Preparation method and equipment of craniocerebral physical model and storage medium

Abstract

The invention discloses a preparation method and equipment of a craniocerebral physical model and a storage medium. The preparation method of the craniocerebral physical model comprises the steps that a mold is manufactured, and a liquid injection hole is formed in the mold; mixing agar powder with conductive salt and water to prepare an agar solution; and introducing the agar solution into the mold through the liquid injection hole, and cooling to form the brain three-dimensional physical model. According to the preparation method of the craniocerebral physical model, the obtained craniocerebral three-dimensional physical model is high in accuracy, accurate detection data for reference can be provided for the medical field, and the accuracy requirements of experiments and researches can be well met.

Description

Technical Field

[0001] The present application relates to the field of medical equipment technology, and specifically to a method for preparing a physical model of the brain, equipment and storage medium. Background Art

[0002] Against the backdrop of the rapid development of modern neuromodulatory technologies, transcranial electrical stimulation (tES) has attracted widespread attention as a non-invasive deep brain stimulation method. Transcranial electrical stimulation (tES) regulates the electrophysiological activity of brain neurons by applying low currents to the scalp, thereby changing the information transmission and functional connections between brain regions to achieve precise regulation of brain function. This technology has shown great application potential in clinical and scientific research fields due to its non-invasive and convenient characteristics, especially in the auxiliary treatment of neuropsychiatric diseases such as depression, Alzheimer's disease and Parkinson's disease, and has gradually become an important intervention method.

[0003] Although tES has significant advantages, its stimulation effect on deep brain regions still faces technical challenges, especially how to achieve precise regulation of deep brain regions under non-invasive conditions. Since the electric field distribution will attenuate and diffuse when the current is transmitted from the scalp to the deep brain regions, the current problem is how to achieve precise stimulation of deep brain regions by optimizing the current intensity, polarity and waveform, combined with personalized electrode position design. In addition, many related technologies make it difficult to effectively verify whether the electric field strength predicted by computational modeling is consistent with the experimental results, making it difficult to effectively optimize through physical measurements.

[0004] The electric field monitoring methods in related technologies mainly rely on minimally invasive stereotactic electroencephalography (sEEG) and imaging methods (such as MRI and CT) to infer the distribution of intracranial electric fields. However, sEEG has obvious limitations: its sampling points are limited in orientation, and it can usually only capture the electrical activity of local brain areas, and electrodes need to be implanted through invasive surgery, which increases the risk to patients. Although imaging methods such as MRI and CT can provide certain auxiliary information, they cannot directly reflect the electrical activity of neurons and are only suitable for indirect observation of structural and local electric field distributions, which makes it difficult to meet the real-time and accurate monitoring needs of deep brain electric fields.

[0005] The brain tissue models in related technologies are difficult to provide accurate detection data for reference in the medical field.

[0006] The above statements are only used to provide background information related to the present application and do not necessarily constitute prior art. Summary of the invention

[0007] The purpose of this application is to provide a method, device, equipment and storage medium for preparing a physical model of the brain. In order to have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to determine the key / important components or describe the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description below.

[0008] According to one aspect of an embodiment of the present application, a method for preparing a physical model of a cranial brain is provided, comprising:

[0009] Making a mold, wherein the mold has a liquid injection hole;

[0010] Agar powder is mixed with conductive salt and water to prepare an agar solution;

[0011] The agar solution is introduced into the mold through the injection hole and cooled to form a three-dimensional physical model of the brain.

[0012] In some embodiments of the present application, before making the mold, the method further includes:

[0013] Acquire brain image data through various medical imaging techniques;

[0014] Performing spatial alignment on the image data obtained by the plurality of medical imaging technologies to obtain spatially aligned image data;

[0015] Based on the spatially aligned image data, a three-dimensional digital model of the brain is constructed; the three-dimensional digital model is used to make a mold and prepare an agar solution.

[0016] In some embodiments of the present application, the making of the mold includes: preparing the mold based on the three-dimensional digital model;

[0017] The method of preparing an agar solution by mixing agar powder with conductive salt and water includes: preparing an agar solution based on the three-dimensional digital model.

[0018] In some embodiments of the present application, the making of the mold includes:

[0019] The mold is made by 3D printing technology, and the injection hole is reserved at the preset position of the mold;

[0020] The through holes or electrodes on the PCB board are used as collection points to form a signal collection array.

[0021] In some embodiments of the present application, the agar powder is mixed with a conductive salt and water to prepare an agar solution, comprising:

[0022] Dissolve the conductive salt in deionized water or ultrapure water and adjust it to a preset concentration range;

[0023] Add agar powder of preset concentration and stir to form a mixed solution;

[0024] Heat the mixed solution until the agar is completely dissolved;

[0025] After cooling to room temperature, the cooled agar solution is introduced into the 3D printing mold through the injection hole, and then naturally cooled to solidify after being sealed to form a three-dimensional physical model.

