Coronary artery CT angiography quality optimization method and system

Abstract

The invention discloses a coronary artery CT angiography quality optimization method and system, and belongs to the technical field of coronary artery CT, and the method comprises the following steps: S1, collecting coronary artery CT projection data of a patient; s2, performing threshold division on the CT projection data of the coronary artery of the patient, and determining a CT division image of the coronary artery of the patient; s3, filtering, contrast enhancement and feature extraction are carried out on the CT divided image of the coronary artery of the patient, and a CT feature image of the coronary artery of the patient is determined; and S4, generating a CT blood vessel image of the coronary artery of the patient. The problems that existing coronary artery CT angiography is poor in quality, a more accurate basis cannot be provided for clinical diagnosis, and a doctor cannot formulate a timely and appropriate treatment scheme for a patient are solved. According to the method, the coronary artery CT angiography quality is good, a more accurate basis can be provided for clinical diagnosis, and a doctor can formulate a timely and proper treatment scheme for a patient.

Description

Technical Field

[0001] The present invention relates to the field of coronary CT technology, and in particular to a method and system for optimizing coronary CT vascular imaging quality. Background Art

[0002] Coronary CT imaging is mainly used for the examination of diseases such as coronary heart disease. Compared with ordinary CT imaging, coronary CT imaging requires the injection of contrast agent into the imaging target during the imaging process, and then a CT scan of the imaging target is performed to obtain the imaging results.

[0003] The Chinese patent with publication number CN113139961A discloses a method and device for determining the dominant type of coronary arteries, the method comprising: detecting a coronary angiography image to obtain semi-heart region information and coronary artery position information in the coronary angiography image; determining the centerline of the coronary artery based on the coronary artery position information, the centerline being a curve representing the topological structure of the coronary artery; determining the dominant type of the coronary artery based on the positional relationship between the neighborhood information of each point on the centerline and the semi-heart region information; however, the patent has the following defects: Existing coronary CT vascular imaging is of poor quality and cannot provide a more accurate basis for clinical diagnosis, making it impossible for doctors to develop timely and appropriate treatment plans for patients. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for optimizing the quality of coronary CT vascular imaging, which can improve the quality of coronary CT vascular imaging, provide a more accurate basis for clinical diagnosis, and enable doctors to formulate timely and appropriate treatment plans for patients, thereby solving the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: The method for optimizing the quality of coronary CT angiography includes the following steps: S1: Obtain the patient's ECG signal, remove the noise in the patient's ECG signal, select appropriate tube voltage, tube current and scanning time parameters, use the detector and scanner to perform real-time detection and scanning of the patient's coronary artery, and collect the patient's coronary artery CT projection data; S2: cleaning the patient's coronary artery CT projection data, removing inconsistent data, invalid values ​​and missing values ​​in the patient's coronary artery CT projection data, performing threshold division on the patient's coronary artery CT projection data, and determining the patient's coronary artery CT division image; S3: filtering the patient's coronary artery CT segmented image to remove noise in the patient's coronary artery CT segmented image, contrast enhancing the patient's coronary artery CT segmented image to highlight detail features in the patient's coronary artery CT segmented image, extracting features from the patient's coronary artery CT segmented image, and determining the patient's coronary artery CT feature image; S4: Analyze the patient's coronary artery CT characteristic image, formulate a reconstruction plan for the patient's coronary artery CT vascular imaging, and generate the patient's coronary artery CT vascular image based on the patient's coronary artery CT characteristic image according to the reconstruction plan for the patient's coronary artery CT vascular imaging.

[0006] According to another aspect of the present invention, a coronary CT vascular imaging quality optimization system is provided, which is used to implement the coronary CT vascular imaging quality optimization method as described above, comprising: A scanning acquisition module is used to detect and scan the patient's coronary arteries and collect the patient's coronary artery CT projection data; A data processing module is used to clean, threshold, filter, contrast enhance and feature extract the patient's coronary artery CT projection data to determine the patient's coronary artery CT feature image; The image reconstruction module is used to formulate a reconstruction plan for the patient's coronary CT vascular imaging and generate a coronary CT vascular image.

