A three-dimensional heart image reconstruction system

Abstract

The present application relates to the technical field of three-dimensional image reconstruction, in particular to a three-dimensional heart image reconstruction system. The system comprises a three-dimensional image reconstruction module. The three-dimensional image reconstruction module divides a three-dimensional data field into a central region and a peripheral region, selects a single triangular facet and a single vertex voxel in the central region, selects a starting voxel, takes the single vertex as a starting point, matches a corresponding neighborhood voxel, and takes the nearest distance between the vertex and the starting point as an evaluation order of merging and a planning of a next level starting voxel. The system can meet a complex triangular facet distribution state, improve the merging effect of triangular facets between voxels, improve the merging efficiency, and reduce the occurrence of invalid merging.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional image reconstruction, in particular to a three-dimensional heart image reconstruction system. BACKGROUND

[0002] With the rapid development of modern medical imaging technology, three-dimensional data visualization and reconstruction has become an important tool for medical research, clinical diagnosis and surgical planning, especially for heart-related disease treatment, the three-dimensional reconstructed heart image can help doctors understand the patient's condition, so as to make treatment decisions.

[0003] The existing three-dimensional reconstruction method of heart image mainly includes the following steps:

[0004] Firstly, the CT device rotates to emit X-rays through the patient's heart part and receives the corresponding signal to generate a two-dimensional image;

[0005] Secondly, the two-dimensional image is processed to remove noise;

[0006] Thirdly, according to the difference of different tissues in the image, the target tissue image related to the heart is extracted;

[0007] Fourthly, the tissue image of the corresponding interaction area is converted into a triangular facet by using the plane interaction state of different cubes and the target tissue image;

[0008] Fifthly, the triangular facets are integrated to generate a three-dimensional image of the target image.

[0009] In the integration process of the triangular facets, due to the different positions of different cubes, the distances of the corresponding triangular facets also exist differences, and the triangular facets formed are similar to the images of different positions of the heart, that is, the closer the distance, the more likely there is a splicing point, and the higher the splicing degree. Therefore, the existing splicing method mainly splices the triangular facets by the position distance of the cubes, and the distance between the adjacent cubes is consistent, and the splicing order is constant, for example, from top to bottom, from left to right, and the position of the triangular facets in the cubes is different, so the position of the corresponding vertex is also different. Therefore, the distance between the adjacent cubes is constant, and the conventional integration route cannot meet the complex distribution state of the triangular facets, resulting in a great reduction in the merging efficiency of the triangular facets.

[0010] In order to solve the above problems, there is an urgent need for a three-dimensional heart image reconstruction system to improve the reconstruction efficiency. SUMMARY

[0011] The purpose of the present application is to provide a three-dimensional heart image reconstruction system, which constructs a three-dimensional data field through a three-dimensional image reconstruction module, divides the three-dimensional data field into a series of voxels, collects the target state in each voxel, combines the vertex state of each voxel, constructs an 8-bit binary, generates a voxel state code, corresponds different triangular facets with different voxel state codes, establishes an index pre-computed lookup table, combines the basic topological pattern to process the intersection state of each voxel and the target surface image to form an equipotential surface, constructs a matching triangular facet, finally obtains the vertex coordinates of each triangular facet, selects the vertex coordinates of one of the triangular facets as the starting point, performs neighborhood voxel merging selection, and performs nearest vertex distance calculation in the neighborhood voxel, and formulates a triangular facet merging route according to the calculation result, so as to solve the problems raised in the above background art, that is:

[0012] The conventional integration route cannot meet the complex triangular facet distribution state, resulting in a greatly reduced triangular facet merging efficiency.

[0013] To achieve the above purpose, a three-dimensional heart image reconstruction system is provided, which comprises a data acquisition module, an image preprocessing module, a target image extraction module and a three-dimensional image reconstruction module. The two-dimensional tomographic image is collected through the data acquisition module, the image preprocessing module is used for image preprocessing of the two-dimensional tomographic image, the two-dimensional tomographic image clarity is improved, and the target tissue image is segmented from the original image based on the threshold segmentation algorithm of the target image extraction module according to the gray value difference of different tissues in the image, and the corresponding threshold is determined.

