Method and System for Optimizing the Quality of Coronary CT Angiography

Abstract

The present invention discloses a method and system for optimizing the quality of coronary CT angiography, belonging to the technical field of coronary CT, and comprising the following steps: S1: Collecting coronary CT projection data of a patient; S2: Performing threshold division on the coronary CT projection data of the patient to determine a coronary CT divided image of the patient; S3: Filtering, contrast enhancement and feature extraction are performed on the coronary CT divided image of the patient to determine a coronary CT feature image of the patient; S4: Generating a coronary CT angiogram of the patient. The present invention solves the problems that the existing coronary CT angiography has poor quality, cannot provide a more accurate basis for clinical diagnosis, and enables doctors to not formulate a timely and appropriate treatment plan for patients. The present invention can make the quality of coronary CT angiography good, can provide a more accurate basis for clinical diagnosis, and can enable doctors to formulate a timely and appropriate treatment plan for patients.

Description

Technical Field

[0001] The present invention relates to the technical field of coronary CT, and specifically to a method and system for optimizing the quality of coronary CT angiography. Background Art

[0002] Coronary CT imaging is mainly used for the examination of diseases such as coronary heart disease. Compared with ordinary CT imaging, during the imaging process of coronary CT imaging, a contrast agent needs to be injected into the imaging target, and then the imaging target is scanned by CT to obtain the imaging result.

[0003] Chinese Patent with publication number CN113139961A discloses a method and device for determining the coronary artery dominance type. The method includes: detecting a coronary angiography image to obtain the semi-heart region information and coronary artery position information in the coronary angiography image; based on the coronary artery position information, determining the center line of the coronary artery, where the center line is a curve representing the topological structure of the coronary artery; based on the positional relationship between the neighborhood information of each point on the center line and the semi-heart region information, determining the coronary artery dominance type. However, this patent has the following defects:

[0004] The existing quality of coronary CT angiography is poor and cannot provide a more accurate basis for clinical diagnosis, making it impossible for doctors to formulate timely and appropriate treatment plans for patients. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for optimizing the quality of coronary CT angiography, which can make the quality of coronary CT angiography good, can provide a more accurate basis for clinical diagnosis, and enable doctors to formulate timely and appropriate treatment plans for patients, solving the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A method for optimizing the quality of coronary CT angiography includes the following steps:

[0008] S1: Obtain the electrocardiogram signal of the patient, remove the noise in the electrocardiogram signal of the patient, select appropriate tube voltage, tube current and scanning time parameters, and use a detector and a scanner to perform real-time detection and scanning on the coronary artery of the patient, and collect the coronary CT projection data of the patient;

[0009] S2: Clean the coronary CT projection data of the patient, remove the inconsistent data, invalid values and missing values in the coronary CT projection data of the patient, perform threshold division on the coronary CT projection data of the patient, and determine the coronary CT divided image of the patient;

[0010] S3: Filter the segmented image of the patient's coronary artery CT to remove the noise in the segmented image of the patient's coronary artery CT, enhance the contrast of the segmented image of the patient's coronary artery CT to highlight the detailed features in the segmented image of the patient's coronary artery CT, extract features from the segmented image of the patient's coronary artery CT, and determine the coronary artery CT feature image of the patient;

[0011] S4: Analyze the coronary artery CT feature image of the patient, formulate a reconstruction plan for coronary artery CT angiography of the patient, and generate a coronary artery CT angiogram of the patient based on the coronary artery CT feature image of the patient according to the reconstruction plan for coronary artery CT angiography of the patient.

[0012] According to another aspect of the present invention, there is provided a system for optimizing the quality of coronary artery CT angiography, which is used to implement the method for optimizing the quality of coronary artery CT angiography as described above, including:

[0013] A scanning and acquisition module, which is used to detect and scan the coronary artery of the patient and acquire the CT projection data of the patient's coronary artery;

[0014] A data processing module, which is used to clean, perform threshold segmentation, filter, enhance the contrast and extract features from the CT projection data of the patient's coronary artery, and determine the coronary artery CT feature image of the patient;

[0015] An image reconstruction module, which is used to formulate a reconstruction plan for coronary artery CT angiography and generate a coronary artery CT angiogram.

[0016] Preferably, the scanning and acquisition module includes:

[0017] A detector, which is used to detect the coronary artery of the patient;

[0018] A scanner, which is used to scan the coronary artery of the patient;

[0019] A control unit, which is used to control the detector and the scanner;

[0020] Among them, the electrocardiogram signal of the patient is acquired, the electrocardiogram signal of the patient is preprocessed to remove the noise in the electrocardiogram signal of the patient, appropriate tube voltage, tube current and scanning time parameters are selected based on the denoised electrocardiogram signal of the patient, and a high-quality detector and scanner are used to perform real-time detection and scanning of the coronary artery of the patient to acquire the CT projection data of the patient's coronary artery.

