Cardiac three-dimensional mapping method and system

Abstract

A cardiac three-dimensional mapping method and system, which belong to the technical field of image mapping. The method comprises: collecting cardiac ultrasound image sequences within a plurality of cardiac cycles, wherein each cardiac ultrasound image sequence comprises a plurality of cardiac ultrasound images; performing image enhancement and edge detection processing on the cardiac ultrasound images to obtain optimized cardiac ultrasound images; registering the optimized cardiac ultrasound images on the basis of a correlation matching algorithm, so as to ensure that the optimized cardiac ultrasound images are in the same coordinate system; performing a three-dimensional reconstruction operation on the registered optimized cardiac ultrasound images, so as to obtain a three-dimensional cardiac model; introducing the three-dimensional cardiac model, and performing layered mapping on the three-dimensional cardiac model by using a morphological analysis method; and outputting the mapped three-dimensional cardiac model. Collected cardiac ultrasound images are subjected to image enhancement and edge detection and are then registered, and a three-dimensional cardiac model is then established and is subjected to layered mapping, such that the display clarity of the three-dimensional cardiac model and the accuracy and reliability of a three-dimensional cardiac structure mapping result are improved.

Description

A three-dimensional cardiac mapping method and system

[0001] This application is based on the Chinese patent application with application number 202310987154.3 and application date of August 7, 2023, and claims the priority of the Chinese patent application. The entire content of the Chinese patent application is hereby introduced into this application as a reference. Technical Field

[0002] The present invention belongs to the field of image mapping technology, and in particular relates to a three-dimensional cardiac mapping method and system. Background Art

[0003] 3D cardiac mapping is a medical imaging technology that combines multiple 2D medical images, such as ultrasound, CT, or MRI images, into a 3D model to comprehensively evaluate the heart. This technology can provide more detailed and accurate information about the heart's structure and function, helping doctors make more accurate diagnoses and treatment plans. When performing 3D cardiac mapping, doctors use specialized software to process and reconstruct multiple 2D images to generate a 3D model with realistic heart morphology and structure. This 3D model can be rotated, zoomed in, and out, allowing doctors to view various parts of the heart and assess its structure and function.

[0004] Traditional methods for diagnosing heart disease can only provide two-dimensional images or low-resolution three-dimensional images. In addition, existing three-dimensional cardiac mapping methods often simply perform rough mapping of the heart's structural dimensions, resulting in large errors in the results of cardiac mapping. Manual mapping is also highly subjective, making it difficult to accurately analyze and diagnose the heart.

[0005] Summary of the Invention

[0006] The purpose of the embodiments of the present invention is to provide a three-dimensional cardiac mapping method and system, which can solve the technical problems that existing heart disease diagnosis methods can only provide two-dimensional images or low-resolution three-dimensional images, and that the existing three-dimensional cardiac mapping methods in the prior art often only simply perform rough mapping of the structural dimensions of the heart, resulting in large errors in the cardiac mapping results and a high degree of subjectivity in manual mapping.

[0007] In order to solve the above-mentioned technical problems, the present invention is achieved as follows:

[0008] First aspect

[0009] An embodiment of the present invention provides a method for three-dimensional cardiac mapping, the method comprising:

[0010] S101: Acquire cardiac ultrasound image sequences within multiple cardiac cycles, wherein each cardiac ultrasound image sequence includes multiple cardiac ultrasound images;

[0011] S102: performing image enhancement and edge detection processing on the cardiac ultrasound image to obtain an optimized cardiac ultrasound image;

[0012] S103: registering the optimized cardiac ultrasound images based on a correlation matching algorithm to ensure that the optimized cardiac ultrasound images are in the same coordinate system;

[0013] S104: performing a three-dimensional reconstruction operation on each registered optimized cardiac ultrasound image to obtain a three-dimensional cardiac model;

[0014] S105: Introducing the cardiac morphological analysis method to perform layered mapping of the cardiac three-dimensional model;

[0015] S106: Outputting the mapped three-dimensional heart model.

