Method and system for 3D cardiac mapping

Abstract

The objective is to provide a method and system for 3D cardiac mapping. [Solution] The present invention discloses a method and system for 3D cardiac mapping, and belongs to the technical field of image mapping. The 3D cardiac mapping method includes: collecting a sequence of cardiac ultrasound images including multiple cardiac ultrasound images within multiple heart cycles; applying image enhancement processing and edge detection processing to the cardiac ultrasound images to obtain optimized cardiac ultrasound images; registering the optimized cardiac ultrasound images based on a correlation matching algorithm to ensure that each optimized cardiac ultrasound image is in the same coordinate system; performing 3D reconstruction on each registered optimized cardiac ultrasound image to obtain a cardiac 3D model; importing the cardiac 3D model and performing hierarchical mapping of the cardiac 3D model using a morphological analysis method; and outputting the mapped cardiac 3D model. By applying image enhancement processing and edge detection processing to the collected cardiac ultrasound images, registering them, and then constructing a cardiac 3D model and performing hierarchical mapping, the clarity of the display of the cardiac 3D model and the accuracy and reliability of the cardiac 3D structure mapping results are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image mapping, and in particular, to a method and system for 3D heart mapping.

Background Art

[0002] 3D heart mapping is a medical imaging technology that integrates multiple 2D medical images, such as ultrasound, CT, or MRI images, into a 3D model to comprehensively evaluate the heart. This technology provides more detailed and accurate information about the structure and function of the heart, helping physicians make more accurate diagnoses and treatment plans. When performing 3D heart mapping, physicians use specialized software to process and reconstruct multiple 2D images to generate a 3D model that reflects the true shape and structure of the heart. This 3D model can be rotated, enlarged, and reduced, allowing physicians to observe each part of the heart and evaluate its structure and function.

[0003] Conventional methods for diagnosing heart diseases can only provide 2D images or low-resolution 3D images. Many existing 3D heart mapping methods in the prior art simply roughly map the structural dimensions of the heart, resulting in large errors in the heart mapping results. Moreover, manual mapping is highly subjective, making it difficult to accurately analyze and diagnose the heart.

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of an embodiment of the present invention is to provide a method and system for 3D heart mapping that can solve the technical problems that conventional methods for diagnosing heart diseases can only provide 2D images or low-resolution 3D images, many existing 3D heart mapping methods in the prior art simply roughly map the structural dimensions of the heart, resulting in large errors in the heart mapping results, and manual mapping is highly subjective.

Means for Solving the Problems

[0005] To solve the above technical problems, the present invention is achieved as follows.

[0006] First aspect Embodiments of the present invention provide a 3D heart mapping method: Step S101 involves collecting a cardiac ultrasound image sequence containing multiple cardiac ultrasound images within multiple heart cycles, Step S102 involves applying image enhancement processing and edge detection processing to the aforementioned cardiac ultrasound image to obtain an optimized cardiac ultrasound image. Step S103 involves registering the optimized cardiac ultrasound images based on a correlation matching algorithm in order to ensure that each of the optimized cardiac ultrasound images is in the same coordinate system. Step S104 involves performing 3D reconstruction on each of the registered optimized cardiac ultrasound images to obtain a cardiac 3D model, Step S105 involves importing the aforementioned 3D cardiac model and performing a hierarchical mapping of the 3D cardiac model using a morphological analysis method. Step S106 outputs a mapped 3D model of the heart.

[0007] Second aspect Embodiments of the present invention provide a 3D cardiac mapping system: It is a 3D cardiac mapping system, An acquisition module for acquiring a cardiac ultrasound image sequence containing multiple cardiac ultrasound images within multiple heart cycles, A processing module for obtaining an optimized cardiac ultrasound image by applying image enhancement processing and edge detection processing to the aforementioned cardiac ultrasound image, To ensure that each of the optimized cardiac ultrasound images is in the same coordinate system, a registration module for registering the optimized cardiac ultrasound images based on a correlation matching algorithm is provided. A reconstruction module for obtaining a 3D model of the heart by performing 3D reconstruction on each of the registered optimized cardiac ultrasound images, A mapping module for importing a 3D cardiac model and performing hierarchical mapping of the 3D cardiac model using a morphological analysis method, An output module for outputting a mapped 3D model of the heart. [Effects of the Invention]

