Sorting-based interpolation method for systolic and diastolic coronary arteries

Abstract

The invention relates to the technical field of medical 3D point cloud processing technologies, in particular to a sorting-based interpolation method for systolic and diastolic coronary arteries. A central line is used for representing a topological structure of a coronary artery, and the central line is divided into a plurality of sections through bifurcation points. According to the sorting method based on the cube, disordered center line segment point clouds are sorted along a curve. A deformation field can be obtained by comparing the central line segments of the systolic period and the diastolic period. And performing interpolation processing on the deformation field to obtain a point-by-point displacement vector of the center line. The displacement vectors are added to the coronary artery points, and point-by-point deformation of the coronary artery can be achieved. Through dense interpolation, a dynamic three-dimensional model can be generated on the point clouds of the systolic period and the diastolic period. The three-dimensional point cloud of the mapping path at any moment is extracted and can be used as supplementary data, and a data set of coronary artery diseases is perfected.

Description

Technical Field

[0001] The present invention relates to the technical field of medical 3D point cloud processing, and more specifically, to a sorting-based interpolation method for coronary arteries in systole and diastole. Background Art

[0002] The diagnosis of coronary artery disease (CAD) usually relies on computed tomography (CT) images. CT images are a way to describe the cross-section of the human body, which is different from the intuitive perception of the human eye. This requires doctors to conduct a large amount of learning and training in order to perform accurate medical diagnoses. With the development of computer technology, three-dimensional models can be quickly reconstructed from CT images. The three-dimensional CT model can more intuitively display the degree and location of blood vessel stenosis. However, due to the inability of the contrast agent to completely fill the blood vessels, resulting in missing imaging, this may lead to misdiagnosis. In addition, due to the inability to continuously inject the contrast agent, the three-dimensional coronary artery model is static and cannot display the dynamic location of stenosis in real time.

[0003] In recent years, deep learning technology has gradually entered the medical field. Some researchers have tried to train an offline model to output the probability that a patient has CAD by inputting a three-dimensional CT model as a diagnostic reference. Some other studies have tried to directly display the possible locations of stenosis in the three-dimensional model to help doctors narrow the scope of examination. These methods all rely on a large amount of data to learn diagnostic strategies. However, due to ethical and annotation difficulties, it is very difficult to collect large-scale medical data sets. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiency of the existing technology that it is relatively difficult to collect coronary artery data, and to provide a sorting-based interpolation method for coronary arteries in systole and diastole, which effectively expands the coronary artery-related data and is conducive to generating a dynamic three-dimensional model of the coronary artery.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is:

[0006] Provide a sorting-based interpolation method for coronary arteries in systole and diastole, including the following steps:

[0007] S1. Input the 3D point cloud data of the coronary artery blood vessels;

[0008] S2. Extract the centerline of the coronary artery;

[0009] S3. Calculate the bifurcation points of the centerline according to the adjacent relationship between points in 3D space; and split the centerline into different centerline segments according to the bifurcation points;

[0010] S4. Extract the centerline segments and downsample the total number of point clouds;

[0011] S5. Iteratively calculate the span of the sampled point cloud based on a cube and sort it along the curve trend direction;

[0012] S6. Subtract the centerline point cloud of the systolic and diastolic phases to obtain the deformation field of the centerline point cloud;

[0013] S7. Cluster the systolic coronary artery point cloud along the centerline normal direction based on a cuboid and assume that the displacement field between the clustered coronary artery point cloud and the centerline point cloud is consistent;

[0014] S8. Calculate the deformation field of the blood vessel points and interpolate the deformation field of the blood vessel points to obtain the displacement vector of each point of the systolic coronary artery point cloud;

[0015] S9. Add the displacement vector to the systolic coronary artery point cloud to obtain the deformed point cloud data.