[0014] Further, in the three-dimensional reconstruction process, the three-dimensional data field is constructed by the three-dimensional image reconstruction module, the three-dimensional data field is divided into a series of voxels, the target state in each voxel is collected, the intersection state of the equipotential surface and the corresponding voxel is used to match the corresponding triangular facet as the three-dimensional image simulation result in the current voxel region, and finally the triangular facets are merged.

[0015] Further, in the triangular facet merging process, since the distance between adjacent voxels is constant and the distance between different triangular facets in adjacent voxels is different, the merging route formulated by the distance between adjacent voxels cannot adapt to the complex triangular facet distribution, so the present application selects the vertex of the voxel with a single triangular facet and a single vertex in the central region as the merging starting point, and uses the distance between the vertex in the corresponding neighborhood voxel and the starting point as the merging order judgment standard, that is, the closer the distance, the higher the merging order, and the distance between the vertex in the different neighborhood voxel and the starting point is used to plan the merging order and the second level starting voxel, so as to improve the merging effect of the triangular facet and improve the merging efficiency.

[0016] Compared with the prior art, the present application has the following advantages:

[0017] In the three-dimensional heart image reconstruction system, the three-dimensional image reconstruction module is used to divide the center region and the edge region of the three-dimensional data field, and the starting voxel is selected from the single triangular facet and single vertex voxel in the center region, the single vertex is taken as the starting point, and the corresponding neighborhood voxel is matched, the nearest distance between the vertex and the starting point is taken as the order of evaluation of the merging and the planning of the next level starting voxel, the complex triangular facet distribution state can be met, the merging effect of the triangular facet between the voxels is improved, the merging efficiency is improved, and the invalid merging situation is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 It is the overall structure diagram of the present application;

[0019] Figure 2 It is the basic topological mode schematic diagram of the present application;

[0020] Figure 3 It is the basic topological mode schematic diagram of the present application;

[0021] Figure 4 It is the basic topological mode schematic diagram of the present application;

[0022] Figure 5 It is the basic topological mode schematic diagram of the present application;

[0023] Figure 6 It is the basic topological mode schematic diagram of the present application;

[0024] Figure 7 It is the neighborhood voxel simulation schematic diagram of the present application;

[0025] Figure 8 It is the neighborhood voxel distance simulation diagram of the present application;

[0026] Figure 9 It is the neighborhood voxel distance simulation diagram of the present application;

[0027] Figure 10 It is the three-dimensional reconstruction schematic diagram of the present application.

[0028] The meanings of various labels in the figure are as follows:

[0029] 10, data acquisition module;

[0030] 20, image preprocessing module;

[0031] 30, target image extraction module;

[0032] 40, three-dimensional image reconstruction module. DETAILED DESCRIPTION

[0033] Clearly, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0034] Please refer to Figure 1 As shown in the figure, a three-dimensional heart image reconstruction system is provided, comprising a data acquisition module 10, an image preprocessing module 20, a target image extraction module 30 and a three-dimensional image reconstruction module 40.

[0035] The data acquisition module 10 is used to acquire signals emitted by a CT device through the heart part of the human body and convert them into two-dimensional tomographic images.

[0036] The image preprocessing module 20 pre-processes the two-dimensional tomographic images.

[0037] The target image extraction module 30 formulates corresponding threshold values based on the threshold segmentation algorithm according to the gray value difference of different tissues in the image, and segments the target tissue image from the original image.