[0021] Preferably, the control unit includes:

[0022] An instruction control unit, which is used to perform instruction control on the detector and the scanner;

[0023] A data control unit for controlling data of the detector and the scanner:

[0024] Among them, a control communication link is established between both the detector and the scanner and the control unit:

[0025] Both the detector and the scanner transmit instructions requesting to establish a control communication link to the control unit;

[0026] After the control unit receives the instructions transmitted by both the detector and the scanner requesting to establish a control communication link, the control unit transmits instructions consenting to establish a control communication link to both the detector and the scanner;

[0027] After both the detector and the scanner receive the instructions transmitted by the control unit consenting to establish a control communication link, both the detector and the scanner establish a control communication link with the control unit according to the instructions transmitted by the control unit consenting to establish a control communication link;

[0028] Among them, the control unit performs instruction control and data control on the detector and the scanner, and is used to start and control the operation of the detector and the scanner.

[0029] Preferably, the data processing module includes:

[0030] A data cleaning unit for cleaning the CT projection data of the patient's coronary artery;

[0031] Obtain the CT projection data of the patient's coronary artery;

[0032] Cleaning the CT projection data of the patient's coronary artery includes:

[0033] Perform consistency check on the CT projection data of the patient's coronary artery;

[0034] Remove inconsistent data in the CT projection data of the patient's coronary artery according to the data consistency requirements;

[0035] Perform processing on invalid values and missing values in the CT projection data of the patient's coronary artery;

[0036] Remove invalid values and missing values that are useless for optimizing the quality of coronary artery CT angiography in the CT projection data of the patient's coronary artery according to the data validity and integrity requirements;

[0037] Determine the CT projection data of the patient's coronary artery that is useful for optimizing the quality of coronary artery CT angiography.

[0038] Preferably, the data processing module further includes:

[0039] A threshold division unit for performing threshold division on the CT projection data of the patient's coronary artery;

[0040] Obtain the patient's coronary artery CT projection data useful for optimizing the quality of coronary artery CT angiography;

[0041] Based on a preset threshold, perform threshold division on the patient's coronary artery CT projection data, remove the pixel points with gray values less than or equal to the threshold in the patient's coronary artery CT projection data, retain the pixel points with gray values greater than the threshold, and determine the patient's coronary artery CT divided image.

[0042] Preferably, the data processing module further includes:

[0043] The first pixel point acquisition module is used to extract the pixel points with gray values greater than the threshold in the CT projection data as the first pixel points;

[0044] The first comparison module is used to compare the number of the first pixel points with a preset pixel point number threshold;

[0045] The second pixel point acquisition module is used to retrieve the pixel points with gray values less than or equal to the threshold in the CT projection data as the second pixel points when the number of the first pixel points is lower than the preset pixel point number threshold;

[0046] The gray value determination parameter acquisition module is used to obtain the gray value determination parameter by using the gray values of the first pixel points and the second pixel points; wherein, the gray value determination parameter is obtained through the following formula:

[0047]

[0048] Wherein, K represents the gray value determination parameter; n represents the total number of the first pixel points; m represents the total number of the second pixel points; H i represents the gray value of the i-th first pixel point; R z represents the median gray value corresponding to the m second pixel points; H z represents the median gray value corresponding to the n first pixel points; R i represents the gray value of the i-th second pixel point; R y represents the preset threshold;

[0049] The image gray value adjustment module is used to determine whether it is necessary to adjust the image gray value of the CT projection data by using the gray value determination parameter.

[0050] Preferably, the image gray value adjustment module includes:

[0051] The second comparison module is used to compare the gray value determination parameter with a preset gray value determination parameter threshold;

[0052] The first grayscale value extraction module is used to extract the grayscale values of all pixel points corresponding to the second pixel point when the grayscale determination parameter is lower than a preset grayscale determination parameter threshold;

[0053] The second grayscale value extraction module is used to extract the grayscale values of all pixel points corresponding to the first pixel point;

[0054] The grayscale adjustment coefficient acquisition module is used to obtain a grayscale adjustment coefficient by using the grayscale values of all pixel points corresponding to the first pixel point;

[0055] Among them, the grayscale adjustment coefficient is obtained through the following formula:

[0056]

[0057] Among them, S represents the grayscale adjustment coefficient; n represents the total number of the first pixel points; H i represents the grayscale value of the i-th first pixel point; R z represents the grayscale intermediate value corresponding to m second pixel points; H z represents the grayscale intermediate value corresponding to n first pixel points; R y represents the preset threshold; f represents the adjustment coefficient, and the adjustment coefficient is obtained through the following formula:

[0058]

[0059] Among them, f represents the adjustment coefficient; H i represents the grayscale value of the i-th first pixel point; R z represents the grayscale intermediate value corresponding to m second pixel points; H z represents the grayscale intermediate value corresponding to n first pixel points; R p represents the grayscale average value corresponding to m second pixel points; H p represents the grayscale average value corresponding to n first pixel points; R max represents the grayscale maximum value corresponding to m second pixel points; H min represents the grayscale minimum value corresponding to n first pixel points;

[0060] The grayscale value adjustment execution module is used to adjust the grayscale values of all pixel points corresponding to the second pixel point by using the grayscale adjustment coefficient. Among them, the adjusted grayscale value corresponding to each pixel point in the second pixel point is obtained through the following formula:

[0061]

[0062] Among them, R tdenote the adjusted gray values corresponding to each pixel in the second pixel points; R0 denotes the gray values before adjustment corresponding to each pixel in the second pixel points; S denotes the gray scale adjustment coefficient; R z denote the gray median values corresponding to m second pixel points; H min denote the minimum gray value corresponding to n first pixel points; R y denote the preset threshold value;

[0063] A secondary partitioning module, configured to perform secondary image partitioning on the second pixel points with adjusted gray values by using a preset threshold value, so as to obtain a CT partition image of the patient's coronary artery after secondary partitioning.