[0016] A sorting-based interpolation method for systolic and diastolic coronary arteries provided by the present invention uses the centerline to represent the topological structure of the coronary artery and divides the centerline into several segments through bifurcation points. The sorting method based on a cube in the present invention sorts the unordered centerline segment point cloud along the curve. By comparing the centerline segments of the systolic and diastolic phases, the deformation field can be obtained. Through interpolation processing of the deformation field, the displacement vector of each point of the centerline can be obtained. Adding the displacement vector to the coronary artery points can realize the point-by-point deformation of the coronary artery. Through dense interpolation, a dynamic three-dimensional model can be generated on the point clouds of the systolic and diastolic phases. The three-dimensional point cloud of the mapping path at any time is extracted and can be used as supplementary data to improve the dataset of coronary artery diseases.

[0017] Further, the step S3 specifically includes:

[0018] S31. Voxelize the centerline point cloud and search point by point;

[0019] S32. According to the number of surrounding adjacent points, the attribute of the centerline point is defined as:

[0020]

[0021] In the formula, represents the attribute of point x, and O(x) represents the number of points directly adjacent to x; during the iteration process, if then the iterative point x belongs to a new class; if then mark this point; if then mark x as the category of the adjacent point;

[0022] S33. Split the centerline into different centerline segments according to the attribute of point x.

[0023] Further, in step S33, during the process of iterating all centerline points, if then the iteration point x belongs to a new class; if If there are labeled points in the current cube, the iteration point x is labeled as the same class, and if there are no labeled points, it is labeled as a new class; if then x is labeled as a point to be removed; by clustering all points of the same class and removing the branch points, i.e., the points to be removed, different centerline segments are obtained.

[0024] Further, in step S5, the span is calculated in the following way:

[0025] For the extraction part C of the blood vessel centerline c Calculate the spans on the x, y, and z axes respectively. The span s is defined as follows:

[0026]

[0027] In the formula, is the maximum value function, represents the minimum value, represents the set of the center points divided by the cube, and i represents the coordinates of the three coordinate axes {x, y, z}; the span is used to characterize the trend of the three-dimensional curve on the coordinate axis.

[0028] Further, in step S5, the extracted centerline is sorted along the curve trend direction: where C sorted represents the sorted point cloud, represents the sorting function, and a is the trend axis.

[0029] Further, in step S6, the deformation field of the centerline point cloud is calculated by the following formula: In the formula, represents the deformation field of the centerline, C s , C d represent the coronary artery centerlines in the systolic and diastolic phases respectively.

[0030] Further, step S7 includes:

[0031] S71. Taking the centerline points as the center, performing coronary artery point clustering based on a cuboid; where the cuboid is defined as follows:

[0032]

[0033] In the formula, Indicates the region for extracting vascular points based on centerline points; Cuboid represents the function for defining the region; o is the center point of the cuboid, i.e., the centerline point; 1 represents the length, set to 1; w is the width, set to 1; h represents the height of the cuboid, determined by the distance between adjacent centerline points;

[0034] S72. Align the clustered points with the deformation field of the centerline points.

[0035] Further, the step S8 includes:

[0036] S81. Calculate the deformation field of the vascular points:

[0037]

[0038] In the formula, represents the set of extracted vascular points, is the deformation vector of the centerline point x, and represents the set of points

[0039] S83. Obtain the point-by-point displacement vector of the systolic coronary artery points by interpolating the of the vascular point deformation field

[0040] The present invention also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned method are implemented.

[0041] A computer-readable storage medium of the present invention stores a computer program thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method are implemented.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] 1. A sorting-based interpolation method for systolic and diastolic coronary arteries of the present invention uses the centerline to characterize the topological structure of the coronary artery and divides the centerline into several segments through bifurcation points. The sorting method based on the cube of the present invention sorts the unordered centerline segment point clouds along the curve. By comparing the centerline segments in the systolic and diastolic phases, the deformation field can be obtained. Interpolation processing is performed on the deformation field to obtain the point-by-point displacement vector of the centerline. Adding the displacement vector to the coronary artery points can achieve the point-by-point deformation of the coronary artery. Through dense interpolation, a dynamic three-dimensional model can be generated on the point clouds in the systolic and diastolic phases. The three-dimensional point cloud of the mapping path at any time is extracted and can be used as supplementary data to improve the dataset of coronary artery diseases.

