Matching layer material parameter prediction method and device, electronic equipment and storage medium

Abstract

The application discloses a matching layer material parameter prediction method and device, electronic equipment and a storage medium. The sound velocity matrix of the matching layer, the density matrix of the matching layer and preset sound source information are used to obtain sound pressure distribution information in a working field. The sound pressure distribution information is input into an initial matching layer material parameter prediction model to obtain a training material parameter matrix. The initial matching layer material parameter prediction model is updated based on the training material parameter matrix and a filling ratio matrix to obtain a trained matching layer material parameter prediction model. Target skull sound pressure information is input into the trained matching layer material parameter prediction model to obtain a target material parameter matrix. The target material parameter matrix of the matching layer can be obtained based on the target skull sound pressure information. The matching layer made based on the target material parameter matrix can make the sound pressure information in the skull meet the target skull sound pressure information, and the efficiency is high without manual calculation based on experience.

Description

Technical Field

[0001] The present application relates to the technical field of acoustic impedance matching materials, and in particular to a matching layer material parameter prediction method, device, electronic device, and storage medium. Background Art

[0002] Transcranial ultrasound is a method that uses ultrasonic energy to penetrate the skull and directly target lesions within the brain. It primarily utilizes high-intensity focused ultrasound (HIFU) technology, which emits low-energy ultrasound waves through an external transducer. This energy is focused on the target area within the body, generating instantaneous high temperatures that trigger thermal, mechanical, and cavitation effects.

[0003] In related art, transcranial ultrasound is optimized using a matching layer to ensure that the sound pressure information within the skull meets the required sound pressure. For example, matching layer parameters are manually calculated based on empirical data from the impedance data of the transducer surface, and then the matching layer is manufactured based on these matching layer parameters. However, when using matching layers manufactured using these manually calculated matching layer parameters, the sound pressure within the skull is difficult to meet the required sound pressure. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a matching layer material parameter prediction method, device, electronic device, and storage medium. These methods can generate a target material parameter matrix for the matching layer based on target skull sound pressure information. A matching layer fabricated based on this target material parameter matrix can ensure that the sound pressure information within the skull meets the target skull sound pressure information.

[0005] To achieve the above objectives, a first embodiment of the present application provides a matching layer material parameter prediction method, comprising:

[0006] constructing a skull model, and constructing a working domain including the skull model;

[0007] Based on the skull model, determining a matching layer region in the working domain, dividing the matching layer region into a plurality of grids of equal size and connected to each other, configuring a filling ratio for each grid to obtain a filling ratio matrix, wherein the filling ratio represents a material parameter within the grid;

[0008] Based on the filling ratio, the sound velocity and density of each grid are calculated respectively to obtain a matching layer sound velocity matrix and a matching layer density matrix;

[0009] Obtaining sound pressure distribution information in the working domain based on the matching layer sound velocity matrix, the matching layer density matrix, and preset sound source information;

[0010] inputting the sound pressure distribution information into an initial matching layer material parameter prediction model to obtain a training material parameter matrix, updating the initial matching layer material parameter prediction model based on the training material parameter matrix and the filling ratio matrix to obtain a trained matching layer material parameter prediction model;

[0011] obtaining target skull sound pressure information, inputting the target skull sound pressure information into the trained matching layer material parameter prediction model to obtain a target material parameter matrix.

[0012] According to some embodiments of the present application, the skull model is constructed, including:

[0013] obtaining computer tomography images of a plurality of skull slices with different thicknesses scanned at different angles;

[0014] determining Hounsfield unit values of a plurality of positions of the skull based on the computer tomography images;

[0015] calculating a Hounsfield unit average value based on the Hounsfield unit values of the plurality of positions of the skull, and constructing the skull model based on the Hounsfield unit average value; wherein the Hounsfield unit value of each position of the skull model is the Hounsfield unit average value.

[0016] According to some embodiments of the present application, the filling ratio is configured for each grid to obtain a filling ratio matrix, including:

[0017] setting a value range of the filling ratio;

[0018] determining a starting column grid in the plurality of grids, randomly selecting a value from the value range as the filling ratio of the starting column grid, and making the filling ratios of the grids in each column increase or decrease column by column in the value range; wherein the filling ratios of the grids in the same column are the same.

[0019] According to some embodiments of the present application, the number of columns of the grids in the matching layer region is N;

[0020] the randomly selecting a value from the value range as the filling ratio of the starting column grid, and making the filling ratios of the grids in each column increase or decrease column by column in the value range, includes:

[0021] randomly selecting a value X1 from the value range as the filling ratio of the starting column grid, and calculating the filling ratios of the grids in the 2nd column to the Nth column according to a first calculation formula, the first calculation formula being:

[0022]

[0023] wherein X iX represents the fill ratio of the i-th column grid, i is 2, 3, …, N. i-1 X represents the fill ratio of the i-1-th column grid, i is 2, 3, …, N; c is a preset attenuation factor.

[0024] According to some embodiments of the present application, the calculation of the fill ratio of the second column grid to the Nth column grid according to the first calculation formula comprises:

[0025] detecting X i whether the value range is exceeded, in the case of detecting X i in the case of exceeding the value range, increasing the preset attenuation factor in the first calculation formula, and recalculating X i so that X i is within the value range; X i X represents the fill ratio of the i-th column grid, i is 2, 3, …, N.

[0026] According to some embodiments of the present application, the sound pressure distribution information is input into an initial matching layer material parameter prediction model to obtain a training material parameter matrix, comprising:

[0027] cutting the skull surrounding sound pressure information around the skull model from the sound pressure distribution information;

[0028] inputting the skull surrounding sound pressure information into the initial matching layer material parameter prediction model to obtain the training material parameter matrix.