[0008] In embodiments of the present invention, the contrast of the cardiac ultrasound images is enhanced and the display effect is improved by applying image enhancement and edge detection processing to the collected cardiac ultrasound images. Subsequently, the optimized cardiac ultrasound images are registered to prevent artifacts from occurring during the reconstruction process of the cardiac 3D model, thereby improving the clarity of the display of the constructed cardiac 3D model. Finally, by automatically performing layered mapping of the cardiac 3D model constructed by morphological analysis, the mapping effect of the details and overall structure of the cardiac 3D structure is improved, subjective influences of mapping are avoided, a more accurate cardiac 3D model is provided for analyzing the patient's condition, and better therapeutic effects are provided to cardiac disease patients. [Brief explanation of the drawing]

[0009] [Figure 1] This is a flowchart of a 3D heart mapping method provided in an embodiment of the present invention. [Figure 2] This is a schematic diagram of a 3D cardiac mapping system provided in an embodiment of the present invention. [Modes for carrying out the invention]

[0010] The achievement of the objectives, functions, features, and advantages of the present invention will be described in detail with reference to the accompanying drawings in conjunction with the embodiments.

[0011] To make the objectives, technical means, and advantages of the present invention clearer, the technical means in the embodiments of the present invention will be described in detail below with reference to the drawings in the embodiments of the present invention. However, it should be noted that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those skilled in the art without creative activities belong to the protection scope of the present invention.

[0012] Hereinafter, with reference to the accompanying drawings, the 3D cardiac mapping method and system provided in the embodiments of the present invention will be described in detail through specific embodiments and application scenarios. <​​​​​​​​​​​​​​​​​​​​​​​It should be noted that due to the influence of various uncertain factors in the collection process, the collected cardiac ultrasound images are subject to certain noise and blurring, which will affect subsequent data analysis and processing. Image enhancement processing can increase the contrast and sharpness of the images, thereby improving the image quality. Edge detection processing helps to identify and extract the contour and edge information of the heart, providing a basis for subsequent 3D reconstruction and morphological analysis.

[0019] In a possible implementation form, S102 specifically includes the following steps.

[0020] S1021: A step of performing image enhancement processing on each cardiac ultrasound image through histogram equalization processing to obtain an enhanced image G(x, y).

[0021]

Number

[0022] S1022: Execute edge detection on the enhanced image using the Sobel operator, and extract the G of the edge information of the enhanced image ,

[0027] , , and G y of.

[0023]

Number

[0024] S1023: Based on the horizontal and vertical gradients of the enhanced image after edge detection, calculate the optimized cardiac ultrasound image G.

[0025]

Number

[0026] S1024: Output the optimized cardiac ultrasound image G.

[0027] S103: A step of registering optimized cardiac ultrasound images based on a correlation matching algorithm in order to ensure that each optimized cardiac ultrasound image is in the same coordinate system.

[0028] Image registration is the process of aligning multiple images of the same scene, taken from different viewpoints, locations, or times, so that they perfectly overlap within the same coordinate system. In other words, by making two or more images overlap at the same spatial location using a specific transformation method, and registering the optimized cardiac ultrasound image, image alignment becomes possible, allowing for more accurate comparison and analysis of medical images and improving the effectiveness of medical image processing.

[0029] During the optimization process of cardiac ultrasound images, slight variations in position and morphology may occur between different images due to factors such as respiration and heart rate. It should be noted that without registration, data inaccuracies and inconsistencies may occur. Therefore, registration processing based on a correlation matching algorithm ensures data consistency and accuracy by aligning multiple images within the same coordinate system, providing a better data foundation for subsequent 3D reconstruction and morphological analysis.

[0030] In possible embodiments, S103 specifically includes the following steps.

[0031] S1031: A step in which an optimized cardiac ultrasound image is selected as a reference image, and a plane rectangular coordinate system is established with it as the origin of the coordinate system.

[0032] S1032: A step in which one image is selected from the remaining optimized ultrasound images to be used as the alignment target image.

[0033] S1033: A step of applying normalization processing to the reference image and the alignment target image to obtain the normalized reference image and the normalized alignment target image.