[0016] Second aspect

[0017] An embodiment of the present invention provides a three-dimensional cardiac mapping system, comprising:

[0018] An acquisition module, configured to acquire cardiac ultrasound image sequences within a plurality of cardiac cycles, wherein each cardiac ultrasound image sequence includes a plurality of cardiac ultrasound images;

[0019] a processing module, configured to perform image enhancement and edge detection processing on the cardiac ultrasound image to obtain an optimized cardiac ultrasound image;

[0020] a registration module, configured to register the optimized cardiac ultrasound images based on a correlation matching algorithm to ensure that the optimized cardiac ultrasound images are in the same coordinate system;

[0021] A reconstruction module is used to perform three-dimensional reconstruction on each registered optimized cardiac ultrasound image to obtain a three-dimensional heart model;

[0022] A mapping module is used to introduce a cardiac morphological analysis method to perform layered mapping on a three-dimensional cardiac model;

[0023] The output module is used to output the mapped three-dimensional heart model.

[0024] In this embodiment of the present invention, acquired cardiac ultrasound images undergo image enhancement and edge detection to enhance contrast and improve visual quality. The optimized cardiac ultrasound images are then registered to avoid artifacts during reconstruction of a 3D cardiac model and enhance the clarity of the resulting model. Finally, morphological analysis is used to automatically map the constructed 3D cardiac model in layers, enhancing both detailed and overall structural mapping while minimizing subjective mapping. This provides a more accurate 3D cardiac model for analyzing a patient's condition and improving treatment outcomes for heart disease patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] FIG1 is a schematic flow chart of a three-dimensional cardiac mapping method according to an embodiment of the present invention;

[0026] FIG2 is a schematic structural diagram of a three-dimensional cardiac mapping system provided by an embodiment of the present invention.

[0027] The realization of the objectives, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0029] The following describes in detail a cardiac three-dimensional mapping method and system provided by the embodiment of the present invention through specific embodiments and application scenarios in conjunction with the accompanying drawings.

[0030] Example 1

[0031] 1 , there is shown a schematic flow chart of a three-dimensional cardiac mapping method according to an embodiment of the present invention;

[0032] An embodiment of the present invention provides a three-dimensional cardiac mapping method, the method comprising:

[0033] S101: Acquire cardiac ultrasound image sequences within multiple cardiac cycles, wherein each cardiac ultrasound image sequence includes multiple cardiac ultrasound images.

[0034] It is important to note that the step of acquiring cardiac ultrasound image sequences over multiple cardiac cycles is intended to obtain multiple cardiac ultrasound images for subsequent processing, thereby gaining more information and data, and thus providing a more comprehensive understanding of cardiac structure and function. Acquiring image sequences over multiple cardiac cycles can improve data reliability and accuracy, thereby better assessing cardiac conditions and disease states. Scientific sampling methods can enhance the accuracy of the final constructed three-dimensional cardiac model and avoid large errors caused by insufficient sampling.

[0035] S102: Perform image enhancement and edge detection processing on the cardiac ultrasound image to obtain an optimized cardiac ultrasound image.

[0036] It should be noted that due to various uncertainties during the acquisition process, acquired cardiac ultrasound images contain a certain amount of noise and blur, which can affect subsequent data analysis and processing. Image enhancement can enhance image contrast and clarity, improving image quality. Edge detection can help accurately locate and extract the heart's contours and edge information, providing a foundation for subsequent 3D reconstruction and morphological analysis.

