Myocardial injury condition detection system based on image characteristics

Abstract

A myocardial injury condition detection system based on image characteristics. The system comprises an image acquisition module, a feature extraction module, a data processing module and a report generation module, wherein the image acquisition module is used for acquiring high-resolution cardiac image data information; the feature extraction module is used for extracting, from the acquired image data information, feature information related to the cardiac structure and function; the data processing module is used for analyzing the extracted feature information, so as to identify myocardial-injury-related conditions; and the report generation module is used for generating a medical report of the myocardial-injury-related conditions for a doctor. By means of comprehensively analyzing cardiac image data information of a patient, a more comprehensive and accurate assessment of myocardial injury conditions is provided, thereby improving the accuracy and effectiveness of medical diagnosis.

Description

A system for detecting myocardial damage based on imaging characteristics Technical Field

[0001] This invention relates to the field of medical testing system technology, and in particular to a system for detecting myocardial damage based on imaging characteristics. Background Technology

[0002] With the rising incidence of cardiovascular disease, early detection and accurate assessment of myocardial damage have become increasingly important. Traditional myocardial detection methods mainly rely on single imaging techniques or physiological parameters, which are insufficient to comprehensively reflect the overall health of the heart. Especially in clinical diagnosis, accurately assessing the type, severity, and progression of myocardial damage is crucial for developing effective treatment plans. While some medical imaging systems can calculate and display myocardial contractility in real time, these systems often focus on the analysis of single functional parameters, lacking a comprehensive assessment of the structural and functional characteristics of the myocardium. This limitation makes it difficult for physicians to obtain comprehensive and accurate information during diagnosis and treatment, affecting the scientific rigor and effectiveness of clinical decisions.

[0003] A review of publicly available technical solutions reveals that CN115462833A proposes a medical imaging system for real-time calculation and display of myocardial contractility. This system includes an input module, a calculation module, and an output module. The input module collects parameter values ​​of myocardial contractility detected by the medical imaging equipment. The calculation module calculates the myocardial contractility index based on these parameter values. The output module transmits the calculated myocardial contractility index to the medical imaging equipment, where it is displayed on the screen. This invention combines physics and mechanics formulas with cardiac imaging measurements, enabling real-time calculation and display of myocardial contractility on the medical imaging equipment screen. This facilitates in-depth research into the myocardial mechanical properties of the heart using medical imaging equipment, opening up new research areas and expanding the clinical and research applications of medical imaging equipment. However, while this solution can calculate and display myocardial contractility in real time, it is limited to a single mechanical characteristic analysis and lacks a comprehensive assessment of the type and severity of myocardial damage. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of current systems by proposing a myocardial damage detection system based on imaging characteristics.

[0005] The present invention adopts the following technical solution:

[0006] A system for detecting myocardial damage based on image characteristics, the system comprising an image acquisition module, a feature extraction module, a data processing module, and a report generation module;

[0007] The image acquisition module is used to acquire high-resolution cardiac image data; the feature extraction module is used to extract feature information related to cardiac structure and function from the acquired image data; the data processing module is used to analyze and identify the relevant conditions of myocardial damage based on the extracted feature information; and the report generation module is used to generate medical reports on the relevant conditions of myocardial damage for doctors.

[0008] The image acquisition module includes an image acquisition unit and an image preprocessing unit; the image acquisition unit is used to acquire cardiac image data of patients using high-resolution medical imaging equipment, the cardiac image data including MRI image data and Doppler imaging data within multiple cardiac activity cycles; the image preprocessing unit is used to preprocess the acquired image data;

[0009] The feature extraction module includes an anatomical feature extraction unit and a functional feature extraction unit; the anatomical feature extraction unit is used to extract structural features of the patient's heart based on the preprocessed image data; the functional feature extraction unit is used to extract functional features of the patient's heart based on the preprocessed image data.