[0038] The three-dimensional image reconstruction module 40 is used to perform three-dimensional reconstruction on the target tissue image, and the reconstruction method is as follows:

[0039] S401, construct a three-dimensional data field and divide it into a series of voxels, and acquire the target state in each voxel;

[0040] S402, construct an 8-bit binary based on the vertex state of each voxel, generate a voxel state code, and different voxel state codes correspond to different triangular facets, and establish an index pre-computed lookup table;

[0041] S403, combine the basic topological pattern to process the intersection state of each voxel and the target surface image to form an isosurface, and construct a matching triangular facet;

[0042] S404, acquire the vertex coordinates of each triangular facet, select the vertex coordinates of one of the triangular facets as the starting point, perform neighborhood voxel merging selection, and perform nearest vertex distance calculation in the neighborhood voxel, and formulate a triangular facet merging route according to the calculation result.

[0043] The specific scheme is as follows:

[0044] First, the data acquisition module 10 acquires signals emitted by a CT device through the heart part of the human body and converts them into two-dimensional tomographic images, i.e. the CT device is used to rotate around the human body, emit X-rays and receive signals after passing through the human body, and these signals are converted into two-dimensional tomographic images as the initial image source;

[0045] After the acquisition of the two-dimensional tomographic image is completed, due to the existence of noise interference and insufficient contrast in the two-dimensional tomographic image, in order to improve its clarity, the image preprocessing module 20 is used to preprocess the two-dimensional tomographic image, that is, the noise in the image is removed through the denoising algorithm, so that the image is clearer; and the enhancement algorithm is used to enhance the contrast of the image, so that the originally blurred details become more obvious, thereby improving the quality of the two-dimensional tomographic image and providing a better basis for subsequent segmentation and reconstruction.

[0046] After the preprocessing of the two-dimensional tomographic image is completed, since the tissues and organs of the human body are mixed together in the two-dimensional tomographic image, in order to accurately construct a three-dimensional model, the target organ needs to be separated from the two-dimensional tomographic image, and the target image extraction module 30 is used based on the threshold segmentation algorithm. According to the gray value difference of different tissues in the image, the corresponding threshold is drafted, and the target tissue image is segmented from the original image, that is, the tissue image of the heart organ, of course, other algorithms can be used, such as the segmentation algorithm based on machine learning, through learning a large number of labeled image data, the computer model can automatically identify and segment different organ images.

[0047] Finally, in order to restore the actual state of the heart organ, the three-dimensional image reconstruction module 40 is needed to perform three-dimensional reconstruction on the target tissue image. In the reconstruction process, first, in order to ensure the simulation effect in the later stage, a three-dimensional data field is constructed, and the three-dimensional data field is divided into a series of voxels, wherein the voxel is a cube with the same volume. By splicing the adjacent faces of the adjacent cubes, the final three-dimensional data field is formed;

[0048] When performing three-dimensional reconstruction, the tissue image of the heart organ is input into the three-dimensional data field. At this time, different regions of the tissue image of the heart organ will fall into the voxels at the corresponding positions. The region falling into the voxel at the corresponding position is marked as an isosurface, and its expression is {(x, y, z) | f(x, y, z) = c}, wherein f(x, y, z) represents the scalar field value of any point (x, y, z) in the voxel space. In this scheme, the reconstructed data is derived from the CT device, so the corresponding scalar field value is the CT value. This is because in CT scanning, different tissues have different CT values, and c represents a given CT value constant. By selecting the CT value of the soft tissue of the heart organ, the corresponding isosurface can be extracted, and the corresponding isosurface in the voxel at different positions is marked as the target state.