[0026] In some embodiments of the present application, constructing a three-dimensional digital model of the brain based on the spatially aligned image data includes:

[0027] For the spatially aligned image data, interpolating any two adjacent slice images to obtain an interpolated image;

[0028] Segmenting the interpolated image;

[0029] Construct voxels and isosurfaces based on the segmented images;

[0030] When a voxel intersects an isosurface, the coordinates of the intersection point between the voxel edge and the isosurface, the normal vector at the voxel vertex, and the normal vector at the intersection point coordinates are calculated;

[0031] A three-dimensional digital model of the brain is generated based on the intersection coordinates, the normal vectors at the voxel vertices, and the normal vectors at the intersection coordinates.

[0032] In some embodiments of the present application, the plurality of medical imaging techniques include magnetic resonance imaging, computed tomography, diffusion tensor imaging, microscopic imaging and / or positron emission tomography.

[0033] According to another aspect of an embodiment of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any embodiment of the present application.

[0034] According to another aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. The computer program is executed by a processor to implement the method described in any embodiment of the present application.

[0035] The technical solution provided by one aspect of the embodiments of the present application may have the following beneficial effects:

[0036] The method for preparing a physical model of the brain provided in the embodiment of the present application can prepare a three-dimensional physical model of the brain with high accuracy, can provide accurate detection data for reference in the medical field, and can well meet the accuracy requirements of experiments and research.

[0037] The above description is only an overview of the technical solution of the embodiment of the present application. In order to more clearly understand the technical means of the embodiment of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A flow chart of a method for preparing a physical model of the brain according to an embodiment of the present application is shown.

[0040] Figure 2 A schematic diagram of the structure of each layer of a three-dimensional physical model of the brain according to an embodiment of the present application is shown.

[0041] Figure 3 A schematic diagram showing the arrangement of discontinuous sampling silver chloride electrodes according to an embodiment of the present application is shown.

[0042] Figure 4 A measurement waveform diagram of the potential difference between two channels obtained by testing a physical model of the brain according to an embodiment of the present application is shown.

[0043] Figure 5 A flow chart of a method for preparing a physical model of the brain according to an embodiment of the present application is shown.

[0044] Figure 6 A structural block diagram of a device for preparing a physical model of the brain according to an embodiment of the present application is shown.

[0045] Figure 7 A structural block diagram of an electronic device according to an embodiment of the present application is shown.

[0046] Figure 8 A schematic diagram of a computer-readable storage medium according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0048] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0049] In response to the technical problem that brain tissue models in related technologies are difficult to provide accurate detection data for reference in the medical field, an embodiment of the present application provides a method for preparing a physical model of the brain, preparing a three-dimensional physical model of the brain. The three-dimensional physical model of the brain has high accuracy, can provide accurate detection data for reference in the medical field, and can well meet the accuracy requirements of experiments and research.

[0050] The following describes a method for preparing a physical model of the brain, a preparation device, equipment and a storage medium according to an embodiment of the present application in conjunction with the accompanying drawings.

[0051] One embodiment of the present application provides a method for preparing a physical model of the brain, which may include the step of preparing a three-dimensional physical model of the brain by an agar casting technique. Specifically, referring to Figure 1 As shown, the agar casting technology includes: step 1, making a mold with an injection hole; step 2, mixing agar powder with conductive salt and water to prepare an agar solution; step 3, introducing the agar solution into the mold through the injection hole, and cooling to form a three-dimensional physical model. Through the agar casting technology, multiple layers of agar are used to cast electrolytes with different dielectric properties to simulate different tissue characteristics. The three-dimensional physical model of the brain obtained in this way has high accuracy, can provide accurate detection data for reference in the medical field, and can well meet the accuracy requirements of experiments and research.

[0052] Exemplarily, making a mold includes: making a mold by 3D printing technology, reserving a liquid injection hole at a preset position of the mold; using the through holes or electrodes on the PCB board as collection points to form a signal collection array. Each collection point is connected to the printed circuit on the PCB board to improve the stability and accuracy of signal transmission and provide efficient technical support for subsequent signal collection and processing. This PCB board can provide a relatively high spatial resolution, that is, the distance between the collection sites is relatively small, the number of channels is relatively large, and different PCB sites can correspond to the spatial distance of the brain area map according to the spatial coordinates, so the points that can be added in combination with the map structure are that the brain is divided into different sizes, marking different age ranges, and made according to the average shape structure of the brain within the age group, so that a more standardized structure can be obtained.