[0010] Furthermore, the data processing module includes a myocardial injury analysis unit and a deterioration assessment unit; the myocardial injury analysis unit analyzes the type and severity of myocardial injury based on the structural and functional characteristics of the patient's heart; the deterioration assessment unit assesses the deterioration trend of the patient's myocardial injury by combining historical data analysis of myocardial injury.

[0011] Furthermore, the anatomical feature extraction unit extracts structurally relevant features of the patient's heart in the following manner:

[0012] S11: Acquire MRI image data from the preprocessed image data; the MRI image data includes cross-sectional images of the heart at multiple time points within multiple cardiac activity cycles;

[0013] S12: For all cross-sectional image data, obtain the heart edge and internal contour information corresponding to each cross-sectional image through threshold segmentation;

[0014] S13: For images at multiple time points on each slice, fuse them according to the heart edge and internal contour information corresponding to each image to obtain a standard fused image on each slice.

[0015] S14: Input the standard fused images from all cross-sections into a pre-trained CNN feature recognition model to obtain the structural features of the patient's heart;

[0016] Furthermore, in step S13, the standard fused image on each cross-section is obtained in the following manner:

[0017] S131: For each slice, calculate the edge contour similarity between each image in the slice and other images on the slice:

[0018] ;

[0019] in, For an image on this cross section With a certain image Edge contour similarity between them; For an image on this cross section The set of points representing the edge and internal contour of the heart; For an image on this cross section The set of points representing the edge and internal contour of the heart; For set A certain point inside; For set A certain point inside; Representative point With point The Euclidean distance between them; The infimum operator represents finding the minimum value among all possible values. The middle represents all possible Find Minimum value; The supremum operator finds the maximum value among all possible values. The middle represents all possible Find The maximum value;

[0020] S132: Set a similarity threshold Images with edge contour similarity less than a similarity threshold are grouped together, and the edge contour similarity between each image in a group and the remaining images is less than the similarity threshold. ; Assume there is a total One group;

[0021] S133: Merge the individual images in each group:

[0022] ;

[0023] For the first The fused images in each group; For the first The total number of images in each group; For the first The first group Zhang Image;

[0024] S134: Combine and fuse the images from each group to obtain the standard fused image of the cross section:

[0025] ;

[0026] For this cross-section The corresponding standard fused image; The magnification factor is greater than 1; This represents the total number of images on this cross section.

[0027] Furthermore, the functional feature extraction unit combines cardiac MRI image data and Doppler imaging data to perform imaging analysis to obtain the functional-related features of the patient's heart;

[0028] Furthermore, the myocardial injury analysis unit integrates and splices the structural and functional features of the patient's heart into a pre-trained neural network recognition model for myocardial injury, thereby completing a quantitative assessment of the type and severity of myocardial injury.

[0029] The beneficial effects achieved by this invention are:

[0030] This invention utilizes high-resolution cardiac imaging data and Doppler imaging data to accurately extract structural and functional features of the patient's heart. By preprocessing and fusing image data from multiple time points, a standardized fused image is obtained, improving the quality and consistency of the image data and ensuring the accurate generation of structural features. By inputting the extracted structural and functional features into a pre-trained neural network recognition model for myocardial damage, a quantitative assessment of the type and severity of myocardial damage is achieved. The deterioration trend of the patient's myocardial damage is obtained through multiple historical quantitative assessments, providing doctors with a more comprehensive and accurate diagnostic basis. This effectively improves the accuracy of myocardial damage detection, thereby enhancing the scientific nature of medical decisions and treatment outcomes. Attached Figure Description

[0031] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0032] Figure 1 is a schematic diagram of the overall modules of the present invention.

[0033] Figure 2 is a schematic diagram of the workflow of the anatomical feature extraction unit of the present invention.

[0034] Figure 3 is a schematic diagram of the workflow of the deterioration trend assessment unit of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to its embodiments. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention. Other systems, methods, and / or features of this embodiment will become apparent to those skilled in the art after reviewing the following detailed description. It is intended that all such additional systems, methods, features, and advantages are included within this specification, are included within the scope of the present invention, and are protected by the appended claims. Further features of the disclosed embodiments are described in the following detailed description, and these features will be apparent from the following detailed description.