[0049] In order to further carry out the regionalization simulation, it is necessary to combine the vertex state of each voxel to construct an 8-bit binary to generate a voxel state code, wherein the vertex state of the voxel includes the coordinate position of the vertex, the scalar field value of the vertex and the state of the vertex relative to the isosurface, wherein each voxel includes 8 vertices, the coordinates of the 8 vertices in the three-dimensional data field are the corresponding vertex coordinate positions, the scalar field value corresponding to each vertex is related to its position, and the state of each vertex relative to the isosurface includes being higher than the isosurface and being lower than the isosurface. If the two end points of an edge of a voxel are greater than the isosurface value and smaller than the isosurface value, then the edge must intersect the isosurface, and there is only one intersection point. Therefore, the isosurface image state is located according to the position relationship between the vertices corresponding to each edge and the isosurface, and the vertices in the voxel are discretely sampled to construct the corresponding triangular facet to simulate the current isosurface image state. In theory, there are 256 possible configurations of the interaction between a voxel and an isosurface, and considering rotation and symmetry, the 256 configurations can be simplified to 15 basic topological patterns, i.e. there are 15 triangular facet simulation modes, as shown in 8 Figures 2-6 , wherein 0-7 respectively represent the vertices at different positions of the voxel, case1 to case15 are different basic topological patterns, and the positions and quantities of the corresponding triangular facets also differ;

[0050] In the actual simulation process, the state of each vertex in the voxel is compared with the isosurface inside the end, the vertex lower than the isosurface is marked as 0, and the vertex higher than the isosurface is marked as 1. By combining the vertex state of each voxel, an 8-bit binary is constructed to generate a corresponding voxel state code by sequentially traversing each vertex. In order to improve the simulation efficiency of the triangular facet, the voxel state code matched with each configuration of the triangular facet is bound to generate an index precomputed lookup table, i.e. the corresponding voxel state code is inputted to match the corresponding triangular facet, so as to improve the generation speed of the triangular facet. The generation route of the triangular facet in the index precomputed lookup table is specified, including indicating which edge intersects the isosurface and indicating how to connect the intersection points to form the triangular facet, as shown in Figure 2 , wherein only the vertex 0 is higher than the isosurface, and the remaining vertices are lower than the isosurface. The corresponding voxel state code is 1000 0000, and the intersection points of the connected triangular facets are the intersection points of vertices 0-1, the intersection points of vertices 0-4 and the intersection points of vertices 0-3.

[0051] For the intersection point calculation of each edge, linear interpolation is used, and the specific formula is as follows:

[0052]

[0053] ​P1 and P2 are two end points of the intersecting edge, V1 and V2 are scalar field values of the corresponding end points, which are related to the positions, isovalue is the value of the isovalue surface, that is, the specified CT value, the intersection points of each intersecting edge are calculated in turn, and finally the normal vector of the eight vertices of the current hexahedron is obtained by using the central difference method, the normal of each vertex of the triangular patch is obtained by using the linear interpolation method, and the triangular patches are connected according to the coordinates of the vertices of each triangular patch and the vertex normal vector.

[0054] After completing the triangular patch simulation in each voxel, since a single triangular patch simulation is only a part of the heart model, in order to obtain the final three-dimensional heart image, adjacent triangular patches need to be merged. In the merging process, the vertex coordinates of each triangular patch are first obtained, a three-dimensional rectangular coordinate system is established in the voxels at the edge positions in the three-dimensional data field, the coordinates of the corresponding vertices are obtained according to the arrangement position of each voxel, and the vertex coordinates of one of the triangular patches are selected as the starting point. It should be noted that, in order to ensure that the entire three-dimensional heart image can be completely reconstructed and analyzed, the projected heart image is generally close to the center position of the three-dimensional data field during the projection process, so that the isosurfaces of different regions can be projected into the voxels at the corresponding positions. Therefore, the starting point is required to be close to the center position of the three-dimensional data field, and therefore it is necessary to divide the starting point selection range. According to the coordinate origin position, the amount of stacked voxels is obtained, and the central region and the edge region are divided according to the amount of voxels. The central region and the edge region are divided according to the ratio of the projected heart image prediction volume to the overall voxel accumulation volume. Under normal conditions, the projected heart image prediction volume changes little, and the smaller the divided volume is, the more accurate the final simulated three-dimensional heart image is. Therefore, the central region and the edge region are associated with the volume of the voxels, and the voxels in the central region are selected as the starting point.