[0037] In a possible implementation, S102 specifically includes:

[0038] S1021: Perform image enhancement on each cardiac ultrasound image through histogram equalization to obtain an enhanced image G(x,y):

[0039] Where G(x,y) represents the pixel value of the enhanced image, H(i,j) represents the pixel value at coordinate (i,j) in the original image, M and N represent the width and height of the image respectively;

[0040] S1022: Perform Sobel operator edge detection on the enhanced image to extract edge information G of the enhanced image x and G y :

[0041] Among them, G x and G y They represent the gradients of the enhanced image in the horizontal and vertical directions after edge detection, respectively. The symbol * represents the convolution operation, and I represents the enhanced image.

[0042] S1023: Calculate the optimized cardiac ultrasound image G based on the horizontal and vertical gradients of the enhanced image after edge detection:

[0043] S1024: Outputting the optimized cardiac ultrasound image G.

[0044] S103: Registering the optimized cardiac ultrasound images based on a correlation matching algorithm to ensure that the optimized cardiac ultrasound images are in the same coordinate system.

[0045] Image registration refers to the process of aligning multiple images of the same scene captured from different perspectives, locations, or times, so that they perfectly overlap within the same coordinate system. This involves transforming two or more images so that they overlap in the same spatial location. By registering optimized cardiac ultrasound images, they can be aligned, enabling more accurate comparison and analysis of medical images and improving the effectiveness of medical image processing.

[0046] It should be noted that during the optimization process of cardiac ultrasound images, factors such as human breathing and heartbeat may cause slight positional and morphological variations between different images. Without proper registration, this can lead to data inaccuracy and inconsistency. Therefore, through registration based on a correlation matching algorithm, multiple images can be aligned in the same coordinate system, ensuring data consistency and accuracy, and providing a better data foundation for subsequent 3D reconstruction and morphological analysis.

[0047] In a possible implementation, S103 specifically includes:

[0048] S1031: Select an optimized cardiac ultrasound image as a reference image and use it as the origin of the coordinate system to establish a plane rectangular coordinate system;

[0049] S1032: Select one of the remaining optimized ultrasound images as the image to be aligned;

[0050] S1033: normalizing the reference image and the image to be aligned to obtain a normalized reference image and a normalized image to be aligned;

[0051] S1034: Calculate the similarity between the normalized reference image and the image to be aligned based on a correlation matching algorithm:

[0052] Among them, I ref (x,y),I mov (x, y) represent the reference image and the image to be aligned respectively, Represent the mean of the normalized reference image and the normalized image to be aligned, (t x ,t y ) represents the relative displacement between the reference image and the image to be aligned;

[0053] S1035: Calculate the target relative displacement when the similarity is maximum;

[0054] S1036: translating the image to be aligned using the target displacement to complete the registration;

[0055] S1037: Repeat S1032-S1036 to register the remaining images to be aligned.

[0056] S104: Performing a three-dimensional reconstruction operation on each registered optimized cardiac ultrasound image to obtain a three-dimensional cardiac model.

[0057] It is important to note that the 3D reconstruction process of the registered cardiac ultrasound images is performed. Through 3D reconstruction, multiple 2D images can be combined into a 3D heart model, providing a more intuitive understanding of cardiac structure and function. 3D reconstruction also provides more geometric and spatial information, facilitating further morphological analysis and quantitative assessment of the patient's heart, thereby enhancing the effectiveness of targeted treatment.

[0058] In a possible implementation, S104 specifically includes:

[0059] S1041: extracting feature points from the registered optimized cardiac ultrasound image, wherein the feature points include ventricular wall feature points and cardiac valve feature points, to obtain a feature point set;

[0060] S1042: Convert the feature point set into a three-dimensional point cloud data set X: X={x1,x2,...,x i}

[0061] Among them, xi is the three-dimensional coordinate of the feature point qi in the feature point set;

[0062] S1043: Use triangulation algorithm to convert 3D point cloud data into triangular mesh data M: M={m1,m2,...,m i} m i =[x k ,x p ,xx] x k ,x p ,x q ∈X

[0063] Among them, each triangle m i Corresponding to three 3D point cloud data x k ,x p ,x q ;

[0064] S1044: Select different materials and textures to optimize and compress the triangular mesh data to obtain a three-dimensional model of the heart.