[0064] Preferably, the data processing module further includes:

[0065] An image filtering unit, configured to perform filtering processing on the CT partition image of the patient's coronary artery;

[0066] Obtain the CT partition image of the patient's coronary artery;

[0067] Based on a median filter, perform filtering processing on the CT partition image of the patient's coronary artery;

[0068] Replace the value of a point in the CT partition image of the patient's coronary artery with the median value of the values of the points in a neighborhood of this point, so that pixels with relatively large differences in the gray values of surrounding pixels are changed to take values close to the values of surrounding pixels, eliminate isolated noise points, and remove the noise in the CT partition image of the patient's coronary artery;

[0069] A contrast enhancement unit, configured to perform contrast enhancement on the CT partition image of the patient's coronary artery;

[0070] Obtain the CT partition image of the patient's coronary artery after filtering;

[0071] Perform contrast enhancement on the CT partition image of the patient's coronary artery after filtering;

[0072] Highlight the detail features in the CT partition image of the patient's coronary artery;

[0073] A feature extraction unit, configured to perform feature extraction on the CT partition image of the patient's coronary artery;

[0074] Obtain the CT partition image of the patient's coronary artery after contrast enhancement;

[0075] Perform feature extraction on the CT partition image of the patient's coronary artery after contrast enhancement;

[0076] Based on image cropping and magnification techniques, perform extraction of the region of interest ROI on the CT partition image of the patient's coronary artery;

[0077] Extract the area containing important information in the segmented image of the patient's coronary artery CT, remove the irrelevant background in the segmented image of the patient's coronary artery CT, and focus the analysis on the key parts of the segmented image of the patient's coronary artery CT;

[0078] Determine the characteristic image of the patient's coronary artery CT.

[0079] Preferably, the image reconstruction module includes:

[0080] An image analysis unit for analyzing the characteristic image of the patient's coronary artery CT;

[0081] Obtain the characteristic image of the patient's coronary artery CT;

[0082] Analyze the characteristic image of the patient's coronary artery CT;

[0083] Based on three-dimensional modeling technology, formulate a reconstruction plan for the coronary artery CT angiography of the patient;

[0084] An image reconstruction unit for generating a coronary artery CT angiogram;

[0085] Obtain the reconstruction plan for the coronary artery CT angiography of the patient;

[0086] According to the reconstruction plan for the coronary artery CT angiography of the patient, based on the characteristic image of the patient's coronary artery CT, generate the coronary artery CT angiogram of the patient.

[0087] Compared with the prior art, the beneficial effects of the present invention are:

[0088] 1. By obtaining the electrocardiogram signal of the patient, removing the noise in the electrocardiogram signal of the patient, selecting appropriate tube voltage, tube current and scanning time parameters, using a detector and a scanner to perform real-time detection and scanning on the coronary artery of the patient, collecting the projection data of the patient's coronary artery CT, cleaning the projection data of the patient's coronary artery CT, removing the inconsistent data, invalid values and missing values in the projection data of the patient's coronary artery CT, and performing threshold segmentation on the projection data of the patient's coronary artery CT to determine the segmented image of the patient's coronary artery CT, the present invention can effectively suppress the generation of artifacts and further improve the image quality.

[0089] 2. The present invention filters the segmented images of the patient's coronary artery CT to remove the noise in the segmented images of the patient's coronary artery CT, enhances the contrast of the segmented images of the patient's coronary artery CT to highlight the detailed features in the segmented images of the patient's coronary artery CT, extracts the features of the segmented images of the patient's coronary artery CT to determine the coronary artery CT feature images of the patient, analyzes the coronary artery CT feature images of the patient, formulates a reconstruction plan for the coronary artery CT angiography of the patient, and generates the coronary artery CT angiography images of the patient based on the coronary artery CT feature images of the patient according to the reconstruction plan for the coronary artery CT angiography of the patient. This can result in good quality of coronary artery CT angiography, provide a more accurate basis for clinical diagnosis, and enable doctors to formulate timely and appropriate treatment plans for patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 is a flowchart of the method for optimizing the quality of coronary artery CT angiography according to the present invention;

[0091] Figure 2 is a structural block diagram of the system for optimizing the quality of coronary artery CT angiography according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0092] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0093] To solve the problem that the existing coronary artery CT angiography has poor quality, cannot provide a more accurate basis for clinical diagnosis, and makes it impossible for doctors to formulate timely and appropriate treatment plans for patients, please refer to Figure 1 - Figure 2 , the following technical solutions are provided in this embodiment: Embodiment 1

[0094] A system for optimizing the quality of coronary artery CT angiography includes: a scanning and acquisition module, a data processing module, and an image reconstruction module.