[0053] Agar powder is mixed with conductive salt and water to prepare an agar solution, including: dissolving the conductive salt in deionized water or ultrapure water and adjusting it to a preset concentration range; adding agar powder of a preset concentration and stirring it to form a mixed solution; heating the mixed solution until the agar is completely dissolved; after cooling to room temperature, introducing the cooled agar solution into the 3D printing mold through the injection hole, sealing it and naturally cooling it until it solidifies to form a three-dimensional physical model. Figure 2 As shown, Figure 2 The diagram shows the structure of each layer of a three-dimensional physical model of the brain in an example, wherein from the inside to the outside, there is a simulated gray matter layer 1, a simulated skull layer 2, and a skin and fat simulation layer 3.

[0054] For example, a simulation device close to the actual size of brain tissue is designed based on brain tissue research data, and agar casting technology is used to generate a three-dimensional physical model of the brain. Through known segmentation technology, key areas such as white matter, gray matter, cerebrospinal fluid, skull and scalp in the model are simulated to prepare smooth interfaces and coherent structures between various tissue areas, providing a stable foundation for the construction of high-precision models.

[0055] In the mold design, the geometric shape is generated based on the three-dimensional brain tissue model after segmentation. The mold material can be nylon composite glass fiber (SLSFS3400GF, farsoon, China) or similar materials such as polyacrylonitrile (PAN) and polyamide (PA), which have good dimensional stability, toughness, insulation and corrosion resistance, and are suitable for long-term use. The mold is made by 3D printing technology, and injection holes are reserved at specific locations to facilitate the subsequent injection of agar solution, thereby forming an accurate brain tissue simulation structure in the mold. 3D printing can use FDM, SLA or SLS technologies to print three-dimensional brain models. 3D printing is suitable for rapid and customized model manufacturing, and can accurately replicate complex structures.

[0056] Acquisition array and signal transmission: In the acquisition structure of the model, through holes or other electrodes on the PCB board (such as silver / silver chloride electrodes, platinum electrodes, etc.) can be used as acquisition points. Through the board-to-board combination technology, a signal acquisition array is formed with a specific spacing. The electrodes on the PCB board are in contact with the preset parts (such as electrode arrays or acquisition points) in the model. Each acquisition point is connected to the printed circuit on the PCB board to improve the stability and accuracy of signal transmission and provide efficient technical support for subsequent signal acquisition and processing. Reference Figure 3 As shown, Figure 3 A schematic diagram of the arrangement of discontinuous sampling silver chloride electrodes is shown.

[0057] Agar water model preparation:

[0058] In order to prepare brain tissue simulations with different electrical conductivities, this embodiment uses high-strength agar powder mixed with appropriate amounts of sodium chloride (NaCl) and deionized water to prepare agar water models with different electrical conductivities.

[0059] The specific steps include (including variable options to increase parameter flexibility):

[0060] Add NaCl or other conductive salts (such as potassium chloride KCl, calcium chloride CaCl , sodium sulfate NaSO ) in proportion to a beaker and dissolve in deionized water or ultrapure water. Adjust the concentration range to 0.1-5% to control the conductivity. The concentration can be adjusted according to different simulation requirements.

[0061] Add appropriate amount of agar powder (e.g. 0.5-3% concentration), or alternative materials such as carrageenan, xanthan gum, etc. to ensure uniform dispersion. Ensure that the particles are completely dissolved during stirring to avoid aggregation.

[0062] Transfer the mixed solution to a suitable container, microwave for 30 seconds, shake evenly and repeat the heating process until the agar is completely dissolved. The heating time (20-45 seconds) can be adjusted according to the ingredients to obtain stable conductivity.

[0063] After cooling at room temperature for a preset time, such as 5 minutes, the agar solution is introduced into the 3D printing mold through the injection hole, and then naturally cooled to solidify after being sealed to form a brain tissue simulation model with structural stability. The cooling time is adjusted according to the solution composition and concentration to ensure the conductivity and structural integrity of the model.

[0064] This three-dimensional structure and mold design is based on the flexible selection of multiple materials and parameters. It not only accurately simulates the spatial distribution and conductivity characteristics of brain tissue, but also provides precise experimental support for the study of electric field distribution in transcranial electrical stimulation and other neural regulation technologies, significantly improving the verifiability and simulation accuracy of the electric field simulation model. This cranial brain model can be used in medical teaching, surgical planning, brain disease research, neuroscience experiments, virtual reality (VR) simulation and other applications. Figure 4 As shown, Figure 4 A measurement waveform of the potential difference between two channels obtained by testing the physical model of the brain in this example is shown.

[0065] refer to Figure 5 As shown, in some embodiments, before making the mold, the method may further include steps S10 to S30:

[0066] S10. Acquire brain image data through a variety of medical imaging technologies.