[0065] S105: Introduce the heart and use the morphological analysis method to perform layered mapping on the three-dimensional heart model.

[0066] Morphological analysis is an image analysis method that can be used to analyze and describe the shape and structure in an image. It extracts morphological information such as contours, corners, holes, and morphological features through a series of morphological operations, such as dilation, erosion, opening, and closing.

[0067] In medical image processing, morphological analysis is often used to analyze and describe the morphological structure of biological tissues, thereby helping doctors diagnose diseases and evaluate treatment effectiveness. In cardiac image processing, morphological analysis can be used to extract cardiac morphological features, such as chamber size, myocardial thickness, and the location and thickness of the ventricular septum. This information can help doctors assess heart health and diagnose heart disease.

[0068] In a possible implementation, S105 specifically includes:

[0069] S1051: Import the 3D heart model and divide it into layers according to its structure: M l =x∈M:α l ≤H(x)<α l+1

[0070] Among them, M represents the three-dimensional model of the heart, M l represents the heart structure of the first layer, H(x) represents the maximum height of the heart three-dimensional model in three-dimensional space, α l and α l+1 They represent the height thresholds of the lth layer and the l+1th layer of the three-dimensional heart model respectively;

[0071] S1052: Preliminary labeling of the structures at each level based on cardiac anatomy knowledge to obtain preliminary labeling results;

[0072] S1053: Based on the preliminary marking, use the morphological analysis algorithm to perform refined marking on each layer to obtain the refined marking result S' l :

[0073] Among them, B represents the preset structural element, that is, a binary matrix, and the symbol Represents the morphological operation operator, S l Indicates the preliminary marking results;

[0074] S1054: The preliminary labeling result and the refined labeling result are combined to obtain the final labeling S of the entire three-dimensional heart model:

[0075] Where n represents the number of divided cardiac structural layers.

[0076] In actual applications, those skilled in the art can set the number of cardiac structural layers based on actual needs. More layers yield more accurate mapping results. However, more layers are not necessarily better during mapping, as more layers increase the mapping time. Choosing the appropriate cardiac structure based on the required mapping accuracy of the 3D cardiac model can improve the efficiency and accuracy of 3D cardiac mapping.

[0077] It should be noted that in layered mapping, the three-dimensional heart model is divided into multiple layers based on the heart's different anatomical structures and functions, and each layer is labeled and annotated. These labels and annotations can be used for more in-depth research and analysis of the heart's structure, function, and related diseases. The high-quality three-dimensional heart model information obtained through the previous steps is not fully utilized. Through layered mapping, we can further explore more information in the model, conduct a more comprehensive and in-depth analysis of the heart's structure, function, and diseases, and carefully divide and label the different structures and parts of the model, which helps to improve the accuracy and credibility of the model.

[0078] S106: Outputting the mapped three-dimensional heart model.

[0079] In this embodiment of the present invention, acquired cardiac ultrasound images undergo image enhancement and edge detection to enhance contrast and improve visual quality. The optimized cardiac ultrasound images are then registered to avoid artifacts during reconstruction of a 3D cardiac model and enhance the clarity of the resulting model. Finally, morphological analysis is used to automatically map the constructed 3D cardiac model in layers, enhancing both detailed and overall structural mapping while minimizing subjective mapping. This provides a more accurate 3D cardiac model for analyzing a patient's condition and improving treatment outcomes for heart disease patients.

[0080] Example 2

[0081] 2 , there is shown a schematic structural diagram of a three-dimensional cardiac mapping system provided by an embodiment of the present invention.