[0029] According to some embodiments of the present application, before obtaining the sound pressure distribution information in the working domain based on the matching layer sound velocity matrix, the matching layer density matrix and the preset sound source information, further comprising:

[0030] setting the focal point position of the sound source information around the skull model;

[0031] the cutting of the skull surrounding sound pressure information around the skull model from the sound pressure distribution information comprises:

[0032] cutting the focal point surrounding sound pressure information around the focal point position from the sound pressure distribution information as the skull surrounding sound pressure information.

[0033] To achieve the above object, the second aspect embodiment of the present application provides a matching layer material parameter prediction device, comprising:

[0034] a construction module for constructing a skull model and constructing a working domain comprising the skull model;

[0035] a configuration module, configured to determine a matching layer region in the working domain based on the skull model, divide the matching layer region into a plurality of equally sized and interconnected grids, and configure a fill ratio for each grid to obtain a fill ratio matrix; wherein the fill ratio represents a material parameter within the grid;

[0036] A calculation module, configured to calculate the sound velocity and density of each grid based on the filling ratio to obtain a matching layer sound velocity matrix and a matching layer density matrix;

[0037] a sound pressure determination module, configured to obtain sound pressure distribution information in the working domain based on the matching layer sound velocity matrix, the matching layer density matrix, and preset sound source information;

[0038] a training module, configured to input the sound pressure distribution information into an initial matching layer material parameter prediction model to obtain a training material parameter matrix, and update the initial matching layer material parameter prediction model based on the training material parameter matrix and the filling ratio matrix to obtain a trained matching layer material parameter prediction model;

[0039] The application module is used to obtain target skull sound pressure information, input the target skull sound pressure information into the trained matching layer material parameter prediction model, and obtain a target material parameter matrix.

[0040] To achieve the above-mentioned objectives, an embodiment of the third aspect of the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the matching layer material parameter prediction method described in any one of the embodiments of the first aspect is implemented.

[0041] To achieve the above-mentioned objectives, a fourth embodiment of the present application provides a computer-readable storage medium, wherein the storage medium stores a computer program. When the computer program is executed by a processor, the matching layer material parameter prediction method described in any one of the first embodiment is implemented.

[0042] The matching layer material parameter prediction method, device, electronic equipment and storage medium provided in the embodiments of the present application first make a training sample of a matching layer material parameter prediction model, specifically, a skull model is constructed and a working domain including the skull model is constructed, then a matching layer region is determined in the working domain, the matching layer region is divided into a plurality of grids of equal size and connected to each other, a filling ratio is configured for each grid to obtain a filling ratio matrix; since the filling ratio represents the material parameters in the grid, configuring the filling ratio for each grid can be used to simulate the characteristics of the real matching layer; then the sound speed and density of each grid are calculated respectively to obtain a matching layer sound speed matrix and a matching layer density matrix; since the transmission of ultrasonic waves in a medium is mainly related to the sound speed and density of the medium, based on the matching layer sound speed matrix, the matching layer density matrix and preset sound source information, the sound pressure distribution information in the working domain can be obtained, and the sound pressure distribution information is used as the training sample of the matching layer material parameter prediction model. The sound pressure distribution information is input into the initial matching layer material parameter prediction model to obtain a training material parameter matrix, the initial matching layer material parameter prediction model is updated based on the training material parameter matrix and the filling ratio matrix to obtain a trained matching layer material parameter prediction model; target skull sound pressure information is obtained, and the target skull sound pressure information is input into the trained matching layer material parameter prediction model to obtain a target material parameter matrix. In this way, the present application can obtain the target material parameter matrix of the matching layer based on the target skull sound pressure information, and the matching layer made based on the target material parameter matrix can make the sound pressure information in the skull meet the target skull sound pressure information, and manual calculation based on experience is not required, and the efficiency is high.

[0043] Additional aspects and advantages of the present application will be made apparent by the following description and the specific descriptions. BRIEF DESCRIPTION OF DRAWINGS

[0044] The present application will be further described below in conjunction with the drawings and embodiments, in which:

[0045] Figure 1 A flowchart of the matching layer material parameter prediction method of the embodiments of the present application is shown in FIG. 1;

[0046] Figure 2 A schematic diagram of a part of the working domain of the embodiments of the present application is shown in FIG. 2;

[0047] Figure 3 A schematic diagram of simulation by a water crystal cell and an aluminum crystal cell of the embodiments of the present application is shown in FIG. 3;

[0048] Figure 4 A schematic diagram of the relationship between the filling ratio and the equivalent sound speed and equivalent density is shown in FIG. 4;

[0049] Figure 5 A schematic diagram of the relationship between the filling ratio and the equivalent sound speed and equivalent density is shown in FIG. 4; Figure 1A specific flow chart of step S110;

[0050] Figure 6 for Figure 1 A specific flow chart of step S120 in FIG.

[0051] Figure 7 for Figure 1 A specific flow chart of step S150;

[0052] Figure 8 This is a schematic diagram of target skull sound pressure information according to an embodiment of the present application;

[0053] Figure 9 Schematic diagram of the first sound pressure information of a skull without a matching layer under the action of a preset focused sound field;

[0054] Figure 10 for Figure 9 Schematic diagram of the sound pressure at the focus of the intersection;

[0055] Figure 11 A schematic diagram of the second sound pressure information of a skull provided with a target matching layer under the action of a preset focused sound field;

[0056] Figure 12 for Figure 11 Schematic diagram of the sound pressure at the focus of the intersection;

[0057] Figure 13 This is a schematic structural diagram of a matching layer material parameter prediction device according to an embodiment of the present application;

[0058] Figure 14 Schematic diagram of the hardware structure of the electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0059] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0060] In the description of this application, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on this application.