[0095] Among them, the coronary artery of the patient is detected and scanned through the scanning and acquisition module to collect the coronary artery CT projection data of the patient; the coronary artery CT projection data of the patient is cleaned, threshold segmented, filtered, contrast enhanced, and feature extracted through the data processing module to determine the coronary artery CT feature images of the patient; the image reconstruction module formulates a reconstruction plan for the coronary artery CT angiography of the patient and generates the coronary artery CT angiography images.

[0096] In this embodiment, the scanning and acquisition module includes:

[0097] A detector for detecting a patient's coronary artery;

[0098] A scanner for scanning a patient's coronary artery;

[0099] A control unit for controlling the detector and the scanner;

[0100] Wherein, the electrocardiogram signal of the patient is acquired, preprocessed, the noise in the electrocardiogram signal of the patient is removed, based on the denoised electrocardiogram signal of the patient, appropriate tube voltage, tube current and scanning time parameters are selected, and a high-quality detector and scanner are used to perform real-time detection and scanning on the patient's coronary artery, and the coronary artery CT projection data of the patient is collected.

[0101] Specifically, the control unit includes:

[0102] An instruction control unit for performing instruction control on the detector and the scanner;

[0103] A data control unit for performing data control on the detector and the scanner:

[0104] Wherein, a control communication link is established between both the detector and the scanner and the control unit:

[0105] Both the detector and the scanner transmit instructions requesting to establish a control communication link to the control unit;

[0106] After the control unit receives the instructions transmitted by both the detector and the scanner requesting to establish a control communication link, the control unit transmits instructions consenting to establish a control communication link to both the detector and the scanner;

[0107] After both the detector and the scanner receive the instructions transmitted by the control unit consenting to establish a control communication link, both the detector and the scanner establish a control communication link with the control unit according to the instructions transmitted by the control unit consenting to establish a control communication link;

[0108] Wherein, the control unit performs instruction control and data control on the detector and the scanner to start and control the operation of the detector and the scanner.

[0109] Specifically, when the patient is ready, the control unit sends a control signal to the detector and the scanner to turn on the detector and the scanner, and a high-quality detector and scanner are used to perform real-time detection and scanning on the patient's coronary artery, and the coronary artery CT projection data of the patient is collected.

[0110] In this embodiment, the data processing module includes:

[0111] A data cleaning unit for cleaning the coronary artery CT projection data of a patient;

[0112] Obtain the coronary artery CT projection data of the patient;

[0113] Clean the coronary artery CT projection data of the patient, including:

[0114] Perform consistency check on the coronary artery CT projection data of the patient;

[0115] Remove the inconsistent data in the coronary artery CT projection data of the patient according to the data consistency requirements;

[0116] Process the invalid values and missing values in the coronary artery CT projection data of the patient;

[0117] Remove the invalid values and missing values that are useless for optimizing the quality of coronary artery CT angiography in the coronary artery CT projection data of the patient according to the data validity and integrity requirements;

[0118] Determine the coronary artery CT projection data of the patient that is useful for optimizing the quality of coronary artery CT angiography.

[0119] Specifically, by cleaning the coronary artery CT projection data of the patient, the inconsistent data, invalid values and missing values in the coronary artery CT projection data of the patient can be removed, and the processing accuracy and processing speed of the subsequent coronary artery CT projection data of the patient can be improved.

[0120] In this embodiment, the data processing module further includes:

[0121] A threshold division unit for performing threshold division on the coronary artery CT projection data of the patient;

[0122] Obtain the coronary artery CT projection data of the patient that is useful for optimizing the quality of coronary artery CT angiography;

[0123] Based on a preset threshold, perform threshold division on the coronary artery CT projection data of the patient, remove the pixel points with gray values less than or equal to the threshold in the coronary artery CT projection data of the patient, retain the pixel points with gray values greater than the threshold, and determine the coronary artery CT divided image, which can effectively suppress the generation of artifacts and further improve the image quality.

[0124] Specifically, the data processing module further includes:

[0125] A first pixel point acquisition module for extracting the pixel points with gray values greater than the threshold in the CT projection data as the first pixel points;

[0126] A first comparison module for comparing the number of the first pixel points with a preset pixel point number threshold;

[0127] A second pixel point acquisition module, configured to, when the number of the first pixel points is lower than a preset pixel point number threshold, retrieve pixel points in the CT projection data whose gray values are less than or equal to the threshold as second pixel points;

[0128] A gray value determination parameter acquisition module, configured to acquire a gray value determination parameter by using the gray values of the first pixel points and the second pixel points; wherein, the gray value determination parameter is acquired through the following formula:

[0129]

[0130] wherein, K represents the gray value determination parameter; n represents the total number of the first pixel points; m represents the total number of the second pixel points; H i represents the gray value of the i-th first pixel point; R z represents the gray middle value corresponding to the m second pixel points; H z represents the gray middle value corresponding to the n first pixel points; R i represents the gray value corresponding to the i-th second pixel point; R y represents the preset threshold;

[0131] An image gray value adjustment module, configured to determine whether to perform image gray value adjustment on the CT projection data by using the gray value determination parameter.