[0067] The cranial brain image data mainly includes brain tissue image data and skull bone image data, among which the accuracy of brain tissue image data plays a decisive role in the accuracy of cranial brain image data.

[0068] In some embodiments, two or more of the following medical imaging techniques may be used to acquire cranial brain image data:

[0069] MRI (Magnetic Resonance Imaging): MRI scans are used to obtain high-resolution image data of the brain structure. MRI can provide high-contrast and high-accuracy images of brain tissues such as gray matter, white matter, and ventricles.

[0070] CT (Computed Tomography): CT is used to obtain image data of cranial and brain structures, and is particularly suitable for obtaining macroscopic structural data of the brain. It is more accurate in imaging the macroscopic structure of the skull or brain.

[0071] Diffusion tensor imaging (DTI): This technology has a high accuracy in obtaining the fiber connection direction of white matter and can accurately obtain the spatial distribution of nerve fiber bundles in the brain.

[0072] Microscopic imaging: used to obtain image data of cranial brain structures, especially for high-precision microscopic structures (such as neurons and cell levels). Confocal microscopy or scanning electron microscopy can be used for cellular-level imaging to obtain image data with higher accuracy.

[0073] Positron emission tomography (PET): Used to make images of the brain by detecting radiation that is injected or inhaled.

[0074] Exemplarily, at least two of the above-mentioned multiple imaging techniques may be used to respectively acquire cranial brain image data.

[0075] S20, spatially aligning the image data obtained by the aforementioned multiple medical imaging technologies to obtain spatially aligned image data.

[0076] The image data obtained by different medical imaging technologies are spatially aligned (registered) so that the images are integrated in a unified spatial coordinate system. The data needs to be standardized according to a standard brain template (such as the MNI template) to achieve consistency and comparability of the model. Alignment is achieved by extracting feature points in the image (such as corner points, edge points, texture features, etc.), and then matching the feature points in each source image with the feature points in the reference image.

[0077] Different medical imaging technologies (such as MRI, CT, DTI, microscopic imaging, etc.) have different advantages and characteristics. By spatially aligning images of these different modalities, we can combine their respective advantages to provide more comprehensive and multidimensional physiological information. For example: CT scans provide high-precision skull bone structure images, MRI scans provide detailed soft tissue contrast, and PET or SPECT scans provide functional information such as metabolic activity or blood flow.

[0078] Through spatial alignment, the information of these different modalities can be combined to obtain more complete and accurate image data, which helps doctors make more accurate data analysis. Since each medical imaging technology has different scanning modes and different advantages of the images obtained, the image data obtained by different medical imaging technologies are spatially aligned so that each image is integrated in a unified spatial coordinate system. The final spatially aligned image data combines the advantages of various scanning modes, and the obtained image is more accurate, more precise and clearer than the image obtained by a single medical imaging technology.

[0079] For example, when the above-mentioned multiple imaging technologies are used to respectively acquire cranial brain image data, the image data acquired by MRI, the image data acquired by CT, the image data acquired by DTI, the image data acquired by microscopic imaging, and the image data acquired by PET are spatially aligned (registered) so that the images acquired by each imaging technology are integrated in a unified spatial coordinate system to obtain spatially aligned image data.

[0080] S30. Constructing a three-dimensional digital model of the brain based on the spatially aligned image data.

[0081] In some embodiments, constructing a three-dimensional digital model of the brain based on the spatially aligned image data may include: interpolating any two adjacent slice images for the spatially aligned image data to obtain an interpolated image; segmenting the interpolated image; constructing voxels and isosurfaces based on the segmented image; when a voxel intersects with an isosurface, calculating the coordinates of the intersection of the voxel edge and the isosurface, the normal vector at the voxel vertex, and the normal vector at the intersection coordinates; generating a three-dimensional digital model of the brain based on the intersection coordinates, the normal vector at the voxel vertex, and the normal vector at the intersection coordinates.

[0082] Exemplarily, based on the spatially aligned image data, the cranial brain image data is converted into a three-dimensional model, that is, a three-dimensional physical model of the cranial brain is obtained. Specifically, the following steps may be included:

[0083] 1) Construct voxels and isosurfaces.

[0084] Each time, two adjacent slice images are randomly selected to form a three-dimensional data field, and then volume elements are constructed one by one in the three-dimensional data field. Volume elements are also called voxels. Each volume element is a cube, and the 8 vertices on each volume element are taken from the upper and lower slice images respectively.

[0085] Scan each voxel to construct triangular patches to form an isosurface. An isosurface is a collection of points in space that have the same value. An isosurface can be expressed as {(x, y, z)|g(x, y, z)=d}, where d is a constant.