[0061] In the description of this application, "several" means more than one, "plurality" means more than two, "greater than," "less than," and "exceed" are understood to exclude the number itself, while "above," "below," and "within" are understood to include the number itself. The use of "first" and "second" in the description is solely for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance, implicitly specifying the number of the indicated technical features, or implicitly specifying the order of the indicated technical features.

[0062] In the description of this application, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in this application based on the specific content of the technical solution.

[0063] In the description of this application, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples.

[0064] First, let’s analyze some of the terms used in this application:

[0065] Matching layer: A key concept in transcranial ultrasound, the matching layer is located between the transducer and the skull, improving the propagation efficiency of ultrasound waves between the two media (typically water and skull). Because the skull strongly attenuates and absorbs ultrasound waves, direct penetration through the skull results in significant loss of ultrasound energy. By selecting a material with appropriate acoustic impedance and speed, the matching layer minimizes reflection and scattering of ultrasound waves as they pass through it, thereby improving ultrasound transmittance and focusing.

[0066] Transducer: A key component in transcranial ultrasound systems, the transducer converts electrical energy into ultrasonic energy and, through focusing technology, directs the ultrasonic energy to the target area within the body. Transducer performance directly impacts the focusing effect and therapeutic efficiency of the ultrasound. Advances in phased transducer technology are enabling more precise ultrasound focusing and more efficient energy transmission.

[0067] Skull transmittance: Skull transmittance refers to the proportion of ultrasound energy remaining after penetrating the skull. Due to the skull's attenuation and absorption of ultrasound waves, the energy of ultrasound waves is significantly reduced after penetrating the skull. Skull transmittance directly impacts the effectiveness of transcranial ultrasound therapy. To improve skull transmittance, various measures can be taken, including optimizing matching layer design, selecting appropriate ultrasound frequency and power, and improving transducer focusing technology.

[0068] Sound pressure: Sound pressure refers to the change in atmospheric pressure caused by sound wave disturbance, that is, the residual pressure of atmospheric pressure. It is equivalent to superimposing a pressure change caused by sound wave disturbance on the atmospheric pressure. Specifically, when there is a sound field in a medium (such as air), the difference between the pressure at a certain point at a certain moment and the static pressure at that point when there is no sound wave is called the sound pressure at that point. The magnitude of sound pressure reflects the strength of the sound wave and is an important physical quantity used to describe sound waves in acoustics. The unit of sound pressure is Pascal (Pa), which is the basic unit of pressure in the International System of Units. In addition, sound pressure can also be expressed in other units, such as bar, where 1 bar is equal to 100KPa.

[0069] In the related art, ultrasound transcranial optimization technology is roughly divided into two types: improving the sound source and making a matching layer. For example, the time reversal method is used to obtain the sound source sequence information of the focused sound field, optimize the focused sound field, and overcome the phase mismatch. However, the impedance matching problem between the transducer, water, and skull has not been solved. Most of the energy focused by the ultrasound is still absorbed by the skull, resulting in low transmittance, which may not reach the focal energy intensity required for treatment. For example, a matching layer close to the transducer is made according to the impedance data of the transducer surface to overcome the impedance mismatch between water and the transducer surface. However, the impedance mismatch at the water-skull interface has not been solved, and a large amount of energy will still accumulate in the skull, resulting in too low transmittance. And there is currently no better way to quickly calculate the material parameters of different matching layers based on different skulls.

[0070] Based on this, embodiments of the present application provide a matching layer material parameter prediction method, apparatus, electronic device, and storage medium. These methods can determine a target material parameter matrix for the matching layer based on target skull sound pressure information. A matching layer fabricated based on this target material parameter matrix can ensure that the sound pressure information within the skull meets the target skull sound pressure information. The resulting matching layer can improve the focusing capability of transcranial ultrasound and enhance skull transmittance.

[0071] The matching layer material parameter prediction method of the embodiment of the present application can be applied to a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the matching layer material parameter prediction method, etc., but is not limited to the above forms.

[0072] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0073] Reference Figure 1 , the first embodiment of the present application provides a method for predicting matching layer material parameters. Figure 1 The illustrated method flow includes but is not limited to steps S110 to S160.

[0074] Step S110, constructing a skull model and a working domain including the skull model;

[0075] In one embodiment, in order to make the training samples produced subsequently conform to the actual situation, a skull model is constructed based on the real skull, and a working domain including the skull model is constructed. The working domain is used to simulate the distribution area of ​​ultrasound waves around the skull during transcranial ultrasound. Figure 2 , Figure 2 This is a schematic diagram of part of the working domain structure, where the skull model is located.

[0076] Step S120: Based on the skull model, determine a matching layer region in the working domain, divide the matching layer region into a plurality of equally sized and interconnected grids, and assign a fill ratio to each grid to obtain a fill ratio matrix; the fill ratio represents a material parameter within the grid.

[0077] It is worth noting that the matching layer area is the area for placing the matching layer. Generally, the transducer serves as an ultrasonic emission source, and the transducer is deployed on one side of the skull. The transducer transmits ultrasonic waves to the skull, forming ultrasonic distribution information around the skull. When performing transcranial ultrasound, the matching layer is usually located between the transducer and the skull. Therefore, in the embodiment of the present application, in the working domain, a portion of the area between the position of the transducer and the position of the skull model is selected as the matching layer area. For example, referring to Figure 2 A 36*10 grid is selected from the working domain as the matching layer area. The matching layer area consists of multiple grids of equal size and connected to each other. Each grid is an independent unit. There are 360 ​​independent units in the matching layer area that can be used to place different materials. From left to right, the sound velocity and impedance of each column of materials are larger than those of the previous column, showing a gradually increasing trend.