[0132] The technical effects of the above technical solution are as follows: Through the first pixel point acquisition module, this technical solution can accurately extract the pixel points with gray values greater than the preset threshold in the CT projection data as the first pixel points. This screening method helps to focus on the highlighted areas in the image, and usually these areas contain more diagnostic information. The function of the first comparison module is to evaluate the number of the first pixel points. If the number is lower than the preset threshold, the second pixel point acquisition module is used to further analyze the pixel points with gray values less than or equal to the threshold (i.e., the second pixel points). This strategy increases the flexibility of data processing, especially in the case of relatively blurred or low-contrast image information, and can provide more comprehensive pixel point information for subsequent analysis. The gray value determination parameter acquisition module calculates the gray value determination parameter K through the above formula. This parameter comprehensively considers the gray value distribution of the first pixel points and the second pixel points and their relationship with the preset threshold. This calculation method not only considers the highlighted areas (the first pixel points) but also the information of the dark areas (the second pixel points), so as to more comprehensively and accurately reflect the gray characteristics of the image. The image gray value adjustment module uses the gray value determination parameter K to determine whether it is necessary to perform image gray value adjustment on the CT projection data. This automated decision-making process reduces the need for manual intervention, improves the efficiency and accuracy of image processing. At the same time, performing gray value adjustment according to the gray value determination parameter can ensure that the adjusted image more meets the diagnostic requirements and improves the readability of the image and the accuracy of diagnosis.

[0133] In summary, through precise pixel point screening, flexible pixel point processing strategies, intelligent gray value determination parameter calculation, and automated image gray value adjustment decision-making, this technical solution realizes the efficient and accurate processing of CT projection data, improving the image quality and diagnostic accuracy.

[0134] Specifically, the image gray value adjustment module includes:

[0135] A second comparison module for comparing the gray value determination parameter with a preset gray value determination parameter threshold;

[0136] A first gray value extraction module for extracting the gray values of all pixel points corresponding to the second pixel points when the gray value determination parameter is lower than the preset gray value determination parameter threshold;

[0137] A second gray value extraction module for extracting the gray values of all pixel points corresponding to the first pixel points;

[0138] A gray value adjustment coefficient acquisition module for obtaining a gray value adjustment coefficient by using the gray values of all pixel points corresponding to the first pixel points;

[0139] Among them, the gray value adjustment coefficient is obtained through the following formula:

[0140]

[0141] Among them, S represents the gray-scale adjustment coefficient; n represents the total number of the first pixel points; H i represents the gray-scale value of the i-th first pixel point; R z represents the gray-scale intermediate value corresponding to m second pixel points; H z represents the gray-scale intermediate value corresponding to n first pixel points; R y represents the preset threshold; f represents the adjustment coefficient, and the adjustment coefficient is obtained through the following formula:

[0142]

[0143] Among them, f represents the adjustment coefficient; H i represents the gray-scale value of the i-th first pixel point; R z represents the gray-scale intermediate value corresponding to m second pixel points; H z represents the gray-scale intermediate value corresponding to n first pixel points; R p represents the gray-scale average value corresponding to m second pixel points; H p represents the gray-scale average value corresponding to n first pixel points; R max represents the gray-scale maximum value corresponding to m second pixel points; H min represents the gray-scale minimum value corresponding to n first pixel points;

[0144] The gray-scale value adjustment execution module is used to adjust the gray-scale values of all pixel points corresponding to the second pixel points by using the gray-scale adjustment coefficient. Among them, the adjusted gray-scale value corresponding to each pixel point in the second pixel points is obtained through the following formula:

[0145]

[0146] Among them, R t represents the adjusted gray-scale value corresponding to each pixel point in the second pixel points; R0 represents the gray-scale value before adjustment corresponding to each pixel point in the second pixel points; S represents the gray-scale adjustment coefficient; R z represents the gray-scale intermediate value corresponding to m second pixel points; H min represents the gray-scale minimum value corresponding to n first pixel points; R y represents the preset threshold;

[0147] The secondary division module is used to perform secondary image division on the second pixel points with adjusted gray-scale values by using the preset threshold to obtain the patient coronary artery CT division image after secondary division.

[0148] The technical effects of the above technical solution are as follows: Through the second comparison module, this technical solution can accurately determine whether the gray scale state of the current image needs to be adjusted, which is based on the comparison between the gray scale determination parameter and the preset threshold.