[0036] In the accompanying 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 terms such as "upper," "lower," "left," "right," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present patent. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances. Example

[0037] As shown in Figure 1, this embodiment provides a myocardial damage detection system based on image characteristics. The system includes an image acquisition module, a feature extraction module, a data processing module, and a report generation module.

[0038] The image acquisition module is used to acquire high-resolution cardiac image data; the feature extraction module is used to extract feature information related to cardiac structure and function from the acquired image data; the data processing module is used to analyze and identify the relevant conditions of myocardial damage based on the extracted feature information; and the report generation module is used to generate medical reports on the relevant conditions of myocardial damage for doctors.

[0039] The image acquisition module includes an image acquisition unit and an image preprocessing unit; the image acquisition unit is used to acquire cardiac image data of patients using high-resolution medical imaging equipment, the cardiac image data including MRI image data and Doppler imaging data within multiple cardiac activity cycles; the image preprocessing unit is used to preprocess the acquired image data;

[0040] The feature extraction module includes an anatomical feature extraction unit and a functional feature extraction unit; the anatomical feature extraction unit is used to extract structural features of the patient's heart based on the preprocessed image data; the functional feature extraction unit is used to extract functional features of the patient's heart based on the preprocessed image data.

[0041] The data processing module includes a myocardial injury analysis unit and a deterioration assessment unit. The myocardial injury analysis unit analyzes the type and severity of myocardial injury based on the structural and functional characteristics of the patient's heart. The deterioration assessment unit combines historical data on myocardial injury to assess the deterioration trend of the patient's myocardial injury.

[0042] Furthermore, as shown in Figure 2, the anatomical feature extraction unit extracts structurally relevant features of the patient's heart in the following manner:

[0043] S11: Acquire MRI image data from the preprocessed image data; the MRI image data includes cross-sectional images of the heart at multiple time points within multiple cardiac activity cycles;

[0044] S12: For all cross-sectional image data, obtain the heart edge and internal contour information corresponding to each cross-sectional image through threshold segmentation;

[0045] S13: For images at multiple time points on each slice, fuse them according to the heart edge and internal contour information corresponding to each image to obtain a standard fused image on each slice.

[0046] S14: Input the standard fused images from all cross-sections into a pre-trained CNN feature recognition model to obtain the structural features of the patient's heart;

[0047] Furthermore, in step S13, the standard fused image on each cross-section is obtained in the following manner:

[0048] S131: For each slice, calculate the edge contour similarity between each image in the slice and other images on the slice:

[0049] ;

[0050] in, For an image on this cross section With a certain image Edge contour similarity between them; For an image on this cross section The set of points representing the edge and internal contour of the heart; For an image on this cross section The set of points representing the edge and internal contour of the heart; For set A certain point inside; For set A certain point inside; Representative point With point The Euclidean distance between them; The infimum operator represents finding the minimum value among all possible values. The middle represents all possible Find Minimum value; The supremum operator finds the maximum value among all possible values. The middle represents all possible Find The maximum value;

[0051] S132: Set a similarity threshold Images with edge contour similarity less than a similarity threshold are grouped together, and the edge contour similarity between each image in a group and the remaining images is less than the similarity threshold. ; Assume there is a total One group;

[0052] S133: Merge the individual images in each group:

[0053] ;

[0054] For the first The fused images in each group; For the first The total number of images in each group; For the first The first group Zhang Image;

[0055] S134: Combine and fuse the images from each group to obtain the standard fused image of the cross section:

[0056] ;

[0057] For this cross-section The corresponding standard fused image; The magnification factor is greater than 1; This represents the total number of images on this cross section.