[0078] Step S130 , calculating the sound velocity and density of each grid based on the filling ratio to obtain a matching layer sound velocity matrix and a matching layer density matrix;

[0079] In one embodiment, the present application is based on the equivalent medium theory design of microspheres periodically arranged in water, so that a grid represents a material, and the material is simulated by crystal cells and aluminum cell, referring to Figure 3 , aluminum cells are filled in the crystal cells, and the aluminum cells are located in the center of the crystal cells. The filling ratio refers to the volume ratio of aluminum cells to crystal cells in a grid. The filling ratio is used to characterize the properties of the material. Figure 3 In the case of aa, the filling ratio is 2 / bb 2 The speed of sound refers to the speed at which sound propagates in a medium. The speed of sound of a crystal cell is 1500 m / s, and the speed of sound of an aluminum cell is 2800 m / s. Therefore, the speed of sound of the material corresponding to the filling ratio of each grid can be calculated based on the filling ratio, thereby obtaining the matching layer speed matrix. Each element in the matching layer speed matrix is ​​the speed of sound corresponding to a grid. The density of the crystal cell is 1000 kg / m 3 , the density of aluminum unit cell is 1800kg / m 3 , so the density of the material corresponding to the filling ratio of each grid can be calculated based on the filling ratio, thereby obtaining the matching layer density matrix. Each element in the matching layer density matrix is ​​the density corresponding to a grid.

[0080] Step S140, obtaining sound pressure distribution information in the working domain based on the matching layer sound velocity matrix, the matching layer density matrix and preset sound source information;

[0081] It is worth noting that the sound source information refers to the relevant information of the ultrasonic source, including the frequency, amplitude, ultrasonic emission source position and focus position of the ultrasonic wave. In one embodiment, the frequency of the ultrasonic wave is set to 0.75MHz, the amplitude is set to 1*10 6 Pa, the focal position is set at a preset position in the working domain. The sound pressure distribution information refers to the distribution of the ultrasonic sound pressure in the working domain.

[0082] In one embodiment, the matching layer sound velocity matrix, the matching layer density matrix, the frequency of the ultrasound wave, the amplitude of the ultrasound wave, the focal position of the ultrasound wave, and the position of the ultrasound emission source are input into the COMSOL toolkit. The coordinate information of the working domain and the position information of the skull in the working domain are also input into the COMSOL toolkit. The COMSOL toolkit is used to perform a simulation to obtain the sound pressure distribution of the ultrasound wave in the working domain, that is, to obtain the sound pressure distribution information of the working domain. It should be noted that the COMSOL toolkit is fully known as COMSOL Multiphysics, which is used to simulate the propagation of sound waves in inhomogeneous media. It can output a waveform at any point in space and effectively generate a steady-state pressure distribution at the frequency of interest.

[0083] Step S150: Input the sound pressure distribution information into the initial matching layer material parameter prediction model to obtain a training material parameter matrix, and update the initial matching layer material parameter prediction model based on the training material parameter matrix and the filling ratio matrix to obtain a trained matching layer material parameter prediction model;

[0084] In one embodiment, the resnet50 model is imported from the ResNet library as the matching layer material parameter prediction model. ResNet (Residual Network) is a very important convolutional neural network structure in the field of deep learning. It provides an effective solution to the problem of gradient vanishing during deep network training. The core idea of ​​ResNet is to solve the problem of gradient vanishing during deep network training by introducing Skip Connection. In the forward propagation process of traditional deep networks, information needs to pass through multiple network layers in sequence, and during backpropagation, the gradient also needs to be propagated back layer by layer through multiple layers. When the number of network layers is deep, the gradient propagation layer by layer will lead to the problem of gradient vanishing or gradient exploding. ResNet solves this problem by introducing skip connections in the network. In the skip connection, the input can be directly passed to the output through the cross-layer connection, so that the gradient has a shorter path to propagate back, thereby avoiding the problem of gradient vanishing or gradient exploding. The basic module of ResNet is the Residual Block, which consists of two or three convolutional layers and skip connections. Among them, the output and input of the two convolutional layers are added and then passed through a nonlinear activation function to form the final residual block output.

[0085] It is worth noting that after passing through steps S110 to S140, the filling ratio matrix and the sound pressure distribution information corresponding to the filling ratio matrix are obtained as training samples. The sound pressure distribution information is input into the initial matching layer material parameter prediction model to obtain a training material parameter matrix. The training material parameter matrix represents the parameters of the matching layer material predicted during the training process. Then, based on the training material parameter matrix and the filling ratio matrix, an error comparison is performed on the initial matching layer material parameters to obtain the corresponding loss value. By returning the gradient of the loss value, the parameters of the matching layer material parameter prediction model are readjusted, thereby updating and optimizing the matching layer material parameter prediction model. Among them, the loss value is calculated by L1loss, and the learning rate is set to 1e-5.

[0086] In one embodiment, steps S110 to S140 are repeatedly performed to obtain a large number of training samples. During this process, the sound pressure distribution information corresponds one-to-one with the fill ratio matrix, so a mapping relationship between the sound pressure distribution information and the fill ratio matrix can be established. This facilitates inputting the sound pressure distribution information into the initial matching layer material parameter prediction model to obtain a training material parameter matrix. Based on this mapping relationship, the fill ratio matrix corresponding to the sound pressure distribution information can then be determined. The initial matching layer material parameter prediction model can then be updated based on the training material parameter matrix and the corresponding fill ratio matrix.