[0149] When it is determined that adjustment is needed, the first gray scale value extraction module and the second gray scale value extraction module respectively extract the gray scale values of the second pixel point (dark area) and the first pixel point (bright area), providing a data basis for subsequent calculation of the gray scale adjustment coefficient. The gray scale adjustment coefficient acquisition module calculates the gray scale adjustment coefficient S using the above formula. This coefficient comprehensively considers multiple factors such as the gray scale value distribution, gray scale median value, gray scale average value, gray scale maximum value, and minimum value of the first pixel point and the second pixel point. The introduction of the adjustment coefficient f further increases the flexibility and accuracy of gray scale adjustment, enabling the gray scale adjustment to better adapt to the characteristics of different images. The gray scale value adjustment execution module precisely adjusts the gray scale value of the second pixel point (dark area) using the gray scale adjustment coefficient S. The adjustment formula not only considers the gray scale adjustment coefficient but also combines the original gray scale value, gray scale median value, and preset threshold of the second pixel point, ensuring that the adjusted gray scale value meets the medical diagnosis requirements and maintains the coherence and naturalness of the image. The secondary division module performs secondary image division on the second pixel point after gray scale value adjustment using the preset threshold. This step helps to further highlight the key information in the image, such as the details of the patient's coronary artery. Through secondary division, a clearer and more accurate coronary artery CT division image can be obtained, providing more valuable diagnostic information for doctors. This technical solution significantly improves the clarity and contrast of coronary artery CT images through fine gray scale adjustment and optimized image division. This helps doctors more accurately identify the pathological conditions of the coronary artery, improving the accuracy and efficiency of diagnosis.

[0150] In summary, this technical solution realizes the efficient and accurate processing of coronary artery CT images through a fine gray scale adjustment strategy, intelligent calculation of the gray scale adjustment coefficient, precise execution of gray scale value adjustment, and optimized image division, improving the accuracy and efficiency of diagnosis.

[0151] In this embodiment, the data processing module further includes:

[0152] An image filtering unit for filtering the coronary artery CT division image of the patient;

[0153] Obtain the coronary artery CT division image of the patient;

[0154] Based on a median filter, filter the coronary artery CT division image of the patient;

[0155] Replace the value of a point in the segmented image of the patient's coronary artery CT with the median value of the values of the points in a neighborhood of that point, make the pixels with a relatively large difference in gray values of the surrounding pixels take values closer to the values of the surrounding pixels, eliminate isolated noise points, remove the noise in the segmented image of the patient's coronary artery CT, eliminate noise interference, and improve the image quality;

[0156] A contrast enhancement unit for enhancing the contrast of the segmented image of the patient's coronary artery CT;

[0157] Obtain the filtered segmented image of the patient's coronary artery CT;

[0158] Enhance the contrast of the filtered segmented image of the patient's coronary artery CT;

[0159] Highlight the detailed features in the segmented image of the patient's coronary artery CT, and can reduce image blurring;

[0160] A feature extraction unit for extracting features from the segmented image of the patient's coronary artery CT;

[0161] Obtain the segmented image of the patient's coronary artery CT after contrast enhancement;

[0162] Extract features from the segmented image of the patient's coronary artery CT after contrast enhancement;

[0163] Based on image cropping and magnification techniques, extract the region of interest (ROI) from the segmented image of the patient's coronary artery CT;

[0164] Extract the region containing important information in the segmented image of the patient's coronary artery CT, remove the irrelevant background in the segmented image of the patient's coronary artery CT, and focus the analysis on the key parts of the segmented image of the patient's coronary artery CT;

[0165] Determine the coronary artery CT feature image of the patient.

[0166] Specifically, by extracting features from the segmented image of the patient's coronary artery CT, determine the coronary artery CT feature image of the patient, which is convenient for formulating the subsequent coronary artery CT angiography reconstruction plan and generating the coronary artery CT blood vessel image of the patient.

[0167] In this embodiment, the image reconstruction module includes:

[0168] An image analysis unit for analyzing the coronary artery CT feature image of the patient;

[0169] Obtain the coronary artery CT feature image of the patient;

[0170] Analyze the coronary artery CT feature image of the patient;

[0171] Based on three-dimensional modeling technology, a reconstruction plan for the patient's coronary artery CT angiography is formulated;

[0172] An image reconstruction unit for generating coronary artery CT angiography images;

[0173] Obtain the reconstruction plan for the patient's coronary artery CT angiography;

[0174] Based on the reconstruction plan for the patient's coronary artery CT angiography and the patient's coronary artery CT feature images, generate the patient's coronary artery CT angiography images. Embodiment 2

[0175] To better demonstrate the coronary artery CT angiography quality optimization process, this embodiment now provides a coronary artery CT angiography quality optimization method, which is implemented based on the coronary artery CT angiography quality optimization system as described above, and includes the following steps:

[0176] S1: Obtain the patient's electrocardiogram signal, remove the noise in the patient's electrocardiogram signal, select appropriate tube voltage, tube current and scanning time parameters, and use a detector and a scanner to perform real-time detection and scanning of the patient's coronary artery to collect the patient's coronary artery CT projection data;

[0177] S2: Clean the patient's coronary artery CT projection data, remove the inconsistent data, invalid values and missing values in the patient's coronary artery CT projection data, perform threshold division on the patient's coronary artery CT projection data, and determine the patient's coronary artery CT divided image;

[0178] S3: Perform filtering on the patient's coronary artery CT divided image to remove the noise in the patient's coronary artery CT divided image, enhance the contrast of the patient's coronary artery CT divided image to highlight the detail features in the patient's coronary artery CT divided image, and perform feature extraction on the patient's coronary artery CT divided image to determine the patient's coronary artery CT feature image;

[0179] S4: Analyze the patient's coronary artery CT feature image, formulate a reconstruction plan for the patient's coronary artery CT angiography, and based on the reconstruction plan for the patient's coronary artery CT angiography and the patient's coronary artery CT feature image, generate the patient's coronary artery CT angiography images.