[0007] Preferably, the scanning acquisition module includes: A detector, used for detecting the coronary artery of the patient; a scanner to scan a patient's coronary arteries; A control unit for controlling the detector and the scanner; Among them, the patient's ECG signal is obtained, the patient's ECG signal is preprocessed, the noise in the patient's ECG signal is removed, and based on the denoised patient's ECG signal, appropriate tube voltage, tube current and scanning time parameters are selected, and the patient's coronary artery is detected and scanned in real time using a high-quality detector and scanner to collect the patient's coronary artery CT projection data.

[0008] Preferably, the control unit comprises: A command control unit, used for command control of the detector and the scanner; Data control unit, used to control the detector and scanner: Among them, a control communication link is established between the detector and the scanner and the control unit: The detector and the scanner both transmit instructions to the control unit requesting the establishment of a control communication link; 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 to both the detector and the scanner agreeing to establish the control communication link; After the detector and the scanner both receive the instruction transmitted by the control unit for agreeing to establish the control communication link, the detector and the scanner both establish the control communication link with the control unit according to the instruction transmitted by the control unit for agreeing to establish the control communication link; The control unit performs command 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.

[0009] Preferably, the data processing module includes: A data cleaning unit, used for cleaning the patient's coronary artery CT projection data; Acquire the patient's coronary artery CT projection data; Clean the patient's coronary artery CT projection data, including: Check the consistency of the patient's coronary artery CT projection data; According to the data consistency requirements, the inconsistent data in the patient's coronary artery CT projection data were removed; Invalid and missing values ​​were processed for the patients’ coronary artery CT projection data; According to the requirements of data validity and completeness, invalid values ​​and missing values ​​in the patient's coronary artery CT projection data that are useless for optimizing the quality of coronary artery CT vascular imaging are removed; To identify patient coronary CT projection data useful for optimizing the quality of coronary CT angiography.

[0010] Preferably, the data processing module further includes: A threshold division unit, used for performing threshold division on the patient's coronary artery CT projection data; Acquiring patient coronary artery CT projection data useful for optimizing the quality of coronary CT vascular imaging; Based on a preset threshold, the patient's coronary artery CT projection data is threshold-divided, the pixel points with grayscale values ​​less than or equal to the threshold in the patient's coronary artery CT projection data are removed, and the pixel points with grayscale values ​​greater than the threshold are retained to determine the patient's coronary artery CT segmentation image.

[0011] Preferably, the data processing module further includes: A first pixel point acquisition module is used to extract pixel points whose grayscale values ​​are greater than a threshold value in the CT projection data as first pixel points; A first comparison module, used for comparing the number of the first pixels with a preset pixel number threshold; A second pixel point acquisition module is used to retrieve pixels whose grayscale values ​​are less than or equal to the threshold value in the CT projection data as second pixels when the number of the first pixels is lower than a preset pixel point number threshold value; A grayscale determination parameter acquisition module is used to obtain a grayscale determination parameter using the grayscale value of the first pixel and the grayscale value of the second pixel; wherein the grayscale determination parameter is obtained by the following formula: Wherein, K represents the grayscale determination parameter; n represents the total number of first pixel points; m represents the total number of second pixel points; H i Represents the gray value of the i-th first pixel; R z H represents the grayscale median value corresponding to the m second pixel points; z Represents the grayscale intermediate value corresponding to the n first pixel points; R i Represents the gray value corresponding to the i-th second pixel; R y represents the preset threshold value; The image grayscale adjustment module is used to use the grayscale determination parameter to determine whether image grayscale adjustment is required for the CT projection data.

