix of the columns necessary to achieve multi-point focusing according to their respective positions, where there is the following relationship between the positions of the multiple points to be focused and the column indices of the required columns:

[0083] j 1 = N*X 1 + Y 1 ,..., j n = N*X n + Y n

[0084] where j 1 , j n are respectively the column indices in the pseudo-inverse matrix of the columns necessary to achieve single-point focusing of the first point and the nth point, N is the number of rows (columns) of the two-dimensional output optical field U , X o , X 1 are respectively the row indices of the first point and the nth point in the two-dimensional output optical field plane U n , Y o are respectively the column indices of the first point and the nth point in the two-dimensional output optical field plane U 1 , Y n are respectively the column indices of the first point and the nth point in the two-dimensional output optical field plane U o ; (2) Extract the required columns 1 , j n from the pseudo-inverse matrix according to the calculated column indices; (3) Sum the multiple columns obtained in the above steps ... ... to obtain the column vector t j necessary to achieve multi-point focusing.

[0085] For an embodiment of the present invention, please refer to Figure 8 and Figure 9 ​, performing dimensionality elevation processing on the column vector to obtain the incident light field, including: cutting the selected column vector into sub-column vectors with the same length; and re-splicing the multiple sub-column vectors to form the input light field.

[0086] Specifically, (1) cutting the obtained N^2*1-dimensional column vector t j into N sub-columns with the same length of N*1 dimension; (2) re-splicing the sub-columns obtained in the previous step into an N*N-dimensional input light field U i .

[0087] Specifically, regarding the program implementation of this embodiment, the following parts are written in Matlab: (1) solving for the column index j of the required column t in the pseudo-inverse matrix through matrix and vector operations j ; (2) selecting the required column t j according to the column index j; (3) performing dimensionality elevation processing on the selected column t j to obtain the two-dimensional input light field U i .

[0088] The embodiment of the present invention also provides a simulation device for optical focusing through biological tissue, including: an acquisition module, configured to acquire three-dimensional parameters of the fractal propagation model and construct biological tissue with scattering characteristics through the three-dimensional parameters; a processing module, configured to acquire a transfer matrix from the biological tissue and solve the transfer matrix using the least squares method to obtain the pseudo-inverse matrix of the biological tissue; and an execution module, configured to solve the two-dimensional incident light field according to the pseudo-inverse matrix.

[0089] In some embodiments, the acquisition module includes: a first acquisition sub-module, configured to acquire the three-dimensional parameters of the fractal propagation model and assign values to the three-dimensional parameters; a first processing sub-module, configured to use the fractal propagation model to calculate the three-dimensional parameters respectively to obtain a three-dimensional spatial distribution with different refractive indexes to form biological tissue with scattering characteristics.

[0090] In some embodiments, the processing module includes: a second acquisition sub-module, configured to establish mutually orthogonal unit column vectors and perform dimensionality elevation processing on the unit column vectors to obtain multiple input light fields; a second processing sub-module, configured to input the multiple input light fields into the Biobeam model to obtain output light fields; a first execution sub-module, configured to perform dimensionality reduction processing on the obtained multiple output light fields to obtain multiple column vectors, and form the transfer matrix of the biological tissue with the obtained multiple column vectors, where the transfer matrix represents the optical propagation characteristics of the biological tissue.

[0091] In some embodiments, the execution module includes: a third acquisition sub-module, configured to select column vectors from the pseudo-inverse matrix; and a third processing sub-module, configured to perform dimensionality elevation processing on the column vectors to obtain the incident light field.

[0092] In some embodiments, when the simulation target is single-point focusing, the third acquisition sub-module includes: a fourth acquisition sub-module, configured to determine the desired position of the single point to be focused on in the two-dimensional output light field plane; a fourth processing sub-module, configured to calculate the column index of the column vector required to achieve single-point focusing in the pseudo-inverse matrix according to the desired position; and a second execution sub-module, configured to extract the corresponding column vector from the pseudo-inverse matrix according to the column index.

[0093] In some embodiments, when the simulation target is multi-point focusing, the third acquisition sub-module includes: a fifth acquisition sub-module, configured to determine the desired positions of the multiple points to be focused on in the two-dimensional output light field plane; a fifth processing sub-module, configured to calculate the column indices of the column vectors required to achieve multi-point focusing in the pseudo-inverse matrix according to the desired positions of the multiple points; a sixth processing sub-module, configured to extract the corresponding first column vector and last column vector from the pseudo-inverse matrix according to the calculated column index of the first column vector and the column index of the last column vector; and a third execution sub-module, configured to sum all the column vectors between the first column vector and the last column vector to obtain the column vector required for multi-point focusing.

[0094] In some embodiments, the third processing sub-module includes: a seventh processing sub-module, configured to cut the selected column vectors into sub-column vectors of the same length; and a fourth execution sub-module, configured to splice the multiple sub-column vectors again to form the input light field.

[0095] To solve the above technical problems, an embodiment of the present invention further provides a computer device. The computer device includes a processor, a non-volatile storage medium, a memory, and a network interface connected through a system bus. Among them, the non-volatile storage medium of the computer device stores an operating system, a database, and computer-readable instructions. The database may store a control information sequence. When the computer-readable instructions are executed by the processor, the processor can implement an image processing method. The processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute an image processing method. The network interface of the computer device is used to connect and communicate with a terminal.

[0096] In this embodiment, the processor is used to execute the specific contents of the acquisition module, the processing module, and the execution module. The memory stores the program codes and various types of data required to execute the above modules. The network interface is used for data transmission between the user terminal and the server. The memory in this embodiment stores the program codes and data required to execute all sub-modules in the image processing method, and the server can call the program codes and data of the server to execute the functions of all sub-modules.