[0055] Since each voxel is spliced by adjacent surfaces, the number of adjacent voxels of each voxel is 6, 5, 4, and 3, respectively. Therefore, according to the position of each voxel, the corresponding adjacent voxels and the number of adjacent voxels can be determined, and the corresponding adjacent voxels and the number of adjacent voxels are bound to generate an adjacent voxel set table, as shown in Figure 7 , wherein the shaded voxels are the starting voxels, and the solid line voxels adjacent thereto are the adjacent voxels, which serve as the basis for subsequent selection and reduce the selection workload.

[0056] Further, since the triangular patches formed by the voxels in the central region may be multiple, the corresponding vertices thereof will also be multiple. In order to improve the efficiency of subsequent starting point determination, in this scheme, the voxels with only one vertex in the divided central region are selected as the starting point, as shown in Figure 2In case 2, only one vertex 0 exists.

[0057] When the starting point is determined, the voxel where the starting point is located is marked as the starting voxel, the coordinates of each vertex in the starting voxel are located to position the current voxel, the voxel position is input to the neighbor voxel set table, the corresponding neighbor voxel is obtained, and the coordinates of the corresponding vertices of each triangular facet in the neighbor voxel are obtained according to the established rectangular coordinate system. Here, the vertex coordinates refer to the vertex coordinates corresponding to each triangular facet of the neighbor voxel. Since the number of vertex coordinates corresponding to different triangular facets is different, the closest vertex coordinates are used as the connection points in the present scheme, and the connection points in each triangular facet and the starting point are sequentially connected, the distance therebetween is calculated, and the shortest distance is used as the distance threshold value. Figure 8 As shown in FIG. 2, the shaded part in the figure is the starting voxel, and the vertex at the top left front of the starting voxel is used as the starting point. There are two vertices in the corresponding neighbor voxel, and the distances of the two vertices from the starting point are d1 and d2 respectively. d1 is the closest distance, and d2 is the farthest distance. Therefore, the vertex corresponding to d1 is the connection point of the neighbor voxel. Figure 9 As shown in FIG. 3, the shaded part in the figure is the starting voxel, and the vertex at the top left front of the starting voxel is used as the starting point. There are four vertices in the corresponding neighbor voxel, and the closest distance is d3 and the farthest distance is d4. At this time, the vertex corresponding to d3 is used as the connection point of the neighbor voxel.

[0058] Further, the closest distance between the vertex of the triangular facet corresponding to the neighbor voxel of the voxel where the starting point is located and the starting point is sequentially calculated, and a distance threshold value is established. In the present scheme, the distance between the two opposite vertices in the same face of the voxel is used as the distance threshold value, and the distance threshold value is compared.

[0059] When the shortest distance between each vertex of the triangular facet corresponding to the neighbor voxel and the starting point exceeds the distance threshold value, the neighbor voxel is marked as a pre-starting voxel. At this time, the longest distance between each vertex of the triangular facet corresponding to the pre-starting voxel and the starting point is calculated, the longest distances of each pre-starting voxel are compared, the pre-starting voxel with the longest distance is marked as the starting voxel of the next order, and the vertex corresponding to the longest distance is used as the pre-starting point. When there are multiple pre-starting voxels with the same longest distance, the starting order is up-down-left-right-front-back, that is, the starting voxel above the starting voxel has the highest order, and the starting voxel behind the starting voxel has the lowest order.

[0060] When the shortest distance between one of the triangular facets corresponding vertex and the starting point in the neighborhood voxel is not more than the distance threshold, then the neighborhood voxel is merged as a neighborhood voxel, and the shortest distance between the vertex and the starting point in each merged neighborhood voxel is calculated, marked as the merging distance, and the merging order is planned according to the merging distance, the shorter the distance, the earlier the order, when the merging distance in multiple merged neighborhood voxels is the same, the nearest distance between the corresponding vertex of the remaining triangular facet and the starting point in the corresponding merged neighborhood voxel is calculated, and the merging order is still planned according to the nearest distance, the shorter the distance, the earlier the order, when the nearest distance between each triangular facet corresponding vertex and the starting point in multiple merged neighborhood voxels is the same, the merging order is up-down-left-right-front-back.