[0012] Preferably, the image grayscale adjustment module includes: A second comparison module, used for comparing the grayscale determination parameter with a preset grayscale determination parameter threshold; A first grayscale value extraction module, configured to extract 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; A second grayscale value extraction module is used to extract the grayscale values ​​of all pixel points corresponding to the first pixel point; A grayscale adjustment coefficient acquisition module, used to acquire the grayscale adjustment coefficient using the grayscale values ​​of all pixel points corresponding to the first pixel point; The grayscale adjustment coefficient is obtained by the following formula: Where S represents the grayscale adjustment coefficient; n represents the total number of first pixels; H i Represents the gray value of the i-th first pixel; R z H represents the grayscale median value corresponding to the m second pixel points; z Represents the grayscale intermediate value corresponding to the n first pixel points; R y represents the preset threshold value; f represents the adjustment coefficient, and the adjustment coefficient is obtained by the following formula: Where, f represents the adjustment coefficient; Hi Represents the gray value of the i-th first pixel; R z H represents the grayscale median value corresponding to the m second pixel points; z Represents the grayscale intermediate value corresponding to the n first pixel points; R p represents the grayscale average value corresponding to the m second pixel points; H p Represents the grayscale average value corresponding to the n first pixels; R max represents the maximum grayscale value corresponding to the m second pixel points; H min Indicates the minimum grayscale value corresponding to the n first pixels; A grayscale value adjustment execution module is used to adjust the grayscale values ​​of all pixel points corresponding to the second pixel point using the grayscale adjustment coefficient, wherein the adjusted grayscale value corresponding to each pixel point in the second pixel point is obtained by the following formula: Among them, R t represents the grayscale value after adjustment corresponding to each pixel in the second pixel; R0 represents the grayscale value before adjustment corresponding to each pixel in the second pixel; S represents the grayscale adjustment coefficient; R z H represents the grayscale median value corresponding to the m second pixel points; min Represents the minimum grayscale value corresponding to the n first pixels; R y represents the preset threshold value; The secondary segmentation module is used to perform secondary image segmentation on the second pixel point after the grayscale value is adjusted using a preset threshold value, and obtain a secondary segmented coronary artery CT segmentation image of the patient.

[0013] Preferably, the data processing module further includes: An image filtering unit, used for filtering the patient's coronary artery CT segmentation image; Obtaining a CT segmentation image of the patient's coronary artery; Based on the median filter, the patient's coronary artery CT segmentation image is filtered; The value of a point in the patient's coronary artery CT segmentation image is replaced by the median value of each point in a region of the point, so that the pixel with a relatively large difference in grayscale value of the surrounding pixels is changed to a value close to the surrounding pixel value, and the isolated noise points are eliminated, and the noise in the patient's coronary artery CT segmentation image is removed; A contrast enhancement unit, used for contrast enhancement of a patient's coronary artery CT segmentation image; Acquire a filtered coronary artery CT segmentation image of the patient; Performing contrast enhancement on the filtered coronary artery CT segmentation images of the patient; Highlight the detailed features in the patient's coronary artery CT segmentation images; A feature extraction unit, used for extracting features from the patient's coronary artery CT segmentation image; Acquire contrast-enhanced CT segmentation images of the patient's coronary arteries; Extract features from the contrast-enhanced coronary artery CT segmentation images of patients; Based on image cropping and magnification technology, the region of interest (ROI) is extracted from the patient's coronary artery CT segmentation image; Extract the area containing important information in the patient's coronary artery CT segmentation image, remove the irrelevant background in the patient's coronary artery CT segmentation image, and focus the analysis on the key parts of the patient's coronary artery CT segmentation image; Determine the patient's coronary artery CT characteristic image.