[0082] A three-dimensional cardiac mapping system 20, comprising:

[0083] An acquisition module 201 is configured to acquire cardiac ultrasound image sequences within a plurality of cardiac cycles, wherein each cardiac ultrasound image sequence includes a plurality of cardiac ultrasound images;

[0084] The processing module 202 is configured to perform image enhancement and edge detection on the cardiac ultrasound image to obtain an optimized cardiac ultrasound image;

[0085] a registration module 203 for registering the optimized cardiac ultrasound images based on a correlation matching algorithm to ensure that the optimized cardiac ultrasound images are in the same coordinate system;

[0086] A reconstruction module 204 is configured to perform three-dimensional reconstruction on each registered optimized cardiac ultrasound image to obtain a three-dimensional cardiac model;

[0087] Mapping module 205, used to introduce the heart morphological analysis method to perform layered mapping on the three-dimensional heart model;

[0088] The output module 206 is configured to output the mapped three-dimensional heart model.

[0089] In a possible implementation, the processing module specifically includes:

[0090] The first processing submodule is used to perform image enhancement on each cardiac ultrasound image through histogram equalization to obtain an enhanced image G(x,y):

[0091] Where G(x,y) represents the pixel value of the enhanced image, H(i,j) represents the pixel value at coordinate (i,j) in the original image, M and N represent the width and height of the image respectively;

[0092] The first extraction submodule is used to perform Sobel operator edge detection on the enhanced image and extract the edge information G of the enhanced image. x and G y :

[0093] Among them, G x and G y They represent the gradients of the enhanced image in the horizontal and vertical directions after edge detection, respectively. The symbol * represents the convolution operation, and I represents the enhanced image.

[0094] The first calculation submodule is used to calculate the optimized cardiac ultrasound image G according to the gradients of the enhanced image in the horizontal and vertical directions after edge detection:

[0095] The output submodule is used to output the optimized cardiac ultrasound image G.

[0096] In a possible implementation, the registration module specifically includes:

[0097] The first selection submodule is used to select an optimized cardiac ultrasound image as a reference image and use it as the origin of the coordinate system to establish a plane rectangular coordinate system;

[0098] A second selection submodule is used to select one of the remaining optimized ultrasound images as an image to be aligned;

[0099] The second processing submodule is used to normalize the reference image and the image to be aligned to obtain a normalized reference image and a normalized image to be aligned;

[0100] The second calculation submodule is used to calculate the similarity between the normalized reference image and the image to be aligned based on the correlation matching algorithm:

[0101] Among them, I ref (x,y),I mov (x, y) represent the reference image and the image to be aligned respectively, Represent the mean of the normalized reference image and the normalized image to be aligned, (t x ,t y ) represents the relative displacement between the reference image and the image to be aligned;

[0102] The third calculation submodule is used to calculate the relative displacement of the target when the similarity is maximum;

[0103] The translation submodule is used to translate the image to be aligned using the target displacement to complete the registration;

[0104] The repeating submodule is used to repeat S1032-S1036 to register the remaining images to be aligned.

[0105] In a possible implementation, the reconstruction module specifically includes:

[0106] A second extraction submodule is configured to extract feature points from the registered optimized cardiac ultrasound image, wherein the feature points include ventricular wall feature points and cardiac valve feature points, to obtain a feature point set;

[0107] The first conversion submodule is used to convert the feature point set into a three-dimensional point cloud data set X: X={x1,x2,...,x i}

[0108] Among them, x i is the feature point q in the feature point set i The three-dimensional coordinates of

[0109] The second conversion submodule is used to convert the 3D point cloud data into triangular mesh data M using a triangulation algorithm: M={m1,m2,...,m i} m i =[x k ,x p ,xq ] x k ,x p ,x q ∈X

[0110] Among them, each triangle m i Corresponding to three 3D point cloud data x k ,x p ,x q ;

[0111] The acquisition submodule is used to select different materials and textures to optimize and compress the triangular mesh data to obtain a three-dimensional model of the heart.