[0086] The isosurface is a cubic surface. When the isosurface intersects with the voxel surface, the intersection line appears as a hyperbola. Since the calculation process of the cubic surface is relatively complicated, in order to simplify this process, this example uses the triangular patch method to approximate the cubic curve. In addition, on the same surface of the voxel, the intersection points of two adjacent edges are connected by a straight line instead of the original curve, which further simplifies the calculation and effectively reduces the complexity of the calculation.

[0087] Any point in the voxel is represented as P(x, y, z), and the function value at point P(x, y, z) can be obtained by linear interpolation, as shown in formula (1):

[0088] g(x,y,z)=k0+k1x+k2y+k3z+k4xy+k5yz+k6zx+k7xyz (1)

[0089] The coefficient k i (i=0, 1, ..., 7) is the function value of the 8 vertices in the voxel. If the isosurface threshold is d, the isosurface equation is as shown in formula (2):

[0090] g(x, y, z) = d (2)

[0091] In this way, the intersection line between the isosurface and the volume element boundary surface can be calculated, and the two equations are linked to form the equation system:

[0092] g(x, y, z) = d; (3)

[0093] g(x,y,z)=k0+k1x+k2y+k3z+k4xy+k5yz+k6zx+k7xyz; (4)

[0094] Assuming the boundary surface equation is z=q, and substituting it into the equation system consisting of the above two equations (3) and (4), it can be determined that the final representation is a pair of hyperbolas. If a straight line is used instead, it may cause errors.

[0095] The isosurface is a cubic surface, and triangular patches can only approximately represent this complex surface. In the related art, when constructing the isosurface, the isosurface is represented by triangular patch fitting, which leads to large errors. At the intersection of the isosurface and the voxel surface, it presents a hyperbolic shape. The density of the three-dimensional data field can be set arbitrarily, and the size of the voxel can be adjusted according to demand. In the case of some large voxels, if triangular patches are directly used to replace the cubic surface, a large approximation error will be generated, thereby affecting the accuracy of the final reconstruction effect. In order to solve this problem, in some embodiments, the slice image is first interpolated to reduce the spacing between images, so that the size of the voxel constructed in the three-dimensional data field is less than a preset threshold. In this case, the error caused by using a straight line instead of a hyperbola can be ignored, thereby ensuring the accuracy of the reconstruction result.

[0096] Interpolation between images cannot be performed arbitrarily. When the spacing between slice images is greater than the spacing between pixels in the image, some intermediate images can be inserted between adjacent slice images through interpolation methods to achieve a smooth transition. In the method of this example, each voxel is constructed as a cube, so that the spacing between slice images after interpolation processing can be equal to the spacing between pixels in the image, thereby ensuring the uniformity and consistency of the voxels. Therefore, through appropriate interpolation processing, the spacing between slice images can be adjusted so that the three-dimensional voxels finally constructed meet the requirements of the cube, thereby improving the reconstruction accuracy.

[0097] The above requirements can be effectively met by using the linear weighted interpolation method to interpolate slice images. The linear weighted interpolation method can ensure that the interpolated image is similar to the two slice images before and after in terms of information, and at the same time, the interpolated image sequence conforms to the trend of gradual change from front to back in space. This can not only greatly improve the accuracy of image interpolation, but also effectively reduce the spacing between slice images, thereby improving the accuracy and quality of subsequent 3D reconstruction.

[0098] Linear weighted interpolation is an operation that performs weighted averaging on two adjacent slice images to generate a series of interpolated images between the two slice images. The interpolated image can be expressed by formula (4):

[0099] g α (i, j) = (1-α)g M (i, j)+αg M+1 (i, j) (4)

[0100] Among them, α = w1 / (w1+w2), which is the linear coefficient of image interpolation; g M (i, j) is the slice image of the Mth layer, w1 is the distance between the interpolated image and the Mth layer image; g M+1 (i, j) is the slice image of the M+1th layer, and w2 is the distance between the interpolated image and the M+1th layer image.

[0101] After the above linear weighted interpolation operation, the distance between slice images becomes smaller. In the subsequent reconstruction process, the error caused by replacing the hyperbola with a straight line will be greatly reduced, and the error impact can be ignored.

[0102] 2) Check whether the voxel intersects with the isosurface.

[0103] According to the comparison result of the function values ​​of the eight vertices on the voxel and the isosurface threshold, an index table is constructed to determine whether the voxel has an intersection with the isosurface, that is, to determine whether they intersect.

[0104] The slice image to be reconstructed is segmented appropriately, and then the segmented image is reconstructed in three dimensions. In this process, a fast search method for non-empty voxels is used, which significantly improves the reconstruction efficiency. This optimization can not only speed up the processing speed, but also effectively reduce redundant calculations and improve the performance of the overall algorithm.