[0044] 2. The sorting-based interpolation method for systolic and diastolic coronary arteries provided by the present invention can generate a dynamic three-dimensional model through interpolation, which can visually display the location and deformation path of coronary artery stenosis, assisting doctors in making more accurate medical diagnoses. At the same time, by extracting the interpolation results at any moment, samples for supplementing the coronary artery disease dataset can be obtained to help train some medical large models. Therefore, the present invention has important commercial potential. In addition, the proposed method is an exploration of point cloud data interpolation. Besides generating a dynamic coronary artery model, it also has an inspiring effect on other non-rigid point cloud registration and interpolation methods, reflecting its scientific research value. Description of the Drawings

[0045] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0046] Figure 2 It is a schematic diagram of the flow of Embodiment 1 of the present invention. Detailed Embodiments

[0047] The present invention will be further described below in conjunction with the detailed embodiments. Among them, the drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as a limitation to the present invention; in order to better illustrate the embodiments of the present invention, some components in the drawings will be omitted, enlarged or reduced, which do not represent the dimensions of the actual products; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0048] In the drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the drawings are only for illustrative purposes and should not be construed as a limitation to the present invention. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0049] Embodiment 1

[0050] This embodiment is the first embodiment of a sorting-based interpolation method for systolic and diastolic coronary arteries. Due to the complex connections between adjacent points of the coronary artery point cloud and its inherent topological structure, this embodiment transforms the coronary artery point cloud into a vascular center line segment; through sorting and differencing, the deformation fields of the systolic and diastolic center line segments are obtained. Interpolating the deformation field can obtain the displacement vector of each point. By slicing, the displacement vector is attached to the coronary artery point cloud, thereby obtaining the mapping path from systole to diastole. Interpolating this path can obtain a dynamic three-dimensional model. Extracting the interpolation results at any moment on this path as augmented data. As Figure 1 shown, the specific steps are as follows:

[0051] S1. Input the 3D point cloud data of the coronary artery vessel;

[0052] S2. Extract the centerline of the coronary artery through the VMTK tool;

[0053] S3. Calculate the bifurcation points of the centerline according to the adjacent relationship between points in 3D space; according to the bifurcation points, split the centerline into different center line segments;

[0054] S4. Extract the center line segment and downsample the total number of point clouds to 200;

[0055] S5. Iteratively calculate the span of the sampled point cloud based on a cube and sort it along the curve trend direction;

[0056] S6. Subtract the centerline point clouds of systole and diastole to obtain the deformation field of the centerline point cloud;

[0057] S7. Cluster the systolic coronary artery point cloud along the centerline normal direction based on a cuboid and assume that the displacement field of the clustered coronary artery point cloud is consistent with the centerline point cloud;

[0058] S8. Calculate the deformation field of the vascular points and interpolate the vascular point deformation field to obtain the displacement vector of each point of the systolic coronary artery point cloud;

[0059] S9. Add the displacement vector to the systolic coronary artery point cloud to obtain the deformed point cloud data.

[0060] A sorting-based interpolation method for systolic and diastolic coronary arteries provided in this embodiment uses the centerline to characterize the topological structure of the coronary artery and divides the centerline into several segments through bifurcation points. Based on the cube sorting method of the present invention, the unordered centerline segment point cloud is sorted along the curve. By comparing the centerline segments in the systolic and diastolic phases, a deformation field can be obtained. Through interpolation processing of the deformation field, the displacement vector of each point on the centerline can be obtained. Adding the displacement vector to the coronary artery points can achieve the point-by-point deformation of the coronary artery. Through dense interpolation, a dynamic three-dimensional model can be generated on the point clouds in the systolic and diastolic phases. Extracting the three-dimensional point cloud of the mapping path at any time can be used as supplementary data to improve the dataset of coronary artery diseases.