[0112] In a possible implementation, the mapping module specifically includes:

[0113] Import submodule, used to import the heart 3D model and divide the heart 3D model into layers according to the structure: M l =x∈M:α l ≤H(x)<α l+1

[0114] Among them, M represents the three-dimensional model of the heart, M l represents the heart structure of the first layer, H(x) represents the maximum height of the heart three-dimensional model in three-dimensional space, α l and α l+1 They represent the height thresholds of the lth layer and the l+1th layer of the three-dimensional heart model respectively;

[0115] The first mapping submodule is used to perform preliminary mapping of the structures at each level based on cardiac anatomical knowledge to obtain preliminary mapping results;

[0116] The second mapping submodule is used to perform a detailed mapping of each layer based on the preliminary marking using a morphological analysis algorithm to obtain a detailed mapping result S' l :

[0117] Among them, B represents the preset structural element, that is, a binary matrix, and the symbol Represents the morphological operation operator, S l Indicates the preliminary marking results;

[0118] The merging submodule is used to merge the preliminary mapping results and the refined mapping results to obtain the final mapping S of the entire heart 3D model:

[0119] Where n represents the number of divided cardiac structural layers.

[0120] The three-dimensional cardiac mapping system 20 provided in the embodiment of the present invention can implement each process implemented in the above method embodiment, and to avoid repetition, they are not described here.

[0121] In this embodiment of the present invention, acquired cardiac ultrasound images undergo image enhancement and edge detection to enhance contrast and improve visual quality. The optimized cardiac ultrasound images are then registered to avoid artifacts during reconstruction of a 3D cardiac model and enhance the clarity of the resulting model. Finally, morphological analysis is used to automatically map the constructed 3D cardiac model in layers, enhancing both detailed and overall structural mapping while minimizing subjective mapping. This provides a more accurate 3D cardiac model for analyzing a patient's condition and improving treatment outcomes for heart disease patients.

[0122] The virtual system in the embodiment of the present invention may be a system, or a component, an integrated circuit, or a chip in a terminal.

[0123] The above are merely embodiments of the present invention and are not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A three-dimensional cardiac mapping method, characterized in that: include: S101: Acquire cardiac ultrasound image sequences within a plurality of cardiac cycles, wherein each of the cardiac ultrasound image sequences includes a plurality of cardiac ultrasound images; S102: performing image enhancement and edge detection processing on the cardiac ultrasound image to obtain an optimized cardiac ultrasound image; S103: registering the optimized cardiac ultrasound images based on a correlation matching algorithm to ensure that the optimized cardiac ultrasound images are in the same coordinate system; S104: performing a three-dimensional reconstruction operation on each registered optimized cardiac ultrasound image to obtain a three-dimensional cardiac model; S105: introducing the heart and performing layered mapping on the three-dimensional heart model using a morphological analysis method; S106: Outputting the mapped three-dimensional heart model.

2. The three-dimensional cardiac mapping method according to claim 1, characterized in that: The S102 specifically includes: S1021: Perform image enhancement on each of the cardiac ultrasound images through histogram equalization processing to obtain an enhanced image G(x, y): Wherein, G(x,y) represents the pixel value of the enhanced image, H(i,j) represents the pixel value at the coordinate (i,j) in the original image, and M and N represent the width and height of the image respectively; S1022: Perform Sobel operator edge detection on the enhanced image to extract edge information G of the enhanced image x and G y : Among them, G x and G y They represent the horizontal and vertical directions of the enhanced image after edge detection. The gradient in the vertical direction, the symbol * represents the convolution operation, and I represents the enhanced image; S1023: Calculate the optimized cardiac ultrasound image G according to the gradients of the enhanced image in the horizontal direction and the vertical direction after edge detection: S1024: Output the optimized cardiac ultrasound image G.