[0087] In one embodiment, steps S110 to S140 are repeated a total of 2000 times, resulting in 2000 sets of data, each of which includes sound pressure distribution information and a fill ratio matrix. A portion of the 2000 sets of data is used as a training set, and another portion as a validation set. Step S150 is then repeated based on the training set to iteratively update the matching layer material parameter prediction model until the number of iterations reaches a preset threshold or the loss converges, thereby obtaining a trained matching layer material parameter prediction model. The trained matching layer material parameter prediction model is then verified using the validation set data. The parameter file of the matching layer material parameter prediction model that performs best on the validation set is selected and saved, thereby obtaining a trained matching layer network prediction model.

[0088] In another embodiment, the sound pressure distribution information is a sound pressure distribution matrix. Since the matching layer material parameter prediction model has a weak perception of the characteristics of the sound pressure distribution information, for example, the difference between an excellent focused sound field and a distorted focused sound field in terms of acoustic angle is very large, but after being converted into a sound pressure distribution matrix, it is difficult for the model to perceive the difference between an excellent focused sound field and a distorted focused sound field. In order to improve the model's sensitivity to the sound pressure distribution information, a weight matrix with the same size as the sound pressure distribution matrix is ​​preset. In the weight matrix, the weight value corresponding to the focal position is set to a maximum value of 10, the farther from the center, the lower the weight value, and the minimum value of 0 is set at the edge. Then, the sound pressure distribution matrix of each set of data in the 2000 sets of data is multiplied by the weight matrix to obtain an updated sound pressure distribution matrix, thereby updating the data, and then the matching layer material parameter prediction model is trained based on the updated 2000 sets of data.

[0089] Step S160 , obtaining target skull sound pressure information, and inputting the target skull sound pressure information into the trained matching layer material parameter prediction model to obtain a target material parameter matrix.

[0090] It is worth noting that the target skull sound pressure information refers to the sound pressure requirement that the ultrasound waves are expected to achieve around the skull. Typically, the sound pressure achieved by ultrasound waves around the skull must meet certain requirements to achieve therapeutic purposes. By inputting the target skull sound pressure information into a trained matching layer material parameter prediction model to obtain a target material parameter matrix, relevant technicians can create a matching layer based on the target material parameter matrix and then apply this matching layer to transcranial ultrasound. During application, the sound pressure of ultrasound waves around the skull can meet the sound pressure requirements.

[0091] In the embodiment of the present application, through steps S110 to S160 above, training samples for the matching layer material parameter prediction model are first prepared. Specifically, a skull model and a working domain including the skull model are constructed. Then, a matching layer region is determined within the working domain, and the matching layer region is divided into a plurality of equally sized and interconnected grids. A filling ratio is assigned to each grid to obtain a filling ratio matrix. Since the filling ratio characterizes the material parameters within the grid, assigning a filling ratio to each grid can be used to simulate the characteristics of a real matching layer. Then, the sound velocity and density of each grid are calculated separately to obtain a matching layer sound velocity matrix and a matching layer density matrix. Since the propagation of ultrasonic waves in a medium is primarily related to the sound velocity and density of the medium, sound pressure distribution information in the working domain can be obtained based on the matching layer sound velocity matrix, the matching layer density matrix, and preset sound source information. The sound pressure distribution information serves as a training sample for the matching layer material parameter prediction model. The sound pressure distribution information is input into the initial matching layer material parameter prediction model to obtain a training material parameter matrix. The initial matching layer material parameter prediction model is updated based on the training material parameter matrix and the filling ratio matrix to obtain a trained matching layer material parameter prediction model. The target skull sound pressure information is obtained and input into the trained matching layer material parameter prediction model to obtain a target material parameter matrix. In this way, the present application can obtain the target material parameter matrix of the matching layer based on the target skull sound pressure information. The matching layer manufactured based on the target material parameter matrix can make the sound pressure information in the skull meet the target skull sound pressure information without the need for manual calculation based on experience, which is highly efficient.

[0092] In one embodiment, referring to Figure 5 , Figure 5 for Figure 1 A specific flow chart of step S110 in FIG. Figure 5 The illustrated process steps include but are not limited to steps S510 to S530.

[0093] Step S510, acquiring a plurality of computed tomography images of skull slices of different thicknesses scanned at different angles;

[0094] It is worth noting that computed tomography (CT) is a medical imaging diagnostic technology that uses X-rays for tomographic imaging. CT uses a rotating X-ray scanner to perform continuous scans at multiple angles within the patient's body, and then uses a computer to process and reconstruct the scanned data to generate high-resolution tomographic images.

[0095] Step S520, determining Hounsfield unit values ​​at multiple locations of the skull based on the computed tomography image;

[0096] It's worth noting that the Hounsfield Unit (HU), also known as the CT value, is a unit of measurement used to measure the density of a specific tissue or organ in the human body. Medically, it's called the Hounsfield Unit (HU). It's a relative value derived from the X-ray attenuation coefficient measured by the detector using a specific mathematical model, and is used to indicate tissue density.

[0097] Step S530 , calculating an average value of the Hounsfield units based on the Hounsfield unit values ​​at multiple locations on the skull, and constructing a skull model based on the average value of the Hounsfield units; wherein the Hounsfield unit values ​​at each location on the skull model are all the average value of the Hounsfield units.

[0098] This application calculates the CT value of each position of the real skull through steps S510 to S530, and then calculates the average value of the Hounsfield unit based on multiple CT values ​​to construct a skull model, so that the CT value of each position in the skull model is the average value of the Hounsfield unit, making the skull module a uniform medium.

[0099] In one embodiment, referring to Figure 6 , Figure 6 for Figure 1 A specific flow chart of step S120 in FIG. Figure 6 The illustrated step flow includes but is not limited to step S610 to step S620.

[0100] Step S610, setting the value range of the filling ratio;

[0101] It is worth noting that the value range of the fill ratio is set to (0, 1).