[0105] The region growing method is a region-based image segmentation method that extracts connected regions in an image through predefined image grayscale values ​​or boundary information. When applying the region growing method, by setting the range of grayscale values, the regions with obvious grayscale value differences in the image can be effectively segmented, while the remaining parts usually correspond to the brain tissue regions that need to be reconstructed. In this way, the region growing method can accurately identify and separate the target region, which is helpful for subsequent image reconstruction and analysis.

[0106] The segmented slice images will form a three-dimensional data field, in which voxels are constructed one by one, and the voxels that intersect with the isosurface are identified.

[0107] By analyzing the spatial structure of all voxels, it can be determined that when a voxel is determined to be non-empty, the voxels adjacent to it will expand in a certain direction and form corresponding triangular patches. Based on this principle, a lookup table LIST, a queue DLE, an array ARRAY and a flag BZB are pre-set, where the lookup table LIST is used to record the adjacent voxel information of each voxel to be checked; the queue DLE is used to store non-empty voxels, which are voxels that intersect with the isosurface; each element in the array ARRAY corresponds to a voxel to be checked, indicating whether the voxel has been operated; the flag BZB is used to mark whether the voxel has entered the region of interest. Through the synergy of the lookup table LIST, the queue DLE, the array ARRAY and the flag BZB, the inspection and processing of voxels can be completed efficiently, and non-empty voxels can be determined.

[0108] When searching for non-empty voxels, each voxel is analyzed one by one continuously through the fast search method. First, each element in the ARRAY array is cleared to zero. When BZB = 0 and ARRAY[i] = 0, it indicates that the i-th voxel is an empty voxel;

[0109] When BZB=0 and ARRAY[i]=1, it indicates that both the i-th volume element and the i-1-th volume element are non-empty volume elements;

[0110] When BZB=1, it indicates that the current voxel is definitely a non-empty voxel.

[0111] When a voxel is confirmed as a non-empty voxel, other voxels adjacent to the voxel in the LIST lookup table are checked until all voxels confirmed to be non-empty are recorded in the DLE queue for subsequent operations. Since the segmented results will be used for image reconstruction, most voxels usually belong to the region of interest, which ensures that non-empty voxels intersecting with isosurfaces can be quickly and effectively checked, thereby greatly reducing the inspection time for empty voxels. This example first segments the image to be reconstructed, and then quickly searches for non-empty voxels, which ensures that voxels intersecting with isosurfaces can be promptly and effectively identified and checked, greatly reducing the time consumed in checking empty voxels, thereby improving the efficiency of model reconstruction.

[0112] 3) When the voxel intersects with the isosurface, calculate the coordinates of the intersection of the voxel edge and the isosurface, that is, the coordinates of each vertex of the triangular patch; then calculate the normal vector at the voxel vertex, and then find the normal vector at each vertex of the triangular patch;

[0113] The steps of constructing triangular facets may include: for each voxel, first compare the function value of each corner point of the voxel with the given isosurface S, and construct the index table of the voxel accordingly; then find the distribution of triangular facets in the voxel according to the index table. For each edge of the cube through which the isosurface passes, the nearest neighbor method is used to determine which point should be selected to replace the isovalue point. Next, the normal vector of the isovalue point is calculated using the central difference method using the three midpoints and two intersection points; finally, a triangular facet list is constructed based on the calculated isovalue points.

[0114] Through the step of constructing triangular facets, accurate isosurfaces are extracted from the voxel data, which reduces the mis-extraction or omission of isosurfaces, improves the matching accuracy between the final surface mesh and the actual data, and uses an index table to describe the relationship between the corner points of each voxel and the isosurface, which is more efficient. The lookup table can avoid repeated calculations in each voxel, and the distribution of triangular facets can be quickly determined through predefined configurations, which greatly improves the calculation efficiency. When processing each edge of the cube through which the isosurface passes, the nearest neighbor method is used to determine which point to use instead of the isosurface. By directly using the values ​​of the neighboring points for estimation, the calculation complexity can be effectively reduced while maintaining a high accuracy. In this way, the intersection of the isosurfaces can be quickly determined, reducing the complexity of the interpolation calculation.

[0115] 4) Based on the coordinates and normal vectors of each vertex of the required triangular face, an overall three-dimensional mesh is generated to form a final isosurface mesh, thereby realizing surface reconstruction and obtaining a three-dimensional digital model of the brain.

[0116] Specifically, based on the coordinates and normal vectors at each vertex of the desired triangular facet, the triangular facets calculated in all voxels are merged into an overall three-dimensional mesh to form a final isosurface mesh.