[0180] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0181] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the quality of coronary CT angiography, which is implemented based on a coronary CT angiography quality optimization system, and is characterized in that, It includes the following steps: S1: Obtain the electrocardiogram signal of the patient, remove the noise in the electrocardiogram signal of the patient, select appropriate tube voltage, tube current and scanning time parameters, and use a detector and a scanner to perform real-time detection and scanning on the coronary artery of the patient, and collect the coronary artery CT projection data of the patient; S2: Clean the coronary artery CT projection data of the patient, remove the inconsistent data, invalid values and missing values in the coronary artery CT projection data of the patient, perform threshold division on the coronary artery CT projection data of the patient, and determine the coronary artery CT divided image of the patient; S3: Perform filtering processing on the coronary artery CT divided image of the patient, remove the noise in the coronary artery CT divided image of the patient, enhance the contrast of the coronary artery CT divided image of the patient, highlight the detail features in the coronary artery CT divided image of the patient, perform feature extraction on the coronary artery CT divided image of the patient, and determine the coronary artery CT feature image of the patient; S4: Analyze the coronary artery CT feature image of the patient, formulate a coronary artery CT angiography reconstruction plan, and generate a coronary artery CT angiogram of the patient based on the coronary artery CT feature image of the patient according to the coronary artery CT angiography reconstruction plan; The coronary artery CT angiography quality optimization system includes: A scanning and acquisition module, which is used to detect and scan the coronary artery of the patient and collect the coronary artery CT projection data of the patient; A data processing module, which is used to clean, perform threshold division, filter, enhance contrast and perform feature extraction on the coronary artery CT projection data of the patient, and determine the coronary artery CT feature image of the patient; An image reconstruction module, which is used to formulate a coronary artery CT angiography reconstruction plan and generate a coronary artery CT angiogram; The data processing module includes: A first pixel point acquisition module, which is used to extract the pixel points with gray values greater than the threshold in the CT projection data as the first pixel points; A first comparison module, which is used to compare the number of the first pixel points with a preset pixel point number threshold; A second pixel point acquisition module, which is used to retrieve the pixel points with gray values less than or equal to the threshold in the CT projection data as the second pixel points when the number of the first pixel points is lower than the preset pixel point number threshold; A gray scale determination parameter acquisition module, which is used to obtain a gray scale determination parameter by using the gray values of the first pixel points and the second pixel points; wherein, the gray scale determination parameter is obtained by the following formula: Among them, K represents the gray-scale determination parameter; n represents the total number of the first pixel points; m represents the total number of the second pixel points; H i represents the gray-scale value of the i-th first pixel point; R z represents the intermediate gray-scale value corresponding to the m second pixel points; H z represents the intermediate gray-scale value corresponding to the n first pixel points; R i represents the gray-scale value corresponding to the i-th second pixel point; R y represents a preset threshold value; An image gray scale adjustment module, which is used to determine whether it is necessary to perform image gray scale adjustment on the CT projection data by using the gray scale determination parameter.

2. The coronary CT angiography quality optimization method according to claim 1, characterized in that The scanning and acquisition module includes: A detector, which is used to detect the coronary artery of the patient; A scanner, which is used to scan the coronary artery of the patient; A control unit, which is used to control the detector and the scanner; Among them, the electrocardiogram signal of the patient is acquired, preprocessed to remove the noise in the electrocardiogram signal of the patient. Based on the denoised electrocardiogram signal of the patient, appropriate tube voltage, tube current and scanning time parameters are selected, and a high-quality detector and scanner are used to perform real-time detection and scanning on the coronary artery of the patient, and the coronary artery CT projection data of the patient is collected.

3. The coronary CT angiography quality optimization method according to claim 2, wherein The control unit includes: An instruction control unit for performing instruction control on the detector and scanner; A data control unit for performing data control on the detector and scanner: Among them, a control communication link is established between both the detector and scanner and the control unit: Both the detector and scanner transmit instructions requesting to establish a control communication link to the control unit; After the control unit receives the instructions transmitted by both the detector and scanner requesting to establish a control communication link, the control unit transmits instructions consenting to establish a control communication link to both the detector and scanner; After both the detector and scanner receive the instructions transmitted by the control unit consenting to establish a control communication link, both the detector and scanner establish a control communication link with the control unit according to the instructions transmitted by the control unit consenting to establish a control communication link; Among them, the control unit performs instruction control and data control on the detector and scanner to start and control the operation of the detector and scanner.

4. The coronary CT angiography quality optimization method according to claim 3, wherein The data processing module includes: A data cleaning unit for cleaning the coronary artery CT projection data of the patient; Acquire the coronary artery CT projection data of the patient; Cleaning the coronary artery CT projection data of the patient includes: Performing consistency check on the coronary artery CT projection data of the patient; Removing inconsistent data in the coronary artery CT projection data of the patient according to the data consistency requirements; Performing processing on invalid values and missing values in the coronary artery CT projection data of the patient; Removing invalid values and missing values that are useless for optimizing the quality of coronary artery CT angiography in the coronary artery CT projection data of the patient according to the data validity and integrity requirements; Determine the coronary artery CT projection data of the patient that is useful for optimizing the quality of coronary artery CT angiography.