[0102] Step S620 , determining a starting column grid among the multiple grids, randomly selecting a value from a value range as the fill ratio of the starting column grid, and making the fill ratio of each column grid increase or decrease column by column within the value range; wherein, the fill ratios of grids in the same column are the same.

[0103] In one embodiment, referring to Figure 2 and Figure 3 In a multi-column grid, the grid closest to the horizontal axis with a value of 0 is used as the starting grid column. Then, starting from the starting grid column, the filling ratio of each grid column increases from left to right within the value range. Since the filling ratio increases column by column, the speed of sound and density of each grid column also increase column by column. Figure 4 , Figure 4 It is a diagram showing the relationship between filling ratio, equivalent sound velocity and equivalent density. Figure 4In the figure, the solid line represents the relationship between the equivalent density and the filling ratio, and the dotted line represents the relationship between the equivalent sound speed and the filling ratio. As the filling ratio changes from 0 to 1, the equivalent sound speed of the corresponding material also changes from the water sound speed (1500m / s) to the average sound speed of the skull (2800m / s). Since the filling ratio produced in this application is increased column by column, the elements in the filling ratio matrix are also increased column by column, so that the target material parameter matrix predicted by the matching layer material parameter prediction model is also increased column by column. The material parameters of the matching layer are increased column by column, which can make the matching layer achieve a gradual effect, can alleviate the impedance matching problem between water and skull to a certain extent, and can enrich the training data set. In one embodiment, the filling ratio is randomly increased column by column.

[0104] In another embodiment, starting from the starting grid column, the fill ratio of each grid column decreases column by column from left to right within a value range. Because the fill ratio produced by this application decreases column by column, the elements in the fill ratio matrix also decrease column by column, and thus the target material parameter matrix predicted by the matching layer material parameter prediction model also decreases column by column. The column-by-column decrease in the matching layer material parameters can achieve a gradual effect on the matching layer, can alleviate the impedance matching problem between water and skull to a certain extent, and can enrich the training data set.

[0105] In one embodiment, the number of columns of the grid in the matching layer region is N, and step S620 may include the following steps:

[0106] Randomly select a value X1 from the value range as the fill ratio of the starting column grid, and calculate the fill ratio of the grids from the second column to the Nth column according to the first calculation formula. The first calculation formula is:

[0107]

[0108] Among them, X i Characterizes the filling ratio of the grid in column i, X i-1 Characterizes the fill ratio of the grid in the i-1th column, where i is 2, 3,..., N; c is a preset attenuation factor. It should be noted that c is a large positive number. Those skilled in the art can adjust the value of c based on actual needs to avoid the fill ratio exceeding the range. Using the first calculation formula, the fill ratio of each column of grids can be increased column by column within the range, ensuring that the increasing relationship is neither geometric nor arithmetic, which is more realistic. Here, X1 is the fill ratio of the starting column of grids.

[0109] When calculating using the first calculation formula, detect X i Whether it exceeds the value range, when X is detected i If the value exceeds the range, increase the preset attenuation factor in the first calculation formula and recalculate X i, so that X i Within the value range; X i Characterize the fill ratio of the i-th column grid, i is 2, 3..., N. In one embodiment, N is 75. In addition, refer to Figure 3 Since the sound velocity is positively correlated with the filling ratio, and the density is positively correlated with the filling ratio, the sound velocity of the material also increases column by column. The impedance of the material is positively correlated with the sound velocity, so the impedance also increases column by column.

[0110] In another embodiment, step S620 may include the following steps:

[0111] Randomly select a value X1 from the value range as the fill ratio of the starting column grid, and calculate the fill ratio of the grids from the 2nd column to the Nth column according to the second calculation formula. The second calculation formula is:

[0112]

[0113] Among them, X i Characterizes the filling ratio of the grid in column i, X i-1 The fill ratio of the grid in the i-1th column is represented by "i", where "i" is 2, 3, ..., N; and "c" is a preset attenuation factor. It should be noted that "c" is a large positive number. Those skilled in the art can adjust the value of "c" based on actual needs to avoid exceeding the fill ratio range. The second calculation formula allows the fill ratio of each column to decrease sequentially within the range. Here, "X1" is the fill ratio of the starting column.

[0114] When calculating using the second calculation formula, detect X i Whether it exceeds the value range, when X is detected i If the value exceeds the range, increase the preset attenuation factor in the second calculation formula and recalculate X i , so that X i Within the value range; X i Characterizes the filling ratio of the i-th column grid, where i is 2, 3..., N.

[0115] In one embodiment, referring to Figure 7 , Figure 7 for Figure 1 A specific flow chart of step S150 in FIG. Figure 8 The illustrated process steps include but are not limited to step S810 to step S820.

[0116] Step S810, extracting the sound pressure information around the skull model from the sound pressure distribution information;

[0117] Step S820 : Inputting the sound pressure information around the skull into the initial matching layer material parameter prediction model to obtain a training material parameter matrix.

[0118] It is worth noting that the sound pressure distribution information includes the sound pressure distribution of the entire working domain. The present application intercepts the skull surrounding sound pressure information around the skull model from the sound pressure distribution information through steps S810 to S820, and inputs the skull surrounding sound pressure information into the initial matching layer material parameter prediction model to obtain a training material parameter matrix, which can reduce the size of the data input into the initial matching layer material parameter prediction model, thereby improving computational efficiency.

[0119] In one embodiment, before step S140, the focal position of the sound source information is set around the skull model. In step S810, the sound pressure information around the focal position is extracted from the sound pressure distribution information as the sound pressure information around the skull. This allows the matching layer material parameter prediction model of this embodiment of the present application to focus only on the sound pressure distribution at the focal point, improving computational efficiency.