[0061] As Figure 2 shown, for the segmentation of the centerline:

[0062] The centerline is a set of scattered points in three-dimensional space, having certain line features, such as the close coordinate values of adjacent points. According to the number of adjacent points owned by different points, these points can be named as starting points (s), middle points (m), and branching points (b). The centerline of the blood vessel is divided according to the bifurcation points to obtain different line segments. Since the centerline of the blood vessel is at different curvatures and the distances between adjacent points are different, it is impossible to directly predict the attributes of the centerline points. In this embodiment, step S3 specifically includes:

[0063] S31. Voxelize the centerline point cloud and search point by point;

[0064] S32. According to the number of surrounding adjacent points, the attribute of the centerline point is defined as:

[0065]

[0066] In the formula, represents the attribute of point x, and O(x) represents the number of points directly adjacent to x; according to the attribute of point x, the centerline is split into different centerline segments.

[0067] During the process of iterating through all centerline points, if then the iterated point x belongs to a new class; if If there are already marked points in the current cube, the iterated point x is marked as the same class, and if there are no already marked points, it is marked as a new class; if then x is marked as a point to be removed; by clustering all points of the same class and removing the branching points, that is, the points to be removed, different centerline segments are obtained.

[0068] As Figure 2 shown, for the deformation field of the centerline point cloud:

[0069] Point-by-point deformation field prediction can indicate the displacement path of points. For the non-rigid registration of coronary arteries is achieved by coupling the systolic points V s and the deformation field as follows:

[0070]

[0071] Due to the disorder of the point cloud, it is difficult to directly obtain the displacement field. The centerline is a representation of the coronary artery, which not only has the characteristic of bending but also reduces the number of points. Therefore, this embodiment proposes a cube-based sorting method.

[0072]

[0073] Among them, [C s , C d respectively represent the coronary artery centerlines in the systolic and diastolic phases, f θ represents the calculation method of the deformation field, represents the deformation field of the centerline. The representation form of the point cloud is a vector in three-dimensional space. When these points are stored in an ordered manner, the estimation of the deformation field is as follows:

[0074]

[0075] The centerline of the coronary artery is extracted disorderly. It is incorrect to try to obtain the deformation field by vector subtraction. The point cloud should be sorted first. The blood vessels show different forms of twisting and bending in the body, and the coordinates of the centerline overlap in any dimension of the three-dimensional space. Therefore, using coordinate sorting is ineffective. According to the above cube-based out-degree calculation method, the centerline shows a twisted morphology on the whole scale, but when the cube scale is very small, the included centerline is considered approximately straight. The extracted part (C c ) of the blood vessel centerline calculates the spans on the x, y, and z axes respectively. The span is used to characterize the trend of the three-dimensional curve on the coordinate axis. The extracted centerline can be sorted along the trend.

[0076] Therefore, in this embodiment, step S5 specifically includes:

[0077] S51. The span is calculated as follows:

[0078] For the extracted part C c of the blood vessel centerline, the spans on the x, y, and z axes are calculated respectively, and the span s is defined as follows:

[0079]

[0080] In the formula, is the maximum value function, Indicates the minimum value, Indicates the set of center points divided by the cube, where i represents the coordinates of the three coordinate axes {x, y, z}; the span is used to characterize the trend of the three-dimensional curve on the coordinate axis.

[0081] S52. Sort the extracted centerline along the curve trend direction: Among them, C sorted Indicates the sorted point cloud, Indicates the sorting function, and a is the trend axis.

[0082] After sorting according to step S5, in step S6, the deformation field of the centerline point cloud is calculated by the following formula: In the formula, Indicates the deformation field of the centerline, C s and C d respectively represent the coronary artery centerlines in the systolic and diastolic phases.

[0083] As Figure 2 shown, for the interpolation of the coronary artery intermediate phase:

[0084] The registration of blood vessels depends on the point-by-point deformation vector. The displacement of the coronary artery in the body is small, and the displacement vectors of adjacent blood vessel points are similar. In CT images, the blood vessel surface has characteristics similar to the center points. Use the centerline to cluster the blood vessel points and assume that the displacement vector of the clustered points is the same as that of the centerline points. By slicing the coronary artery points, the displacement vector of each part can be derived. This embodiment proposes a cuboid-based point extraction method, which uses the coordinates of the centerline for clustering and obtaining slices.