3. The three-dimensional cardiac mapping method according to claim 1, characterized in that: The S103 specifically includes: S1031: Selecting one of the optimized cardiac ultrasound images as a reference image, and using it as the origin of the coordinate system to establish a plane rectangular coordinate system; S1032: Selecting one of the remaining optimized ultrasound images as an image to be aligned; S1033: normalizing the reference image and the image to be aligned to obtain a normalized reference image and a normalized image to be aligned; S1034: Calculate the similarity between the normalized reference image and the image to be aligned based on a correlation matching algorithm: Among them, I ref (x,y),I mov (x, y) represent the reference image and the image to be aligned respectively, Respectively represent the mean of the normalized reference image and the normalized image to be aligned, (t x ,t y ) represents the relative displacement between the reference image and the image to be aligned; S1035: Calculating the target relative displacement when the similarity is maximum; S1036: translating the image to be aligned using the target displacement to complete the registration; S1037: Repeat S1032-S1036 to register the remaining images to be aligned.

4. The three-dimensional cardiac mapping method according to claim 1, characterized in that: The S104 specifically includes: S1041: extracting feature points from the registered optimized cardiac ultrasound image, wherein the feature points include ventricular wall feature points and cardiac valve feature points, to obtain a feature point set; S1042: Convert the feature point set into a three-dimensional point cloud data set X: X={x1,x2,...,x i } Among them, x i is the feature point q in the feature point set i The three-dimensional coordinates of S1043: Using the triangulation algorithm to convert the three-dimensional point cloud data into triangular mesh data M: M={m1,m2,...,m i } m i =[x k ,x p ,x q ] x k ,x p ,x q ∈X Among them, each triangle m i Corresponding to three 3D point cloud data x k ,x p ,x q ; S1044: Select different materials and textures to optimize and compress the triangular mesh data to obtain the three-dimensional model of the heart.

5. The three-dimensional cardiac mapping method according to claim 1, characterized in that: The S105 specifically includes: S1051: Import the three-dimensional heart model, and layer the three-dimensional heart model according to the structure: M l =x∈M:a l ≤H(x)<α l+1 Wherein, M represents the three-dimensional model of the heart, M l represents the lth layer of cardiac structure, H(x) represents the maximum height of the cardiac three-dimensional model in three-dimensional space, α l and α l+1 represent the height thresholds of the lth layer and the l+1th layer of the three-dimensional heart model respectively; S1052: Preliminarily mark the structures at each level based on cardiac anatomical knowledge to obtain preliminary marking results; S1053: Based on the preliminary marking, each level is refined by using the morphological analysis algorithm to obtain the refined marking result S′ l : Among them, B represents the preset structural element, that is, a binary matrix, and the symbol Represents the morphological operation operator, S l Indicates the preliminary marking result; S1054: Merge the preliminary labeling result and the refined labeling result to obtain a final label S of the entire three-dimensional heart model: Wherein, n represents the number of divided cardiac structure layers.

6. A three-dimensional cardiac mapping system, characterized in that: include: An acquisition module, used for acquiring cardiac ultrasound image sequences within a plurality of cardiac cycles, wherein each of the cardiac ultrasound image sequences comprises a plurality of cardiac ultrasound images; A processing module, used for performing image enhancement and edge detection processing on the cardiac ultrasound image to obtain an optimized cardiac ultrasound image; A registration module, used for registering the optimized cardiac ultrasound images based on a correlation matching algorithm to ensure that the optimized cardiac ultrasound images are in the same coordinate system; A reconstruction module, used for performing three-dimensional reconstruction on each registered optimized cardiac ultrasound image to obtain a three-dimensional heart model; A mapping module, used for introducing a heart and performing layered mapping on the three-dimensional heart model by using a morphological analysis method; The output module is used to output the three-dimensional heart model after mapping.