[0120] It is worth noting that, referring to Figure 8 , Figure 8 This is a schematic diagram of the target skull sound pressure information of an embodiment of the present application. The target skull sound pressure information is the sound pressure information obtained in a preset focused sound field without a skull. The target skull sound pressure information is input into the trained matching layer material parameter prediction model to obtain the target material parameter matrix, and the target matching layer is prepared using the target material parameter matrix. Figure 9 , Figure 9 Schematic diagram of the first sound pressure information of a skull without a matching layer under the action of a preset focused sound field; Figure 10 , Figure 10 for Figure 9 Schematic diagram of the sound pressure at the focus of the cut line. Figure 11 , Figure 11 Schematic diagram of the second sound pressure information of a skull with a target matching layer under the action of a preset focused sound field. Figure 12 , Figure 12 for Figure 11 Schematic diagram of the sound pressure at the focus in the figure. Figure 9 to Figure 12 It can be clearly seen that after applying the target matching layer, the position and shape of the focus have been significantly improved, the energy at the focus has also been significantly improved, and the sound pressure value at the focus has increased by 60% compared to the sound pressure value of the skull alone without a matching layer.

[0121] Reference Figure 13 The second embodiment of the present application provides a matching layer material parameter prediction device, including:

[0122] A construction module 1410 is configured to construct a skull model and a working domain including the skull model;

[0123] a configuration module 1420 for determining a matching layer region in the working domain based on the skull model, dividing the matching layer region into a plurality of equally sized and interconnected grids, and configuring a fill ratio for each grid to obtain a fill ratio matrix; wherein the fill ratio represents a material parameter within the grid;

[0124] A calculation module 1430 is configured to calculate the sound velocity and density of each grid based on the filling ratio to obtain a matching layer sound velocity matrix and a matching layer density matrix;

[0125] A sound pressure determination module 1440 is configured to obtain sound pressure distribution information in the working domain based on the matching layer sound velocity matrix, the matching layer density matrix, and preset sound source information;

[0126] a training module 1450 configured to input the acoustic pressure distribution information into an initial matching layer material parameter prediction model to obtain a training material parameter matrix, and update the initial matching layer material parameter prediction model based on the training material parameter matrix and the filling ratio matrix to obtain a trained matching layer material parameter prediction model;

[0127] The application module 1460 is configured to obtain target skull sound pressure information, and input the target skull sound pressure information into the trained matching layer material parameter prediction model to obtain a target material parameter matrix.

[0128] The matching layer material parameter prediction device can execute the matching layer material parameter prediction method of the embodiment of the present application. When executing the method, training samples of the matching layer material parameter prediction model are first prepared. Specifically, a skull model and a working domain including the skull model are constructed. Then, a matching layer area is determined in the working domain, and the matching layer area is divided into multiple grids of equal size and connected to each other. A filling ratio is configured for each grid to obtain a filling ratio matrix. Since the filling ratio represents the material parameters within the grid, configuring a filling ratio for each grid can be used to simulate the characteristics of a real matching layer. Then, the sound velocity and density of each grid are calculated separately to obtain a matching layer sound velocity matrix and a matching layer density matrix. Since the transmission of ultrasonic waves in a medium is mainly related to the sound velocity and density of the medium, based on the matching layer sound velocity matrix, the matching layer density matrix and preset sound source information, sound pressure distribution information in the working domain can be obtained. The sound pressure distribution information serves as a training sample for the matching layer material parameter prediction model. The sound pressure distribution information is input into the initial matching layer material parameter prediction model to obtain a training material parameter matrix. The initial matching layer material parameter prediction model is updated based on the training material parameter matrix and the filling ratio matrix to obtain a trained matching layer material parameter prediction model. The target skull sound pressure information is obtained and input into the trained matching layer material parameter prediction model to obtain a target material parameter matrix. In this way, the present application can obtain the target material parameter matrix of the matching layer based on the target skull sound pressure information. The matching layer manufactured based on the target material parameter matrix can make the sound pressure information in the skull meet the target skull sound pressure information without the need for manual calculation based on experience, which is highly efficient.

[0129] The specific implementation of the matching layer material parameter prediction device is basically the same as the specific implementation of the matching layer material parameter prediction method in the above embodiment, and will not be repeated here. On the premise of meeting the requirements of the embodiment of the present application, the matching layer material parameter prediction device can also be provided with other functional modules to implement the matching layer material parameter prediction method in the above embodiment.

[0130] To achieve the above objectives, a third embodiment of the present application provides an electronic device comprising a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program, implements the matching layer material parameter prediction method of the above embodiment. The electronic device can be any smart terminal, including a tablet computer and an in-vehicle computer.

[0131] In one embodiment, referring to Figure 14 , Figure 14 The hardware structure of the electronic device according to the embodiment of the present application is shown. The electronic device includes:

[0132] The processor 151 can be implemented in a manner of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is used to execute related programs to implement the technical solutions provided by the embodiments of the present application.

[0133] The memory 152 can be implemented in a manner of a ROM (ReadOnly Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory), and the like. The memory 152 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present application are implemented by software or firmware, the related program codes are stored in the memory 152 and are called and executed by the processor 151 to implement the matching layer material parameter prediction method of the embodiments of the present application.

[0134] The input / output interface 153 is used to realize information input and output.

[0135] The communication interface 154 is used to realize the communication interaction between the device and other devices, and can realize the communication through a wired manner (for example, a USB, a network cable, or the like) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, or the like).

[0136] The bus 155 is used to transmit information between various components (for example, the processor 151, the memory 152, the input / output interface 153, and the communication interface 154) of the device.