[0034] S1034: A step to calculate the similarity between the normalized reference image and the alignment target image based on the correlation matching algorithm.

[0035]

number

[0036] S1035: A step to calculate the target relative shift amount when the similarity peaks.

[0037] S1036: A step to complete registration by translating the target image for alignment using the target relative movement amount.

[0038] Steps S1037: Repeat steps S1032 to S1036 to register the remaining alignment target images.

[0039] S104: A step to obtain a 3D cardiac model by performing 3D reconstruction on each registered optimized cardiac ultrasound image.

[0040] The step of performing 3D reconstruction on registered cardiac ultrasound images should be noted because 3D reconstruction allows for the integration of multiple 2D images into a single 3D cardiac model, leading to a more intuitive understanding of the structure and function of the heart. Furthermore, 3D reconstruction provides more geometric morphological and spatial positional information, which is useful for further morphological analysis and quantitative evaluation of the patient's heart, thereby improving the effectiveness of targeted therapy.

[0041] In a possible embodiment, S104 specifically includes the following steps.

[0042] S1041: A step of extracting feature points, including ventricular wall feature points and cardiac valve feature points, from registered and optimized cardiac ultrasound images to obtain a set of feature points.

[0043] S1042: Step to convert the feature point set into a 3D point cloud dataset X.

[0044]

number

[0045] S1043: A step to convert 3D point cloud data into triangular mesh data M using a triangulation algorithm.

[0046]

number

[0047] S1044: Steps to select different materials and textures, optimize and compress triangular mesh data, and obtain a heart 3D model.

[0048] S105: Steps to import a 3D cardiac model and perform hierarchical mapping of the 3D cardiac model using morphological analysis.

[0049] Morphological analysis is an image analysis technique used to analyze and describe the shape and structure within an image. Through a series of morphological processes such as expansion, erosion, opening, and closing operations, morphological information from an image, such as contours, corners, holes, and morphological features, can be extracted.

[0050] In medical image processing, morphological analysis is frequently used to analyze and describe the morphological structure of biological tissues, helping physicians diagnose diseases and evaluate treatment effectiveness. In cardiac image processing, morphological analysis can extract morphological features of the heart, such as the size of the cardiac chambers, the thickness of the myocardium, and the position and thickness of the interventricular septum. This information helps physicians assess the health of the heart and diagnose cardiac diseases.

[0051] In possible embodiments, S105 specifically includes the following steps.

[0052] S1051: Step to import a 3D heart model and layer it according to its structure.

[0053]

number

[0054] S1052: A step to obtain preliminary mapping results by performing preliminary mapping of the structure at each level based on knowledge of cardiac anatomy.

[0055] JPEG2026527661000009.jpg12170

[0056]

number

[0057] S1054: A step to merge preliminary mapping results and subdivided mapping results to obtain the final mapping S of the entire 3D model of the heart.

[0058]

number

[0059] In practical applications, those skilled in the art can set the number of layers in the cardiac structure according to their specific needs. While a greater number of layers generally leads to more accurate mapping results, a higher number of layers is not always preferable during the mapping process. This is because a greater number of layers increases the mapping time. By selecting an appropriate number of layers for the cardiac structure according to the required mapping accuracy of the 3D cardiac model, the efficiency and accuracy of 3D cardiac mapping can be improved.

[0060] In hierarchical mapping, it should be noted that the 3D cardiac model is divided into multiple different layers based on the different anatomical structures and functions of the heart, and each layer is labeled and annotated. These labels and annotations can be used to study and analyze the structure, function, and related diseases of the heart in more depth. The information from the high-quality cardiac 3D model obtained in the previous steps has not yet been fully utilized, but through hierarchical mapping, more information within the model can be delved into further, enabling a more comprehensive and detailed analysis of the structure, function, and diseases of the heart. By finely dividing and labeling the different structures and parts of the model, it contributes to improving the accuracy and reliability of the model.

[0061] S106: Step to output a mapped 3D model of the heart.