[0014] Preferably, the image reconstruction module comprises: An image analysis unit, used for analyzing the patient's coronary artery CT characteristic image; Acquire the patient's coronary artery CT characteristic image; Analyze the patient's coronary artery CT characteristic images; Based on 3D modeling technology, a reconstruction plan for the patient's coronary CT angiography was developed; An image reconstruction unit, used for generating a coronary artery CT vascular image; Obtain the patient's coronary artery CT angiography reconstruction plan; According to the patient's coronary artery CT vascular imaging reconstruction plan, based on the patient's coronary artery CT characteristic image, the patient's coronary artery CT vascular image is generated.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention obtains the patient's electrocardiogram signal, removes the noise in the patient's electrocardiogram signal, selects appropriate tube voltage, tube current and scanning time parameters, uses a detector and a scanner to perform real-time detection and scanning of the patient's coronary artery, collects the patient's coronary artery CT projection data, cleans the patient's coronary artery CT projection data, removes inconsistent data, invalid values ​​and missing values ​​in the patient's coronary artery CT projection data, performs threshold division on the patient's coronary artery CT projection data, and determines the patient's coronary artery CT division image, which can effectively suppress the generation of artifacts and further improve image quality.

[0016] 2. The present invention performs filtering processing on the patient's coronary artery CT segmented image to remove noise in the patient's coronary artery CT segmented image, performs contrast enhancement on the patient's coronary artery CT segmented image to highlight detail features in the patient's coronary artery CT segmented image, performs feature extraction on the patient's coronary artery CT segmented image to determine the patient's coronary artery CT characteristic image, performs analysis on the patient's coronary artery CT characteristic image, formulates a reconstruction plan for the patient's coronary artery CT vascular imaging, generates the patient's coronary artery CT vascular image according to the reconstruction plan for the patient's coronary artery CT vascular imaging and based on the patient's coronary artery CT characteristic image, thereby improving the quality of the coronary artery CT vascular imaging, providing a more accurate basis for clinical diagnosis, and enabling doctors to formulate timely and appropriate treatment plans for patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of the method for optimizing the quality of coronary CT vascular imaging of the present invention; Figure 2 It is a structural block diagram of the coronary CT vascular imaging quality optimization system of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In order to solve the problem that the existing coronary CT angiography has poor quality and cannot provide more accurate basis for clinical diagnosis, so that doctors cannot formulate timely and appropriate treatment plans for patients, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions: Embodiment 1

[0020] The coronary CT vascular imaging quality optimization system includes: a scanning acquisition module, a data processing module and an image reconstruction module.

[0021] Among them, the patient's coronary arteries are detected and scanned through the scanning acquisition module to collect the patient's coronary artery CT projection data; the patient's coronary artery CT projection data is cleaned, thresholded, filtered, contrast enhanced and feature extracted through the data processing module to determine the patient's coronary artery CT characteristic image; the patient's coronary artery CT vascular imaging reconstruction plan is formulated through the image reconstruction module to generate the coronary artery CT vascular image.

[0022] In this embodiment, the scanning acquisition module includes: A detector, used for detecting the coronary artery of the patient; a scanner to scan a patient's coronary arteries; A control unit for controlling the detector and the scanner; Among them, the patient's ECG signal is obtained, the patient's ECG signal is preprocessed, the noise in the patient's ECG signal is removed, and based on the denoised patient's ECG signal, appropriate tube voltage, tube current and scanning time parameters are selected, and the patient's coronary artery is detected and scanned in real time using a high-quality detector and scanner to collect the patient's coronary artery CT projection data.

[0023] Specifically, the control unit includes: A command control unit, used for command control of the detector and the scanner; Data control unit, used to control the detector and scanner: Among them, a control communication link is established between the detector and the scanner and the control unit: The detector and the scanner both transmit instructions to the control unit requesting the establishment of a control communication link; 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 to both the detector and the scanner agreeing to establish the control communication link; After the detector and the scanner both receive the instruction transmitted by the control unit for agreeing to establish the control communication link, the detector and the scanner both establish the control communication link with the control unit according to the instruction transmitted by the control unit for agreeing to establish the control communication link; The control unit performs command 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.