[0137] The processor 151, the memory 152, the input / output interface 153, and the communication interface 154 are connected to each other through the bus 155 to realize the communication connection between the device.

[0138] To achieve the above object, a computer readable storage medium is provided in the fourth aspect of the present application. The storage medium stores a computer program, and the computer program is executed by a processor to implement the matching layer material parameter prediction method of the first aspect of the present application.

[0139] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0140] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0141] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0142] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0143] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0144] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0145] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0146] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the above units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0147] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0148] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0149] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in other words, the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various program storage media.

[0150] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not limited to the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A matching layer material parameter prediction method, characterized in that: include: constructing a skull model, and constructing a working domain including the skull model; Based on the skull model, determining a matching layer region in the working domain, dividing the matching layer region into a plurality of grids of equal size and connected to each other, configuring a filling ratio for each grid to obtain a filling ratio matrix, wherein the filling ratio represents a material parameter within the grid; Based on the filling ratio, the sound velocity and density of each grid are calculated respectively to obtain a matching layer sound velocity matrix and a matching layer density matrix; Obtaining sound pressure distribution information in the working domain based on the matching layer sound velocity matrix, the matching layer density matrix, and preset sound source information; Inputting the sound pressure distribution information into an initial matching layer material parameter prediction model to obtain a training material parameter matrix, and updating the initial matching layer material parameter prediction model based on the training material parameter matrix and the filling ratio matrix to obtain a trained matching layer material parameter prediction model; Target skull sound pressure information is acquired, and the target skull sound pressure information is input into the trained matching layer material parameter prediction model to obtain a target material parameter matrix.

2. The matching layer material parameter prediction method according to claim 1, characterized in that: The method of constructing a skull model comprises: Obtaining computed tomography images of multiple skull slices of varying thicknesses scanned at different angles; determining Hounsfield unit values ​​at a plurality of locations of the skull based on the computed tomography image; Based on the Hounsfield unit values ​​at multiple locations on the skull, an average Hounsfield unit value is calculated, and the skull model is constructed based on the average Hounsfield unit value; wherein the Hounsfield unit value at each location on the skull model is the average Hounsfield unit value.

3. The matching layer material parameter prediction method according to claim 1, characterized in that: The filling ratio is configured for each grid to obtain a filling ratio matrix, including: Setting a value range of the filling ratio; Determine a starting column grid among the multiple grids, randomly select a value from the value range as the fill ratio of the starting column grid, and make the fill ratio of each column grid increase or decrease column by column within the value range; wherein the fill ratios of grids located in the same column are the same.

4. The matching layer material parameter prediction method according to claim 3, characterized in that: The number of columns of the grid in the matching layer area is N; Randomly selecting a value from the value range as the fill ratio of the starting column grid, so that the fill ratio of each column grid increases or decreases column by column within the value range, includes: A value X1 is randomly selected from the value range as the fill ratio of the starting column grid; and the fill ratios of the second column grid to the Nth column grid are calculated according to a first calculation formula, wherein the first calculation formula is: Among them, X i Characterizes the fill ratio of the i-th column grid, X i-1 Characterizes the filling ratio of the i-1th column grid, i is 2, 3..., N; c is a preset attenuation factor.

5. The matching layer material parameter prediction method according to claim 4, characterized in that: Calculating the fill ratio of the second column of grids to the Nth column of grids according to the first calculation formula includes: Detect X i Whether it exceeds the value range, when X is detected i If the value range is exceeded, the preset attenuation factor in the first calculation formula is increased and X is recalculated. i , so that X i Within the value range; X i Characterizes the filling ratio of the i-th column grid, where i is 2, 3..., N.

6. The matching layer material parameter prediction method according to claim 1, characterized in that: Inputting the sound pressure distribution information into the initial matching layer material parameter prediction model to obtain a training material parameter matrix includes: intercepting the skull periphery sound pressure information around the skull model from the sound pressure distribution information; The sound pressure information around the skull is input into the initial matching layer material parameter prediction model to obtain the training material parameter matrix.

7. The matching layer material parameter prediction method according to claim 4, characterized in that: Before obtaining the sound pressure distribution information in the working domain based on the matching layer sound velocity matrix, the matching layer density matrix and the preset sound source information, the method further includes: Setting the focal position of the sound source information around the skull model; The step of intercepting the sound pressure information around the skull model from the sound pressure distribution information includes: The peri-focal sound pressure information around the focal position is extracted from the sound pressure distribution information as the peri-skull sound pressure information.

8. A matching layer material parameter prediction device, characterized in that: include: A construction module, configured to construct a skull model and a working domain including the skull model; a configuration module, configured to determine a matching layer region in the working domain based on the skull model, divide the matching layer region into a plurality of equally sized and interconnected grids, and configure a fill ratio for each grid to obtain a fill ratio matrix; wherein the fill ratio represents a material parameter within the grid; A calculation module, configured to calculate the sound velocity and density of each grid based on the filling ratio to obtain a matching layer sound velocity matrix and a matching layer density matrix; a sound pressure determination module, configured to obtain sound pressure distribution information in the working domain based on the matching layer sound velocity matrix, the matching layer density matrix, and preset sound source information; a training module, configured to input the sound pressure distribution information into an initial matching layer material parameter prediction model to obtain a training material parameter matrix, and update the initial matching layer material parameter prediction model based on the training material parameter matrix and the filling ratio matrix to obtain a trained matching layer material parameter prediction model; The application module is used to obtain target skull sound pressure information, input the target skull sound pressure information into the trained matching layer material parameter prediction model, and obtain a target material parameter matrix.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the matching layer material parameter prediction method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the matching layer material parameter prediction method according to any one of claims 1 to 7 is implemented.

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