[0062] In embodiments of the present invention, the contrast of the cardiac ultrasound images is enhanced and the display effect is improved by applying image enhancement and edge detection processing to the collected cardiac ultrasound images. Subsequently, the optimized cardiac ultrasound images are registered to prevent artifacts from occurring during the reconstruction process of the cardiac 3D model, thereby improving the clarity of the display of the constructed cardiac 3D model. Finally, by automatically performing layered mapping of the cardiac 3D model constructed by morphological analysis, the mapping effect of the details and overall structure of the cardiac 3D structure is improved, subjective influences of mapping are avoided, a more accurate cardiac 3D model is provided for analyzing the patient's condition, and better therapeutic effects are provided to cardiac disease patients.

[0063] (Second embodiment) Referring to Figure 2, this is a schematic diagram of the 3D cardiac mapping system provided in an embodiment of the present invention.

[0064] 3D cardiac mapping system 20, A collection module 201 for collecting cardiac ultrasound image sequences containing multiple cardiac ultrasound images within multiple heart cycles, A processing module 202 for obtaining optimized cardiac ultrasound images by applying image enhancement processing and edge detection processing to cardiac ultrasound images, To ensure that each optimized cardiac ultrasound image is in the same coordinate system, a registration module 203 is provided for registering optimized cardiac ultrasound images based on a correlation matching algorithm, A reconstruction module 204 performs 3D reconstruction on each registered optimized cardiac ultrasound image to obtain a cardiac 3D model, A mapping module 205 for importing a 3D cardiac model and performing hierarchical mapping of the 3D cardiac model using morphological analysis methods, Output module 206 for outputting a mapped 3D model of the heart and The above system, comprising the above-mentioned features.

[0065] In a possible embodiment, the processing module specifically comprises a first processing submodule, a first extraction submodule, a first calculation submodule, and an output submodule. The first processing submodule is used to apply image enhancement processing to each cardiac ultrasound image by histogram homogenization processing to obtain an enhanced image G(x, y).

[0066]

number

[0067] The first extraction submodule performs edge detection on the enhanced image using the Sobel operator, and G of the edge information of the enhanced image. x and G y It is used to extract [something].

[0068]

number

[0069] The first computation submodule is used to calculate the optimized cardiac ultrasound image G based on the horizontal and vertical gradients of the enhanced image after edge detection.

[0070]

number

[0071] The output submodule is used to output optimized cardiac ultrasound images G.

[0072] In a possible embodiment, the registration module specifically comprises a first selection submodule, a second selection submodule, a second processing submodule, a second calculation submodule, a third calculation submodule, a translation submodule, and a repeating submodule. The first selection submodule is used to select an optimized cardiac ultrasound image as a reference image and establish a plane rectangular coordinate system with it as the coordinate system origin. The second selection submodule is used to select one image from the remaining optimized ultrasound images as the alignment target image. The second processing submodule is used to perform normalization processing on the reference image and the alignment target image in order to obtain the normalized reference image and the normalized alignment target image. The second computation submodule is used to calculate the similarity between the normalized reference image and the image to be aligned, based on a correlation matching algorithm.

[0073]

number

[0074] The third calculation submodule is used to calculate the target relative shift when the similarity peaks. The translation submodule is used to translate the target image using the target relative movement amount and complete the registration. The repeating submodule is used to register the remaining alignment target images by repeating steps S1032 to S1036.

[0075] In a possible embodiment, the reconstruction module specifically comprises a second extraction submodule, a first transformation submodule, a second transformation submodule, and an acquisition submodule. The second extraction submodule is used to extract feature points, including ventricular wall feature points and cardiac valve feature points, from registered and optimized cardiac ultrasound images to obtain a feature point set. The first transformation submodule is used to transform the feature point set into a 3D point cloud dataset X.

[0076]

number

[0077] The second transformation submodule is used to convert 3D point cloud data into triangular mesh data M using a triangulation algorithm.

[0078]

number

[0079] The acquisition submodule is used to select different materials and textures to optimize and compress triangular mesh data and obtain a 3D model of the heart.

[0080] In a possible embodiment, the mapping module specifically comprises an import submodule, a first mapping submodule, a second mapping submodule, and a merge submodule. The import submodule is used to import 3D models of the heart and to hierarchize them according to their structure.

[0081]

number

[0082] JPEG2026527661000019.jpg26170

[0083]

number

[0084] The merge submodule is used to merge preliminary mapping results and subdivided mapping results to obtain the final mapping S of the entire 3D model of the heart.