[0024] Specifically, when the patient is ready, the control unit sends a control signal to the detector and the scanner to start the detector and the scanner, and uses high-quality detectors and scanners to perform real-time detection and scanning of the patient's coronary arteries to collect the patient's coronary artery CT projection data.

[0025] In this embodiment, the data processing module includes: A data cleaning unit, used for cleaning the patient's coronary artery CT projection data; Acquire the patient's coronary artery CT projection data; Clean the patient's coronary artery CT projection data, including: Check the consistency of the patient's coronary artery CT projection data; According to the data consistency requirements, the inconsistent data in the patient's coronary artery CT projection data were removed; Invalid and missing values ​​were processed for the patients’ coronary artery CT projection data; According to the requirements of data validity and completeness, invalid values ​​and missing values ​​in the patient's coronary artery CT projection data that are useless for optimizing the quality of coronary artery CT vascular imaging are removed; To identify patient coronary CT projection data useful for optimizing the quality of coronary CT angiography.

[0026] Specifically, by cleaning the patient's coronary artery CT projection data, inconsistent data, invalid values ​​and missing values ​​in the patient's coronary artery CT projection data can be removed, and the processing accuracy and speed of subsequent patient coronary artery CT projection data can be improved.

[0027] In this embodiment, the data processing module further includes: A threshold division unit, used for performing threshold division on the patient's coronary artery CT projection data; Acquiring patient coronary artery CT projection data useful for optimizing the quality of coronary CT vascular imaging; Based on a pre-set threshold, the patient's coronary artery CT projection data is threshold-divided, the pixel points with grayscale values ​​less than or equal to the threshold in the patient's coronary artery CT projection data are removed, and the pixel points with grayscale values ​​greater than the threshold are retained to determine the patient's coronary artery CT segmentation image, which can effectively suppress the generation of artifacts and further improve image quality.

[0028] Specifically, the data processing module further includes: A first pixel point acquisition module is used to extract pixel points whose grayscale values ​​are greater than a threshold value in the CT projection data as first pixel points; A first comparison module, used for comparing the number of the first pixels with a preset pixel number threshold; A second pixel point acquisition module is used to retrieve pixels whose grayscale values ​​are less than or equal to the threshold value in the CT projection data as second pixels when the number of the first pixels is lower than a preset pixel point number threshold value; A grayscale determination parameter acquisition module is used to obtain a grayscale determination parameter using the grayscale value of the first pixel and the grayscale value of the second pixel; wherein the grayscale determination parameter is obtained by the following formula: Wherein, K represents the grayscale determination parameter; n represents the total number of first pixel points; m represents the total number of second pixel points; H i Represents the gray value of the i-th first pixel; R z H represents the grayscale median value corresponding to the m second pixel points; z Represents the grayscale intermediate value corresponding to the n first pixel points; R iRepresents the gray value corresponding to the i-th second pixel; R y represents the preset threshold value; The image grayscale adjustment module is used to use the grayscale determination parameter to determine whether image grayscale adjustment is required for the CT projection data.

[0029] The technical effect of the above technical solution is: through the first pixel acquisition module, the technical solution can accurately extract the pixel points with grayscale values ​​greater than the preset threshold in the CT projection data as the first pixel points. This screening method helps to focus on the highlight areas in the image, which usually contain more diagnostic information. The function of the first comparison module is to evaluate the number of first pixels. If the number is lower than the preset threshold, the second pixel acquisition module is used to further analyze the pixel points with grayscale 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 fuzzy image information or low contrast, it can provide more comprehensive pixel information for subsequent analysis. The grayscale judgment parameter acquisition module calculates the grayscale judgment parameter K through the above formula, which comprehensively considers the grayscale value distribution of the first pixel point and the second pixel point and their relationship with the preset threshold. This calculation method not only considers the information of the highlight area (first pixel point), but also considers the information of the dark area (second pixel point), so as to more comprehensively and accurately reflect the grayscale characteristics of the image. The image grayscale adjustment module uses the grayscale judgment parameter K to determine whether the image grayscale adjustment of the CT projection data is required. This automated decision-making process reduces the need for manual intervention and improves the efficiency and accuracy of image processing. At the same time, grayscale adjustment based on grayscale judgment parameters can ensure that the adjusted image better meets diagnostic requirements and improves image readability and diagnostic accuracy.