[0061] It is worth noting that in the process of merging triangular facets, distance is mainly used as the basis for merging, so when the distance of the triangular facets in the same voxel is less than the merging distance in the neighborhood voxel, the triangular facets in the same voxel are preferentially merged, and the present scheme plans the merging order of the triangular facets in the neighborhood voxel after the triangular facets in the same voxel have been merged.

[0062] Finally, the triangular facets in the starting voxel and the triangular facets in the neighborhood voxel are merged according to the merging order, and after the triangular facet merging work of the current starting voxel is completed, the neighborhood voxel with the highest starting order is selected as the second-level starting voxel according to the starting order to perform the triangular facet merging work, and a merged route is generated, and all triangular facets in the three-dimensional data field are merged according to the merged route, and the three-dimensional heart image reconstruction work is completed.

[0063] The present application divides the center area and the edge area of the three-dimensional data field through the three-dimensional image reconstruction module 40, selects a single triangular facet and a single vertex voxel as a starting voxel from the center area, takes the single vertex as a starting point, matches the corresponding neighborhood voxel, and takes the nearest distance between the vertex and the starting point as the evaluation order of merging and the planning of the next-level starting voxel, which can meet the complex triangular facet distribution state, thereby improving the merging effect of the triangular facets between voxels, improving the merging efficiency, and reducing the occurrence of invalid merging.

[0064] The basic principles, main features and advantages of the present application are shown and described above. Those skilled in the art should understand that the present application is not limited by the above examples, the above examples and descriptions in the specification are only preferred examples of the present application, and are not intended to limit the present application, various changes and improvements can be made to the present application without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A three-dimensional cardiac image reconstruction system, characterized in that: It includes a data acquisition module (10), an image preprocessing module (20), a target image extraction module (30), and a three-dimensional image reconstruction module (40); The data acquisition module (10) is used to acquire signals from the CT equipment that irradiates the area of ​​the human heart and convert them into two-dimensional tomographic images. The image preprocessing module (20) preprocesses the two-dimensional tomographic image; The target image extraction module (30) uses a threshold-based segmentation algorithm to determine the corresponding threshold based on the difference in gray values ​​of different tissues in the image, and segments the target tissue image into the original image. The three-dimensional image reconstruction module (40) is used to perform three-dimensional reconstruction of the target tissue image, and the reconstruction method is as follows: S401. Construct a three-dimensional data field and divide the three-dimensional data field into a series of voxels, and collect the target state in each voxel; S402. Combine the vertex states of each voxel to construct an 8-bit binary code and generate a voxel status code. Different voxel status codes correspond to different triangles. Establish an index pre-computed lookup table. S403. Combine the basic topological pattern to process the intersection state of each voxel with the target surface image to form an isosurface, and construct a matching triangular patch. S404. Obtain the vertex coordinates of each triangular facet, select the vertex coordinates of one triangular facet as the starting point, perform neighborhood voxel merging selection, calculate the distance to the nearest vertex among the neighborhood voxels, and formulate the triangular facet merging route according to the calculation results. The method for defining the merging route of triangular facets in S404 includes the following steps: S4043. Based on the location of each voxel, determine its corresponding neighboring voxels and the number of neighboring voxels, and bind the corresponding neighboring voxels and the number of neighboring voxels to generate a neighboring voxel set table. S4044. Mark the voxel where the starting point is located as the starting voxel, obtain the coordinates of each vertex in the starting voxel to locate the current voxel position, input the voxel position to the neighboring voxel set table, and obtain the corresponding neighboring voxels. S4045. Connect the connection points and the starting point in each triangular facet of different neighboring voxels in sequence, calculate the distance between them, obtain the nearest distance between the voxel where the starting point is located and the vertex of the triangular facet corresponding to its neighboring voxels, set a distance threshold, and compare the distance thresholds. When the shortest distance between the vertex corresponding to each triangle in the neighborhood voxel and the starting point exceeds the distance threshold, the neighborhood voxel is marked as a pre-starting voxel. The longest distance between the vertex corresponding to each triangle in the pre-starting voxel and the starting point is calculated. The longest distance of each pre-starting voxel is compared. The pre-starting voxel with the longest distance is marked as the next starting voxel in sequence, and the vertex corresponding to the longest distance is taken as the pre-starting point. When there are multiple pre-starting voxels with the same longest distance, their starting order is top-bottom-left-right-front-back, that is, the starting voxel above the starting voxel has the highest order, and the starting voxel behind the starting voxel has the lowest order. When the shortest distance between the vertex corresponding to one of the triangles in a neighborhood voxel and the starting point does not exceed the distance threshold, the neighborhood voxel is considered a merged neighborhood voxel. The shortest distance between the vertex and the starting point in each merged neighborhood voxel is calculated and marked as the merge distance. The merge order is planned according to the merge distance. When the merge distances in multiple merged neighborhood voxels are the same, the shortest distance between the vertex corresponding to the other triangles in the corresponding merged neighborhood voxel and the starting point is calculated. The merge order is still planned according to the shortest distance. When the shortest distances between the vertices corresponding to each triangle in multiple merged neighborhood voxels and the starting point are all the same, the merge order is top-bottom-left-right-front-back. S4046. Merge the triangular facets in the starting voxel and the triangular facets in the neighboring voxels according to the merging order. Select the neighboring voxel with the highest starting order as the second-level starting voxel for triangular facet merging, generate a merging route, and complete the merging of all triangular facets in the 3D data field according to the merging route.