5. The coronary CT angiography quality optimization method according to claim 4, characterized in that The data processing module further includes: A threshold division unit for performing threshold division on the coronary artery CT projection data of the patient; Obtain the coronary artery CT projection data of the patient that is useful for optimizing the quality of coronary artery CT angiography; Based on a preset threshold, perform threshold division on the coronary artery CT projection data of the patient, remove the pixel points with gray values less than or equal to the threshold in the coronary artery CT projection data of the patient, retain the pixel points with gray values greater than the threshold, and determine the coronary artery CT division image of the patient.

6. The coronary CT angiography quality optimization method according to claim 5, wherein, The image gray level adjustment module includes: A second comparison module for comparing the gray level determination parameter with a preset gray level determination parameter threshold; A first gray value extraction module for extracting the gray values of all pixel points corresponding to the second pixel points when the gray level determination parameter is lower than the preset gray level determination parameter threshold; A second gray value extraction module for extracting the gray values of all pixel points corresponding to the first pixel points; A grayscale adjustment coefficient acquisition module, configured to acquire a grayscale adjustment coefficient by using the grayscale values of all pixel points corresponding to the first pixel point; Wherein, the grayscale adjustment coefficient is acquired by the following formula: Wherein, S represents the gray scale adjustment coefficient; n represents the total number of the first pixel points; H i represents the gray scale value of the i-th first pixel point; R z represents the gray scale intermediate value corresponding to m second pixel points; H z represents the gray scale intermediate value corresponding to n first pixel points; R y represents the preset threshold; f represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula: where f represents the adjustment coefficient; H i represents the gray value of the i-th first pixel; R z represents the intermediate gray value corresponding to m second pixels; H z represents the intermediate gray value corresponding to n first pixels; R p represents the average gray value corresponding to m second pixels; H p represents the average gray value corresponding to n first pixels; R max represents the maximum gray value corresponding to m second pixels; H min represents the minimum gray value corresponding to n first pixels; A grayscale value adjustment execution module, configured to perform grayscale value adjustment on the grayscale values of all pixel points corresponding to the second pixel point by using the grayscale adjustment coefficient, wherein the adjusted grayscale value corresponding to each pixel point in the second pixel point is acquired by the following formula: Among them, R t represents the adjusted gray value corresponding to each pixel in the second pixel points; R0 represents the gray value before adjustment corresponding to each pixel in the second pixel points; S represents the gray adjustment coefficient; R z represents the gray intermediate value corresponding to m second pixel points; H min represents the minimum gray value corresponding to n first pixel points; R y represents the preset threshold value; A secondary partitioning module, configured to perform secondary image partitioning on the second pixel point after grayscale value adjustment by using a preset threshold to obtain a patient coronary artery CT partitioned image after secondary partitioning.

7. The coronary CT angiography quality optimization method according to claim 6, characterized in that The data processing module further includes: An image filtering unit, configured to perform filtering processing on the patient coronary artery CT partitioned image; Obtain a patient coronary artery CT partitioned image; Based on a median filter, perform filtering processing on the patient coronary artery CT partitioned image; Replace the value of a point in the patient coronary artery CT partitioned image with the median value of the values of all points in a neighborhood of this point, make pixels with a relatively large difference in grayscale values of surrounding pixels take values close to the surrounding pixel values, eliminate isolated noise points, and remove the noise in the patient coronary artery CT partitioned image; A contrast enhancement unit, configured to perform contrast enhancement on the patient coronary artery CT partitioned image; Obtain the patient coronary artery CT partitioned image after filtering; Perform contrast enhancement on the patient coronary artery CT partitioned image after filtering; Highlight the detail features in the patient coronary artery CT partitioned image; A feature extraction unit, configured to perform feature extraction on the patient coronary artery CT partitioned image; Obtain the patient coronary artery CT partitioned image after contrast enhancement; Perform feature extraction on the patient coronary artery CT partitioned image after contrast enhancement; Based on image cropping and magnification techniques, perform region of interest (ROI) extraction on the patient coronary artery CT partitioned image; Extract the region containing important information in the patient coronary artery CT partitioned image, remove the irrelevant background in the patient coronary artery CT partitioned image, and focus the analysis on the key parts of the patient coronary artery CT partitioned image; Determine the patient coronary artery CT feature image.

8. The coronary CT angiography quality optimization method according to claim 7, wherein The image reconstruction module includes: An image analysis unit, configured to analyze the patient coronary artery CT feature image; Obtain the patient coronary artery CT feature image; Analyze the patient coronary artery CT feature image; Based on three-dimensional modeling techniques, formulate a patient coronary artery CT angiography reconstruction plan; An image reconstruction unit, configured to generate a coronary artery CT angiogram; Obtain the patient coronary artery CT angiography reconstruction plan; Based on the patient coronary artery CT angiography reconstruction plan and the patient coronary artery CT feature image, generate a patient coronary artery CT angiogram.

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