[0117] Thus, after obtaining the three-dimensional digital model of the brain, the following step can be performed: S40, preparing a three-dimensional physical model of the brain based on the three-dimensional digital model of the brain. Specifically, based on the three-dimensional digital model, the three-dimensional physical model of the brain is generated by using agar casting technology.

[0118] This three-dimensional structure and mold design, based on the flexible selection of multiple materials and parameters, not only accurately simulates the spatial distribution and conductivity characteristics of brain tissue, but also provides precise experimental support for the study of electric field distribution in transcranial electrical stimulation and other neural regulation technologies, significantly improving the verifiability and simulation accuracy of the electric field simulation model. The brain model can be used for medical teaching, surgical planning, brain disease research, neuroscience experiments, virtual reality (VR) simulation and other applications.

[0119] The cranial brain physical model prepared by the method for preparing the cranial brain physical model of the embodiment of the present application can be applied to TIS (temporal interfere stimulation), which is characterized by spatial focusing and long-distance focusing, similar to the remote focus formed by an array radar. Compared with the characteristic of tES in the related art that the farther the distance, the smaller the energy, the high-frequency current of TIS realized by the cranial brain physical model prepared by the method for preparing the cranial brain physical model of the embodiment of the present application decays more slowly, and through spatial positioning intensity measurement, the low-frequency effective neural stimulation formed by TIS can be stronger and more focused at a long distance.

[0120] The method for preparing a physical model of the brain in this example obtains brain image data through a variety of medical imaging technologies, spatially aligns the image data obtained by the multiple medical imaging technologies to obtain the spatially aligned image data, constructs a three-dimensional digital model of the brain based on the spatially aligned image data, and prepares a three-dimensional physical model of the brain based on the three-dimensional digital model. The three-dimensional physical model of the brain has high accuracy, can provide accurate detection data for reference in the medical field, and can well meet the accuracy requirements of experiments and research.

[0121] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0122] refer to Figure 6 As shown, another embodiment of the present application provides a device for preparing a physical model of the brain, comprising:

[0123] An imaging module, used for acquiring cranial brain image data through a variety of medical imaging techniques;

[0124] An alignment module, used for spatially aligning the image data obtained by the plurality of medical imaging technologies to obtain spatially aligned image data;

[0125] A construction module, used for constructing a three-dimensional digital model of the brain based on the spatially aligned image data;

[0126] A preparation module is used to prepare a three-dimensional physical model of the brain based on the three-dimensional digital model.

[0127] Exemplarily, preparing a three-dimensional physical model of the brain based on the three-dimensional digital model includes:

[0128] Based on the three-dimensional digital model, an agar casting technique is used to generate a three-dimensional physical model of the brain.

[0129] Exemplarily, the agar pouring technique comprises:

[0130] Making a mold, wherein the mold has a liquid injection hole;

[0131] Agar powder is mixed with conductive salt and water to prepare an agar solution;

[0132] The agar solution is introduced into the mold through the injection hole and cooled to form a three-dimensional physical model.

[0133] Exemplarily, the making of the mold includes:

[0134] The mold is made by 3D printing technology, and the injection hole is reserved at the preset position of the mold;

[0135] The through holes or electrodes on the PCB board are used as collection points to form a signal collection array.

[0136] Exemplarily, the agar powder is mixed with a conductive salt and water to prepare an agar solution, comprising:

[0137] Dissolve the conductive salt in deionized water or ultrapure water and adjust it to a preset concentration range;

[0138] Add agar powder of preset concentration and stir to form a mixed solution;

[0139] Heat the mixed solution until the agar is completely dissolved;

[0140] After cooling to room temperature, the cooled agar solution is introduced into the 3D printing mold through the injection hole, and then naturally cooled to solidify after being sealed to form a three-dimensional physical model.

[0141] Exemplarily, constructing a three-dimensional digital model of the brain based on the spatially aligned image data includes:

[0142] For the spatially aligned image data, interpolating any two adjacent slice images to obtain an interpolated image;

[0143] Segmenting the interpolated image;

[0144] Construct voxels and isosurfaces based on the segmented images;

[0145] When a voxel intersects an isosurface, the coordinates of the intersection point between the voxel edge and the isosurface, the normal vector at the voxel vertex, and the normal vector at the intersection point coordinates are calculated;

[0146] A three-dimensional digital model of the brain is generated based on the intersection coordinates, the normal vectors at the voxel vertices, and the normal vectors at the intersection coordinates.

[0147] Exemplarily, the plurality of medical imaging techniques include at least two of magnetic resonance imaging, computed tomography, diffusion tensor imaging, microscopic imaging and positron emission tomography.

[0148] Another embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method of any of the above embodiments.