[0097] The present invention also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors are caused to execute the steps of the image processing method described in any one of the above embodiments.

[0098] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the foregoing storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0099] It should be understood that although the steps in the flowchart of the accompanying drawings are shown in sequence according to 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 limitation, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same moment, but can be executed at different moments. Their execution order is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0100] The above are only some embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A simulation method for optical focusing through biological tissues, characterized in that, comprising: Obtaining three-dimensional parameters of a fractal propagation model, and constructing a biological tissue with scattering characteristics through the three-dimensional parameters; Obtaining a transfer matrix from the biological tissue, and solving the transfer matrix by using the least squares method to obtain the pseudo-inverse matrix of the biological tissue; Solving a two-dimensional incident light field according to the pseudo-inverse matrix; Wherein, the obtaining the transfer matrix from the biological tissue includes: Establishing mutually orthogonal unit column vectors, and performing dimensionality elevation processing on the unit column vectors to obtain a plurality of input light fields; Inputting the plurality of input light fields into a Biobeam model to obtain output light fields; Performing dimensionality reduction processing on the obtained plurality of output light fields to obtain a plurality of column vectors, and forming the transfer matrix of the biological tissue by the obtained plurality of column vectors, wherein the transfer matrix represents the optical propagation characteristics of the biological tissue; The solving the two-dimensional incident light field according to the pseudo-inverse matrix includes: Selecting a column vector from the pseudo-inverse matrix; Performing dimensionality elevation processing on the column vector to obtain the incident light field; When the simulation target is single-point focusing, the selecting a column vector from the pseudo-inverse matrix includes: Determining the desired position of the single point to be focused in the two-dimensional output light field plane; Calculating the column index of the column vector required for single-point focusing in the pseudo-inverse matrix according to the desired position; Extracting the corresponding column vector from the pseudo-inverse matrix according to the column index; When the simulation target is multi-point focusing, the selecting a column vector from the pseudo-inverse matrix includes: Determining the desired positions of the multiple points to be focused in the two-dimensional output light field plane; Calculating the column indexes of the column vectors required for multi-point focusing in the pseudo-inverse matrix according to the desired positions of the multiple points; Extracting the corresponding first column vector and last column vector from the pseudo-inverse matrix according to the column index of the first column vector and the column index of the last column vector calculated; Summing all the column vectors between the first column vector and the last column vector to obtain the column vector required for multi-point focusing.

2. The simulation method according to claim 1, characterized in that, the constructing a biological tissue with scattering characteristics through the three-dimensional parameters includes: Obtaining the three-dimensional parameters of the fractal propagation model, and assigning values to the three-dimensional parameters; Using the fractal propagation model to calculate the three-dimensional parameters respectively to obtain a three-dimensional spatial distribution with different refractive indexes to form a biological tissue with scattering characteristics.

3. The simulation method according to claim 1, characterized in that, the performing dimensionality elevation processing on the column vector to obtain the incident light field includes: Cutting the selected column vector into sub-column vectors with the same length; Re-splicing the multiple sub-column vectors to form an incident light field.

4. A simulation device for optical focusing through biological tissues, characterized in that, comprising: An obtaining module, configured to obtain three-dimensional parameters of a fractal propagation model, and construct a biological tissue with scattering characteristics through the three-dimensional parameters; A processing module, configured to obtain a transfer matrix from the biological tissue and solve the transfer matrix by using the least squares method to obtain the pseudo-inverse matrix of the biological tissue; An execution module, configured to solve a two-dimensional incident light field according to the pseudo-inverse matrix; Wherein, the processing module includes: A second acquisition sub-module, configured to establish mutually orthogonal unit column vectors, and perform dimensionality elevation processing on the unit column vectors to obtain a plurality of input light fields; A second processing sub-module, configured to input the plurality of input light fields into a Biobeam model to obtain an output light field; A first execution sub-module, configured to perform dimensionality reduction processing on the obtained plurality of output light fields to obtain a plurality of column vectors, and form the transfer matrix of the biological tissue by using the obtained plurality of column vectors, wherein the transfer matrix represents the optical propagation characteristics of the biological tissue; The execution module includes: A third acquisition sub-module, configured to select column vectors from the pseudo-inverse matrix; a third processing sub-module, configured to perform dimensionality elevation processing on the column vectors to obtain the incident light field; When the simulation target is single-point focusing, the third acquisition sub-module includes: A fourth acquisition sub-module, configured to determine an expected position of a single point to be focused on in a two-dimensional output light field plane; A fourth processing sub-module, configured to calculate a column index of a column vector required to achieve single-point focusing in the pseudo-inverse matrix according to the expected position; A second execution sub-module, configured to extract a corresponding column vector from the pseudo-inverse matrix according to the column index; When the simulation target is multi-point focusing, the third acquisition sub-module includes: A fifth acquisition sub-module, configured to determine expected positions of a plurality of points to be focused on in a two-dimensional output light field plane; A fifth processing sub-module, configured to calculate column indexes of column vectors required to achieve multi-point focusing in the pseudo-inverse matrix according to the expected positions of the plurality of points; A sixth processing sub-module, configured to extract corresponding first and last column vectors from the pseudo-inverse matrix according to the calculated column index of the first column vector and the column index of the last column vector; A third execution sub-module, configured to sum all column vectors between the first column vector and the last column vector to obtain a column vector required for multi-point focusing.

5. A computer device, including a memory and a processor, wherein computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the method for optical focusing through biological tissue according to any one of claims 1 to 3.

6. A storage medium storing computer-readable instructions, wherein when the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method for optical focusing through biological tissue according to any one of claims 1 to 3.

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