[0030] In summary, this technical solution achieves efficient and accurate processing of CT projection data, improves image quality and diagnostic accuracy through precise pixel screening, flexible pixel processing strategies, intelligent grayscale judgment parameter calculation and automated image grayscale adjustment decisions.

[0031] Specifically, the image grayscale adjustment module includes: A second comparison module, used for comparing the grayscale determination parameter with a preset grayscale determination parameter threshold; A first grayscale value extraction module, configured to extract 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; A second grayscale value extraction module, used to extract the grayscale values ​​of all pixel points corresponding to the first pixel point; A grayscale adjustment coefficient acquisition module, used to acquire the grayscale adjustment coefficient using the grayscale values ​​of all pixel points corresponding to the first pixel point; The grayscale adjustment coefficient is obtained by the following formula: Where S represents the grayscale adjustment coefficient; n represents the total number of first pixels; H i Represents the gray value of the i-th first pixel; R z H represents the grayscale median value corresponding to the m second pixel points; z Represents the grayscale intermediate value corresponding to the n first pixel points; R y represents the preset threshold value; 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 H represents the grayscale median value corresponding to the m second pixel points; z Represents the grayscale intermediate value corresponding to the n first pixel points; R p represents the grayscale average value corresponding to the m second pixel points; H p Represents the grayscale average value corresponding to the n first pixels; R max represents the maximum grayscale value corresponding to the m second pixel points; H min Indicates the minimum grayscale value corresponding to the n first pixels; A grayscale value adjustment execution module is used to adjust the grayscale values ​​of all pixel points corresponding to the second pixel point using the grayscale adjustment coefficient, wherein the adjusted grayscale value corresponding to each pixel point in the second pixel point is obtained by the following formula: Among them, R t represents the grayscale value after adjustment corresponding to each pixel in the second pixel; R0 represents the grayscale value before adjustment corresponding to each pixel in the second pixel; S represents the grayscale adjustment coefficient; R z H represents the grayscale median value corresponding to the m second pixel points; min Represents the minimum grayscale value corresponding to the n first pixels; R y represents the preset threshold value; The secondary segmentation module is used to perform secondary image segmentation on the second pixel point after the grayscale value is adjusted using a preset threshold value, and obtain a secondary segmented coronary artery CT segmentation image of the patient.

[0032] The technical effect of the above technical solution is: through the second comparison module, the technical solution can accurately determine whether the grayscale state of the current image needs to be adjusted, which is based on the comparison between the grayscale determination parameter and the preset threshold.

[0033] When it is determined that adjustment is required, the first grayscale value extraction module and the second grayscale value extraction module respectively extract the grayscale values ​​of the second pixel (dark area) and the first pixel (bright area) to provide a data basis for the subsequent calculation of the grayscale adjustment coefficient. The grayscale adjustment coefficient acquisition module uses the above formula to calculate the grayscale adjustment coefficient S, which comprehensively considers multiple factors such as the grayscale value distribution, grayscale intermediate value, grayscale average value, grayscale maximum value and minimum value of the first pixel and the second pixel. The introduction of the adjustment coefficient f further increases the flexibility and accuracy of grayscale adjustment, so that the grayscale adjustment can better adapt to the characteristics of different images. The grayscale value adjustment execution module uses the grayscale adjustment coefficient S to accurately adjust the grayscale value of the second pixel (dark area). The adjustment formula not only takes into account the grayscale adjustment coefficient, but also combines the original grayscale value, grayscale intermediate value and preset threshold of the second pixel, ensuring that the adjusted grayscale value meets the medical diagnosis requirements while maintaining the coherence and naturalness of the image. The secondary segmentation module uses a preset threshold to perform secondary image segmentation on the second pixel point after the grayscale value is adjusted. This step helps to further highlight key information in the image, such as the details of the patient's coronary arteries. Through secondary segmentation, a clearer and more accurate coronary artery CT segmentation image can be obtained, providing doctors with more valuable diagnostic information. This technical solution significantly improves the clarity and contrast of coronary artery CT images through fine grayscale adjustment and optimized image segmentation. This helps doctors more accurately identify coronary artery lesions and improve the accuracy and efficiency of diagnosis.