[0058] Specifically, the structural features of the patient's heart include, but are not limited to, myocardial thickness, heart chamber size, and heart morphology;

[0059] Comprehensive information is obtained from multi-time-point cardiac cross-sectional images, thereby improving the integrity and reliability of image data. Threshold segmentation is used to extract the cardiac edges and internal contours, laying the foundation for subsequent image similarity calculation and fusion. Image grouping is achieved by calculating edge contour similarity, ensuring high similarity among images within the same group and enhancing the quality of the fused image. High-quality standard fused images are obtained by weighted fusion based on the number of images in each group, ensuring higher accuracy and clarity of the images input to the CNN feature recognition model, thus providing a reliable basis for subsequent diagnosis and treatment.

[0060] Furthermore, the functional feature extraction unit combines cardiac MRI image data and Doppler imaging data to perform imaging analysis to obtain the functional-related features of the patient's heart; the functional-related features of the patient's heart include, but are not limited to, cardiac systolic and diastolic characteristics, myocardial motion velocity, and blood flow velocity. Example

[0061] This embodiment should be understood to include at least all the features of any of the foregoing embodiments, and to further improve upon them;

[0062] This embodiment provides a myocardial damage detection system based on image characteristics. The system includes an image acquisition module, a feature extraction module, a data processing module, and a report generation module.

[0063] The image acquisition module is used to acquire high-resolution cardiac image data; the feature extraction module is used to extract feature information related to cardiac structure and function from the acquired image data; the data processing module is used to analyze and identify the relevant conditions of myocardial damage based on the extracted feature information; and the report generation module is used to generate medical reports on the relevant conditions of myocardial damage for doctors.

[0064] The data processing module includes a myocardial injury analysis unit and a deterioration assessment unit. The myocardial injury analysis unit analyzes the type and severity of myocardial injury based on the structural and functional characteristics of the patient's heart. The deterioration assessment unit combines historical data on myocardial injury to assess the deterioration trend of the patient's myocardial injury.

[0065] Furthermore, the myocardial injury analysis unit integrates and splices the structural and functional features of the patient's heart into a pre-trained myocardial injury neural network recognition model to complete a quantitative assessment of the type and severity of myocardial injury.

[0066] Furthermore, as shown in Figure 3, the deterioration assessment unit assesses the worsening trend of myocardial damage in the patient in the following manner:

[0067] S21: Retrieve continuous data from historical data with the current time as the cutoff time. Data on the severity of myocardial damage in these patients, and the patients' condition in this context. Information on daily habits during the second data collection period;

[0068] S22: Will continue Among the historical patient data on the severity of myocardial damage, the most recent one is marked as the [number]. The one furthest from the current time is marked as the 1st time;

[0069] S23: Calculate from the 2nd to the 1st The coefficient of change of myocardial damage in each instance:

[0070] ;

[0071] in, For the first The coefficient of change in myocardial damage over time For the first Quantitative assessment values ​​of the severity of myocardial damage in patients from previous historical data; meeting the requirements ;

[0072] S24: Calculate the patient's deterioration trend parameters:

[0073]

[0074] in, This is a parameter representing the patient's deterioration trend; a higher parameter indicates a more pronounced trend of worsening myocardial damage. In the first To the The parameters for assessing patients' unhealthy habits during the data collection period can be set by doctors based on their extensive clinical experience and patient lifestyle information. The range of values ​​is as follows: ; The preset magnification factor has a range of values. ;

[0075] By comprehensively considering multiple historical data and patients' lifestyle information, the deterioration trend assessment unit can gain a more comprehensive understanding of the changes in myocardial damage in patients, thereby providing more accurate assessment results. By acquiring the multiple myocardial damage change coefficients in historical data, it can promptly identify the deterioration trend of myocardial damage in patients at different stages. By combining timelines and patients' lifestyle information to provide assessment weights for deterioration trend parameters, and analyzing the deterioration trend of myocardial damage based on deterioration trend parameters, it can remind doctors to intervene in potential risks and thus improve the overall health status of patients.

[0076] Furthermore, the medical report information generated by the report generation module includes the analysis results of the myocardial injury analysis unit and the assessment results of the deterioration assessment unit.