[0085]

number

[0086] The 3D cardiac mapping system 20 provided in the embodiments of the present invention can perform each of the processes realized in the above method embodiments, and a detailed explanation is omitted here to avoid duplication.

[0087] In embodiments of the present invention, the contrast of the cardiac ultrasound images is enhanced and the display effect is improved by applying image enhancement and edge detection processing to the collected cardiac ultrasound images. Subsequently, the optimized cardiac ultrasound images are registered to prevent artifacts from occurring during the reconstruction process of the cardiac 3D model, thereby improving the clarity of the display of the constructed cardiac 3D model. Finally, by automatically performing layered mapping of the cardiac 3D model constructed by morphological analysis, the mapping effect of the details and overall structure of the cardiac 3D structure is improved, subjective influences of mapping are avoided, a more accurate cardiac 3D model is provided for analyzing the patient's condition, and better therapeutic effects are provided to cardiac disease patients.

[0088] The virtual system in the embodiments of the present invention may be a system, and may be a component in a terminal, an integrated circuit, a chip, etc.

[0089] The above are merely embodiments of the present invention and are not intended to limit the invention. To those skilled in the art, the present invention is subject to various modifications and variations. Any modifications, substitutions with equivalents, improvements, etc., made within the spirit and principles of the present invention should be included in the claims of the present invention.

Claims

1. A 3D cardiac mapping method, Step S101 involves collecting a cardiac ultrasound image sequence containing multiple cardiac ultrasound images within multiple heart cycles, Step S102 involves applying image enhancement processing and edge detection processing to the aforementioned cardiac ultrasound image to obtain an optimized cardiac ultrasound image. In order to ensure that each of the optimized cardiac ultrasound images is in the same coordinate system, step S103 is to register the optimized cardiac ultrasound images based on a correlation matching algorithm, Step S104 involves performing 3D reconstruction on each of the registered optimized cardiac ultrasound images to obtain a cardiac 3D model, Step S105 involves importing the aforementioned 3D cardiac model and performing a hierarchical mapping of the 3D cardiac model using a morphological analysis method. Step S106 outputs a mapped 3D model of the heart. A 3D cardiac mapping method characterized by including the following:

2. The above S102 specifically refers to the next step, S1021: A step of applying image enhancement processing to each cardiac ultrasound image by histogram homogenization processing to obtain an enhanced image G(x, y), [Math 1] S1022: Edge detection is performed on the enhanced image using the Sobel operator, and the edge information of the enhanced image is G ｘ and G ｙ Steps to extract [Math 2] S1023: A step of calculating the optimized cardiac ultrasound image G based on the horizontal and vertical gradients of the enhanced image after edge detection. [Math 3] S1024: Step of outputting the optimized cardiac ultrasound image G, A 3D cardiac mapping method according to claim 1, characterized by including the following:

3. The above S103 specifically refers to the next step, S1031: A step of selecting the optimized cardiac ultrasound image as a reference image and establishing a plane rectangular coordinate system with it as the origin of the coordinate system. S1032: A step of selecting one image from the remaining optimized cardiac ultrasound images as the alignment target image. S1033: A step of applying normalization processing to the reference image and the alignment target image to obtain a normalized reference image and a normalized alignment target image. S1034: A step of calculating the similarity between the normalized reference image and the alignment target image based on the correlation matching algorithm. [Math 4] S1035: A step of calculating the target relative displacement when the similarity reaches its peak. S1036: A step of completing registration by translating the alignment target image using the target relative movement amount, S1037: The step of repeating S1032 to S1036 and registering the remaining alignment target images, A 3D cardiac mapping method according to claim 1, characterized by including the following:

4. The above S104 specifically refers to the next step, S1041: A step of extracting feature points, including ventricular wall feature points and cardiac valve feature points, from a registered and optimized cardiac ultrasound image to obtain a set of feature points. S1042: Step of converting the feature point set into a 3D point cloud dataset X, [Math 5] S1043: A step of converting the 3D point cloud data into triangulation mesh data M using a triangulation algorithm. [Math 6] S1044: A step of selecting different materials and textures to optimize and compress the triangular mesh data and obtain the heart 3D model. A 3D cardiac mapping method according to claim 1, characterized by including the following:

5. The above S105 specifically refers to the next step, S1051: A step of importing the 3D model of the heart and hierarchizing it according to its structure. [Number 7] [Number 8] S1054: A step of merging the preliminary mapping result and the subdivided mapping result to obtain the final mapping S of the entire 3D model of the heart. [Number 9] A 3D cardiac mapping method according to claim 1, characterized by including the following:

6. It is a 3D cardiac mapping system, An acquisition module for acquiring a cardiac ultrasound image sequence containing multiple cardiac ultrasound images within multiple heart cycles, A processing module for obtaining an optimized cardiac ultrasound image by applying image enhancement processing and edge detection processing to the aforementioned cardiac ultrasound image, To ensure that each of the optimized cardiac ultrasound images is in the same coordinate system, a registration module for registering the optimized cardiac ultrasound images based on a correlation matching algorithm is provided. A reconstruction module for obtaining a 3D model of the heart by performing 3D reconstruction on each of the registered optimized cardiac ultrasound images, A mapping module for importing a 3D cardiac model and performing hierarchical mapping of the 3D cardiac model using a morphological analysis method, Output module for outputting a mapped 3D model of the heart A 3D cardiac mapping system characterized by comprising the following features.

7. The processing module specifically comprises a first processing submodule, a first extraction submodule, a first calculation submodule, and an output submodule. The first processing submodule is used to apply image enhancement processing to each cardiac ultrasound image by histogram homogenization processing to obtain an enhanced image G(x, y), [Number 10] The first extraction submodule performs edge detection on the enhanced image using the Sobel operator, and extracts the G edge information of the enhanced image. ｘ and G ｙ Used to extract, [Math 11] The first calculation submodule is used to calculate the optimized cardiac ultrasound image G based on the horizontal and vertical gradients of the enhanced image after edge detection. [Math 12] The output submodule is used to output an optimized cardiac ultrasound image G. The 3D cardiac mapping system according to claim 6, characterized in that...

8. The registration module specifically comprises a first selection submodule, a second selection submodule, a second processing submodule, a second calculation submodule, a third calculation submodule, a translation submodule, and a repeating submodule. The first selection submodule is used to select the optimized cardiac ultrasound image as a reference image and to establish a plane rectangular coordinate system with it as the origin of the coordinate system. The second selection submodule is used to select one of the remaining optimized ultrasound images as the alignment target image. The second processing submodule is used to perform normalization processing on the reference image and the alignment target image in order to obtain a normalized reference image and a normalized alignment target image. The second calculation submodule is used to calculate the similarity between the normalized reference image and the alignment target image based on a correlation matching algorithm. [Number 13] The third calculation submodule is used to calculate the target relative shift amount when the similarity peaks. The translation submodule is used to translate the alignment target image using the target relative movement amount and to complete the registration. The repeating submodule is used to register the remaining alignment target image by repeating steps S1032 to S1036. The 3D cardiac mapping system according to claim 6, characterized in that...

9. The reconstruction module specifically comprises a second extraction submodule, a first transformation submodule, a second transformation submodule, and an acquisition submodule. The second extraction submodule is used to extract feature points, including ventricular wall feature points and cardiac valve feature points, from registered optimized cardiac ultrasound images to obtain a set of feature points. The first transformation submodule is used to transform the feature point set into a three-dimensional point cloud dataset X. [Number 14] The second conversion submodule is used to convert 3D point cloud data into triangular mesh data M using a triangulation algorithm. [Number 15] The acquisition submodule is used to select different materials and textures to optimize and compress the triangular mesh data and obtain the heart 3D model. The 3D cardiac mapping system according to claim 6, characterized in that...

10. The mapping module specifically comprises an import submodule, a first mapping submodule, a second mapping submodule, and a merge submodule. The aforementioned import submodule is used to import the 3D model of the heart and to hierarchize it according to its structure. [Number 16] [Number 17] The merge submodule is used to merge the preliminary mapping result and the subdivided mapping result to obtain the final mapping S of the entire 3D model of the heart. [Number 18] The 3D cardiac mapping system according to claim 6, characterized in that...

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