[0034] In summary, this technical solution achieves efficient and accurate processing of coronary CT images and improves the accuracy and efficiency of diagnosis through sophisticated grayscale adjustment strategies, intelligent grayscale adjustment coefficient calculations, precise grayscale value adjustment execution, and optimized image segmentation.

[0035] In this embodiment, the data processing module further includes: An image filtering unit, used for filtering the patient's coronary artery CT segmentation image; Obtaining a CT segmentation image of the patient's coronary artery; Based on the median filter, the patient's coronary artery CT segmentation image is filtered; The value of a point in the patient's coronary artery CT segmentation image is replaced by the median value of each point in a region of the point, so that the pixel with a relatively large difference in grayscale value of the surrounding pixels is changed to a value close to the surrounding pixel value, thereby eliminating isolated noise points, removing noise in the patient's coronary artery CT segmentation image, eliminating noise interference, and improving image quality; A contrast enhancement unit, used for contrast enhancement of a patient's coronary artery CT segmentation image; Acquire a filtered coronary artery CT segmentation image of the patient; Performing contrast enhancement on the filtered coronary artery CT segmentation images of the patient; Highlighting the detailed features in the patient's coronary artery CT segmentation image can reduce image blur; A feature extraction unit, used for extracting features from the patient's coronary artery CT segmentation image; Acquire contrast-enhanced CT segmentation images of the patient's coronary arteries; Extract features from the contrast-enhanced coronary artery CT segmentation images of patients; Based on image cropping and magnification technology, the region of interest (ROI) is extracted from the patient's coronary artery CT segmentation image; Extract the area containing important information in the patient's coronary artery CT segmentation image, remove the irrelevant background in the patient's coronary artery CT segmentation image, and focus the analysis on the key parts of the patient's coronary artery CT segmentation image; Determine the patient's coronary artery CT characteristic image.

[0036] Specifically, by extracting features from the patient's coronary artery CT segmentation image, the patient's coronary artery CT characteristic image is determined, which facilitates the subsequent formulation of the patient's coronary artery CT vascular imaging reconstruction plan and generates the patient's coronary artery CT vascular image.

[0037] In this embodiment, the image reconstruction module includes: An image analysis unit, used for analyzing the patient's coronary artery CT characteristic image; Acquire the patient's coronary artery CT characteristic image; Analyze the patient's coronary artery CT characteristic images; Based on 3D modeling technology, a reconstruction plan for the patient's coronary CT angiography was developed; An image reconstruction unit, used for generating a coronary artery CT vascular image; Obtain the patient's coronary artery CT angiography reconstruction plan; According to the patient's coronary artery CT vascular imaging reconstruction plan, based on the patient's coronary artery CT characteristic image, the patient's coronary artery CT vascular image is generated. Embodiment 2