[0085] In this embodiment, step S7 includes:

[0086] S71. Centering on the centerline points, perform coronary artery point clustering based on a cuboid; where the cuboid is defined as follows:

[0087]

[0088] In the formula, Indicates the area for extracting blood vessel points based on the centerline points; Cuboid represents the function for defining the area; o is the center point of the cuboid, that is, the centerline point; 1 represents the length, set to 1; w is the width, set to 1; h represents the height of the cuboid, determined by the distance between adjacent centerline points;

[0089] S72. Keep the clustered points consistent with the deformation field of the centerline points.

[0090] In this embodiment, step S8 includes:

[0091] S81. Calculate the deformation field of vascular points:

[0092]

[0093] wherein, represents the extracted vascular point set, is the deformation vector of the centerline point x, and represents the set the points in

[0094] S83. By interpolating the of the vascular point deformation field, obtain the point-by-point displacement vector of the systolic coronary artery points

[0095] A sorting-based interpolation method for systolic and diastolic coronary arteries in this embodiment uses the centerline to characterize the topological structure of the coronary artery and divides the centerline into several segments through bifurcation points. The sorting method based on cubes in the present invention sorts the unordered centerline segment point clouds along the curve. By comparing the centerline segments in the systolic and diastolic phases, the deformation field can be obtained. Interpolation processing is performed on the deformation field to obtain the point-by-point displacement vector of the centerline. Adding the displacement vector to the coronary artery points can achieve the point-by-point deformation of the coronary artery. Through dense interpolation, a dynamic three-dimensional model can be generated on the point clouds in the systolic and diastolic phases. The three-dimensional point cloud of the mapping path at any moment is extracted and can be used as supplementary data to improve the dataset of coronary artery diseases.

[0096] A sorting-based interpolation method for systolic and diastolic coronary arteries provided in this embodiment can generate a dynamic three-dimensional model through interpolation, which can intuitively display the location of coronary artery stenosis and the deformation path, assisting doctors in more accurate medical diagnosis. At the same time, by extracting the interpolation results at any moment, samples for supplementing the coronary artery disease dataset can be obtained to help train some medical large models. Therefore, the present invention has important commercial potential. In addition, the proposed method is an exploration of point cloud data interpolation. Besides generating a dynamic coronary artery model, it also has an inspiring effect on other non-rigid point cloud registration and interpolation methods, reflecting its scientific research value.

[0097] A sorting-based interpolation method for systolic and diastolic coronary arteries in this embodiment: 1. High accuracy: The calculation of deformation is made possible through centerline representation. Additionally, the point-by-point mapping path of the coronary artery point cloud is deduced and interpolated on the time axis, thus ensuring the accuracy of the generated coronary artery data. 2. Less data required: The proposed method only requires systolic and diastolic point clouds, with relatively low requirements for data acquisition. 3. Strong scalability: With the development of computer technology and medical technology, the present invention can be updated and iterated online. 4. Many potential application scenarios: The proposed method is mainly aimed at coronary arteries. However, for other blood vessels, such as cerebral blood vessels, etc., it has potential application scenarios.

[0098] Embodiment 2

[0099] This embodiment is an embodiment of a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method described in Embodiment 1 are implemented.

[0100] Embodiment 3

[0101] This embodiment is an embodiment of a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in Embodiment 1 are implemented.

[0102] In the specific content of the above specific implementation manners, the technical features can be combined arbitrarily without contradiction. For the sake of brevity of description, not all possible combinations of the above technical features are described. However, as long as the combinations of these technical features do not exist in contradiction, they should be considered as the scope recorded in this specification.