2. The three-dimensional cardiac image reconstruction system according to claim 1, characterized in that: The method for constructing a three-dimensional data field in S401 includes the following steps: S4011. Implant the tissue images of the heart organ into a three-dimensional data field; S4012. Mark the region falling within the voxel at the corresponding position as an isosurface, the expression of which is {(x, y, z)|f(x, y, z)=c}, where f(x, y, z) represents the scalar field value at any point (x, y, z) in the voxel space, and c represents the given CT value constant. S4013. Mark the corresponding isosurfaces in voxels at different locations as the target state.

3. The three-dimensional cardiac image reconstruction system according to claim 1, characterized in that: The vertex state of the voxel in S402 includes the vertex's coordinate position, the vertex's scalar field value, and the vertex's state relative to the isosurface.

4. The three-dimensional cardiac image reconstruction system according to claim 3, characterized in that: The lookup table in S402, which is pre-computed by the index, specifies the generation path of the triangular facets, including indicating the intersection of different edges with isosurfaces and indicating the connection of intersection points to form triangular facets.

5. The three-dimensional cardiac image reconstruction system according to claim 4, characterized in that: In step S402, the intersection points of each triangular facet are calculated using linear interpolation, and the specific formula is as follows: Where P1 and P2 are the two endpoints of the intersecting edge, V1 and V2 are the scalar field values ​​of the corresponding endpoints, which are related to their positions, and isovalue is the value of the isosurface.

6. The three-dimensional cardiac image reconstruction system according to claim 1, characterized in that: The method for obtaining the vertex coordinates of each triangular facet in S404 includes the following steps: S4041. Establish a three-dimensional rectangular coordinate system in the voxels at the corner positions in the three-dimensional data field; S4042. Based on the arrangement of each voxel, obtain the coordinates of its corresponding vertex.

7. The three-dimensional cardiac image reconstruction system according to claim 6, characterized in that: The starting point selected in S404 is selected from the central region of the three-dimensional data field, and is selected from the voxels in the divided central region that have only one vertex.

8. The three-dimensional cardiac image reconstruction system according to claim 1, characterized in that: The connection point in S4045 is the coordinate of the nearest vertex in the triangular facet.

9. The three-dimensional cardiac image reconstruction system according to claim 8, characterized in that: The distance threshold in S4045 is the distance between two diagonal vertices on the same face of a voxel.

Images