7. The three-dimensional cardiac mapping system according to claim 6, characterized in that: The processing module specifically includes: The first processing submodule is used to perform image enhancement on each of the cardiac ultrasound images through histogram equalization processing to obtain an enhanced image G(x, y): Wherein, G(x,y) represents the pixel value of the enhanced image, H(i,j) represents the pixel value at the coordinate (i,j) in the original image, and M and N represent the width and height of the image respectively; The first extraction submodule is used to perform Sobel operator edge detection on the enhanced image to extract edge information G of the enhanced image. x and G y : Among them, G x and G y Respectively represent the gradients of the enhanced image in the horizontal direction and the vertical direction after edge detection, the symbol * represents the convolution operation, and I represents the enhanced image; The first calculation submodule is used to calculate the optimized cardiac ultrasound image G according to the gradients of the enhanced image in the horizontal direction and the vertical direction after edge detection: The output submodule is used to output the optimized cardiac ultrasound image G.

8. The three-dimensional cardiac mapping system according to claim 6, characterized in that: The registration module specifically includes: A first selection submodule is used to select one of the optimized cardiac ultrasound images as a reference image and use it as the origin of the coordinate system to establish a plane rectangular coordinate system; A second selection submodule is used to select one of the remaining optimized ultrasound images as an image to be aligned; A second processing submodule, used for normalizing the reference image and the image to be aligned to obtain a normalized reference image and a normalized image to be aligned; The second calculation submodule is used to calculate the similarity between the normalized reference image and the image to be aligned based on a correlation matching algorithm: Among them, I ref (x,y),I mov (x, y) represent the reference image and the image to be aligned respectively, Respectively represent the mean of the normalized reference image and the normalized image to be aligned, (t x ,t y ) represents the relative displacement between the reference image and the image to be aligned; A third calculation submodule is used to calculate the target relative displacement when the similarity is maximum; A translation submodule, used to translate the image to be aligned using the target displacement to complete the registration; The repeating submodule is used to repeat S1032-S1036 to register the remaining images to be aligned.

9. The three-dimensional cardiac mapping system according to claim 6, characterized in that: The reconstruction module specifically includes: A second extraction submodule is used to extract feature points in the registered optimized cardiac ultrasound image, wherein the feature points include ventricular wall feature points and cardiac valve feature points, to obtain a feature point set; The first conversion submodule is used to convert the feature point set into a three-dimensional point cloud data set X: X={x1,x2,...,x i } Among them, x i is the feature point q in the feature point set i The three-dimensional coordinates of The second conversion submodule is used to convert the three-dimensional point cloud data into triangular mesh data M using the triangulation algorithm: M={m1,m2,...,m i } m i =[x k ,x p ,x q ] x k ,x p ,x q ∈X Among them, each triangle m i Corresponding to three 3D point cloud data x k ,x p ,x q ; The acquisition submodule is used to select different materials and textures to optimize and compress the triangular mesh data to obtain the three-dimensional model of the heart.

10. The three-dimensional cardiac mapping system according to claim 6, characterized in that: The mapping module specifically includes: The import submodule is used to import the three-dimensional heart model and to layer the three-dimensional heart model according to the structure: M l =x∈M:a l ≤H(x)<α l+1 Wherein, M represents the three-dimensional model of the heart, M l represents the lth layer of cardiac structure, H(x) represents the maximum height of the cardiac three-dimensional model in three-dimensional space, α l and α l+1 represent the height thresholds of the lth layer and the l+1th layer of the three-dimensional heart model respectively; The first mapping submodule is used to perform preliminary mapping of each layer of structure based on cardiac anatomical knowledge to obtain preliminary mapping results; The second mapping submodule is used to perform a detailed mapping of each layer based on the preliminary marking using a morphological analysis algorithm to obtain a detailed mapping result S′ l : Among them, B represents the preset structural element, that is, a binary matrix, and the symbol Represents the morphological operation operator, S l Indicates the preliminary marking result; The merging submodule is used to merge the preliminary mapping result and the refined mapping result to obtain the final mapping S of the entire three-dimensional heart model: Wherein, n represents the number of divided cardiac structure layers.

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