[0103] Obviously, the above embodiments of the present invention are merely examples for clearly explaining the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A sorting-based interpolation method for systolic and diastolic coronary arteries, characterized in that: The following steps are involved: S1. Input 3D point cloud data of coronary arteries; S2. Extract the centerline of the coronary artery; S3. Calculate the bifurcation point of the center line according to the adjacent relationship between points in the 3D space; split the center line into different center line segments according to the bifurcation point; S4. extract the center line segment and downsample the total number of point clouds; S5. Iteratively calculate the span of the sampled point cloud based on the cube and sort it along the trend direction of the curve; S6. Subtract the centerline point cloud during the systolic period from the centerline point cloud during the diastolic period to obtain a deformation field of the centerline point cloud; S7. Cluster the systolic coronary point cloud along the normal direction of the center line based on the cuboid, and assume that the clustered coronary point cloud is consistent with the point cloud displacement field of the center line; S8. Calculate the deformation field of the blood vessel point, and interpolate the deformation field of the blood vessel point to obtain the displacement vector of the coronary point cloud point by point during the systolic period; S9. Add the displacement vector to the systolic coronary artery point cloud to obtain deformed point cloud data.

2. The method for interpolation of coronary arteries in systolic and diastolic phases according to claim 1, characterized in that: The step S3 specifically includes: S31. voxelize the center line point cloud and search point by point; S32. According to the number of surrounding neighboring points, the attributes of the centerline point are defined as: In the formula, represents the attribute of point x, O(x) represents the number of points directly adjacent to x; during the iteration process, if Then the iteration point x belongs to the new class; if Then mark the point; if Then mark x as the category of the adjacent points; S33. Split the center line into different center line segments according to the attributes of point x.

3. The method for interpolation of coronary arteries in systolic and diastolic phases according to claim 3, characterized in that: In step S33, during the iteration of all centerline points, if Then the iteration point x belongs to the new class; if If there is a marked point in the current cube, the iterated point x is marked as the same class. If there is no marked point, it is marked as a new class. Then x is marked as the point to be removed; by clustering all points of the same category and removing the branch points, i.e. the points to be removed, different center line segments are obtained.

4. The method for interpolation of coronary arteries in systolic and diastolic phases according to claim 1, characterized in that: In step S5, the span is calculated in the following way: For the extraction part C of the blood vessel centerline c Calculate the spans on the x, y, and z axes respectively. The span s is defined as follows: In the formula, is the maximum value function, Indicates the minimum value, It represents the set of center points divided by the cube, i represents the coordinates of the three coordinate axes {x, y, z}; the span is used to characterize the trend of the three-dimensional curve on the coordinate axis.

5. The method for interpolation of coronary arteries in systolic and diastolic phases according to claim 4, characterized in that: In step S5, the extracted center lines are sorted along the curve trend direction: Among them, C sorted represents the sorted point cloud, represents the sorting function, and a is the trend axis.

6. The method for sorting-based interpolation of coronary arteries in systolic and diastolic phases according to claim 5, characterized in that: In step S6, the deformation field of the centerline point cloud is calculated by the following formula: In the formula, represents the deformation field of the center line, C s , C d Represent the coronary artery centerline during systole and diastole, respectively.

7. The method for interpolation of coronary arteries in systolic and diastolic phases according to claim 6, characterized in that: The step S7 comprises: S71. Cluster the coronary artery points based on a cuboid with the center line point as the center; wherein the cuboid is defined as follows: In the formula, represents the region used to extract blood vessel points based on the centerline points; Cuboid represents the function that defines the region; o is the center point of the cuboid, i.e., the centerline point; 1 represents the length, which is set to 1; w is the width, which is set to 1; h represents the height of the cuboid, which is determined by the distance between adjacent centerline points; S72. Keep the deformation field of the clustered points consistent with that of the center line points.

8. The method for interpolation of coronary arteries in systolic and diastolic phases according to claim 6, characterized in that: The step S8 comprises: S81. Calculate the deformation field of the blood vessel point: In the formula, represents the extracted blood vessel point set, is the deformation vector of the centerline point x, and Representing a collection Points in S83. Deformation field of blood vessel points by interpolation Get the point-by-point displacement vector of the systolic coronary artery point cloud 9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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