[0077] The content disclosed above is only a preferred and feasible embodiment of the present invention, and is not intended to limit the scope of protection of the present invention. Therefore, all equivalent technical changes made based on the content of the present invention specification and drawings are included within the scope of protection of the present invention. Furthermore, the elements therein can be updated as technology develops.

Claims

1. A system for detecting myocardial damage based on imaging characteristics, characterized in that, The system includes an image acquisition module, a feature extraction module, a data processing module, and a report generation module; The image acquisition module is used to acquire high-resolution cardiac image data; the feature extraction module is used to extract feature information related to cardiac structure and function from the acquired image data; the data processing module is used to analyze and identify the relevant conditions of myocardial damage based on the extracted feature information; and the report generation module is used to generate medical reports on the relevant conditions of myocardial damage for doctors. The image acquisition module includes an image acquisition unit and an image preprocessing unit; the image acquisition unit is used to acquire cardiac image data of patients using high-resolution medical imaging equipment, the cardiac image data including MRI image data and Doppler imaging data within multiple cardiac activity cycles; the image preprocessing unit is used to preprocess the acquired image data; The feature extraction module includes an anatomical feature extraction unit and a functional feature extraction unit; the anatomical feature extraction unit is used to extract structural features of the patient's heart based on the preprocessed image data; the functional feature extraction unit is used to extract functional features of the patient's heart based on the preprocessed image data. The data processing module includes a myocardial injury analysis unit and a deterioration assessment unit. The myocardial injury analysis unit analyzes the type and severity of myocardial injury based on the structural and functional characteristics of the patient's heart. The deterioration assessment unit combines historical data on myocardial injury to assess the deterioration trend of the patient's myocardial injury. The anatomical feature extraction unit extracts structurally relevant features of the patient's heart in the following manner: S11: Acquire MRI image data from the preprocessed image data; the MRI image data includes cross-sectional images of the heart at multiple time points within multiple cardiac activity cycles; S12: For all cross-sectional image data, obtain the heart edge and internal contour information corresponding to each cross-sectional image through threshold segmentation; S13: For images at multiple time points on each slice, fuse them according to the heart edge and internal contour information corresponding to each image to obtain a standard fused image on each slice. S14: Input the standard fused images from all cross-sections into a pre-trained CNN feature recognition model to obtain the structural features of the patient's heart; In step S13, the standard fused image on each cross-section is obtained in the following way: S131: For each slice, calculate the edge contour similarity between each image in the slice and other images on the slice: ； in, For an image on this cross section With a certain image Edge contour similarity between them; For an image on this cross section The set of points representing the edge and internal contour of the heart; For an image on this cross section The set of points representing the edge and internal contour of the heart; For set A certain point inside; For set A certain point inside; Representative point With point The Euclidean distance between them; This is the infimum operator, which finds the minimum value among all possible values. Indicates all possible Find Minimum value; The supremum operator finds the largest value among all possible values. Indicates all possible Find The maximum value; S132: Set a similarity threshold Images with edge contour similarity less than a similarity threshold are grouped together, and the edge contour similarity between each image in a group and the remaining images is less than the similarity threshold. ; Assume there is a total One group; S133: Merge the individual images in each group: ； For the first The fused images in each group; For the first The total number of images in each group; For the first The first group Zhang Image; S134: Combine and fuse the images from each group to obtain the standard fused image of the cross section: ； For this cross-section The corresponding standard fused image; The magnification factor is greater than 1; This represents the total number of images on this cross section. The myocardial injury analysis unit integrates and splices the structural and functional features of the patient's heart into a pre-trained neural network recognition model for myocardial injury, thereby completing a quantitative assessment of the type and severity of myocardial injury.

2. The myocardial damage detection system based on imaging characteristics according to claim 1, characterized in that, The functional feature extraction unit combines cardiac MRI image data and Doppler imaging data to perform imaging analysis, thereby obtaining the functional-related features of the patient's heart.

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