[0149] refer to Figure 7 As shown, the electronic device 10 may include: a processor 100, a memory 101, a bus 102 and a communication interface 103, and the processor 100, the communication interface 103 and the memory 101 are connected via the bus 102; the memory 101 stores a computer program that can be run on the processor 100, and when the processor 100 runs the computer program, it executes the method provided in any of the aforementioned embodiments of the present application.

[0150] The memory 101 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the device network element and at least one other network element is realized through at least one communication interface 103 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0151] The bus 102 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The memory 101 is used to store a program, and the processor 100 executes the program after receiving an execution instruction. The method disclosed in any implementation of the aforementioned embodiment of the present application may be applied to the processor 100, or implemented by the processor 100.

[0152] The processor 100 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 100 or an instruction in the form of software. The above processor 100 may be a general-purpose processor, which may include a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 101, and the processor 100 reads the information in the memory 101 and completes the steps of the above method in combination with its hardware.

[0153] The electronic device provided in the embodiment of the present application and the method provided in the embodiment of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, operated or implemented by them.

[0154] Another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to implement the method of any of the above embodiments. Figure 8 As shown, the computer-readable storage medium shown is a CD 20 on which a computer program (ie, a program product) is stored. When the computer program is run by a processor, the method provided by any of the aforementioned embodiments will be executed.

[0155] It should be noted that examples of computer-readable storage media may also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which are not listed here one by one.

[0156] The computer-readable storage medium provided in the above-mentioned embodiments of the present application and the method provided in the embodiments of the present application are based on the same inventive concept and have the same beneficial effects as the method adopted, run or implemented by the application program stored therein.

[0157] It should be noted that:

[0158] The term "module" is not intended to be limited to a specific physical form. Depending on the specific application, a module can be implemented as hardware, firmware, software, and / or a combination thereof. In addition, different modules can share common components or even be implemented by the same components. There may or may not be clear boundaries between different modules.

[0159] The algorithm and display provided herein are not inherently related to any specific computer, virtual device or other equipment. Various general devices can also be used together with examples based on this. According to the above description, it is obvious to construct the structure required for this type of device. In addition, the application is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the application described herein, and the description made to specific languages ​​above is for the purpose of disclosing the best mode of implementation of the application.

[0160] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, 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, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0161] The above embodiments only express the implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for preparing a physical model of the brain, characterized in that: include: Making a mold, wherein the mold has a liquid injection hole; Agar powder is mixed with conductive salt and water to prepare an agar solution; The agar solution is introduced into the mold through the injection hole and cooled to form a three-dimensional physical model of the brain.

2. The method according to claim 1, characterized in that Before making the mold, the method further comprises: Acquire brain image data through various medical imaging techniques; Performing spatial alignment on the image data obtained by the plurality of medical imaging technologies to obtain spatially aligned image data; Based on the spatially aligned image data, a three-dimensional digital model of the brain is constructed; the three-dimensional digital model is used to make a mold and prepare an agar solution.

3. The method according to claim 2, characterized in that The making of the mold comprises: preparing the mold based on the three-dimensional digital model; The method of preparing an agar solution by mixing agar powder with conductive salt and water includes: preparing an agar solution based on the three-dimensional digital model.

4. The method according to any one of claims 1 to 3, characterized in that The manufacturing mold comprises: The mold is made by 3D printing technology, and the injection hole is reserved at the preset position of the mold; The through holes or electrodes on the PCB board are used as collection points to form a signal collection array.

5. The method according to any one of claims 1 to 3, characterized in that: The agar powder is mixed with conductive salt and water to prepare an agar solution, comprising: Dissolve the conductive salt in deionized water or ultrapure water and adjust it to a preset concentration range; Add agar powder of preset concentration and stir to form a mixed solution; Heat the mixed solution until the agar is completely dissolved; After cooling to room temperature, the cooled agar solution is introduced into the 3D printing mold through the injection hole, and then naturally cooled to solidify after being sealed to form a three-dimensional physical model.

6. The method according to claim 2 or 3, characterized in that: The step of constructing a three-dimensional digital model of the brain based on the spatially aligned image data comprises: For the spatially aligned image data, interpolating any two adjacent slice images to obtain an interpolated image; Segmenting the interpolated image; Construct voxels and isosurfaces based on the segmented images; When a voxel intersects an isosurface, the coordinates of the intersection point between the voxel edge and the isosurface, the normal vector at the voxel vertex, and the normal vector at the intersection point coordinates are calculated; A three-dimensional digital model of the brain is generated based on the intersection coordinates, the normal vectors at the voxel vertices, and the normal vectors at the intersection coordinates.

7. The method according to claim 2 or 3, characterized in that: The plurality of medical imaging techniques include magnetic resonance imaging, computed tomography, diffusion tensor imaging, microscopic imaging and / or positron emission tomography.

8. An electronic device, characterized in that: The device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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