[0038] In order to better demonstrate the coronary CT vascular imaging quality optimization process, this embodiment now provides a coronary CT vascular imaging quality optimization method, which is implemented based on the coronary CT vascular imaging quality optimization system as described above, and includes the following steps: S1: Obtain the patient's ECG signal, remove the noise in the patient's ECG signal, select appropriate tube voltage, tube current and scanning time parameters, use the detector and scanner to perform real-time detection and scanning of the patient's coronary artery, and collect the patient's coronary artery CT projection data; S2: cleaning the patient's coronary artery CT projection data, removing inconsistent data, invalid values ​​and missing values ​​in the patient's coronary artery CT projection data, performing threshold division on the patient's coronary artery CT projection data, and determining the patient's coronary artery CT division image; S3: filtering the patient's coronary artery CT segmented image to remove noise in the patient's coronary artery CT segmented image, contrast enhancing the patient's coronary artery CT segmented image to highlight detail features in the patient's coronary artery CT segmented image, extracting features from the patient's coronary artery CT segmented image, and determining the patient's coronary artery CT feature image; S4: Analyze the patient's coronary artery CT characteristic image, formulate a reconstruction plan for the patient's coronary artery CT vascular imaging, and generate the patient's coronary artery CT vascular image based on the patient's coronary artery CT characteristic image according to the reconstruction plan for the patient's coronary artery CT vascular imaging.

[0039] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0040] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that 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 vascular imaging, characterized in that: The steps include: S1: Obtain the patient's ECG signal, remove the noise in the patient's ECG signal, select appropriate tube voltage, tube current and scanning time parameters, use the detector and scanner to perform real-time detection and scanning of the patient's coronary artery, and collect the patient's coronary artery CT projection data; S2: cleaning the patient's coronary artery CT projection data, removing inconsistent data, invalid values ​​and missing values ​​in the patient's coronary artery CT projection data, performing threshold division on the patient's coronary artery CT projection data, and determining the patient's coronary artery CT division image; S3: filtering the patient's coronary artery CT segmented image to remove noise in the patient's coronary artery CT segmented image, contrast enhancing the patient's coronary artery CT segmented image to highlight detail features in the patient's coronary artery CT segmented image, extracting features from the patient's coronary artery CT segmented image, and determining the patient's coronary artery CT feature image; S4: Analyze the patient's coronary artery CT characteristic image, formulate a reconstruction plan for the patient's coronary artery CT vascular imaging, and generate the patient's coronary artery CT vascular image based on the patient's coronary artery CT characteristic image according to the reconstruction plan for the patient's coronary artery CT vascular imaging.

2. A coronary CT vascular imaging quality optimization system, used to implement the coronary CT vascular imaging quality optimization method as claimed in claim 1, characterized in that: include: A scanning acquisition module is used to detect and scan the patient's coronary arteries and collect the patient's coronary artery CT projection data; A data processing module is used to clean, threshold, filter, contrast enhance and feature extract the patient's coronary artery CT projection data to determine the patient's coronary artery CT feature image; The image reconstruction module is used to formulate a reconstruction plan for the patient's coronary CT vascular imaging and generate a coronary CT vascular image.

3. The coronary CT vascular imaging quality optimization system according to claim 2, characterized in that: The scanning acquisition module comprises: A detector, used for detecting the coronary artery of the patient; a scanner to scan a patient's coronary arteries; A control unit for controlling the detector and the scanner; Among them, the patient's ECG signal is obtained, the patient's ECG signal is preprocessed, the noise in the patient's ECG signal is removed, and based on the denoised patient's ECG signal, appropriate tube voltage, tube current and scanning time parameters are selected, and the patient's coronary artery is detected and scanned in real time using a high-quality detector and scanner to collect the patient's coronary artery CT projection data.

4. The coronary CT vascular imaging quality optimization system according to claim 3, characterized in that: Control unit, comprising: A command control unit, used for command control of the detector and the scanner; Data control unit, used to control the detector and scanner: Among them, a control communication link is established between the detector and the scanner and the control unit: The detector and the scanner both transmit instructions to the control unit requesting the establishment of a control communication link; 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 to both the detector and the scanner agreeing to establish the control communication link; After the detector and the scanner both receive the instruction transmitted by the control unit for agreeing to establish the control communication link, the detector and the scanner both establish the control communication link with the control unit according to the instruction transmitted by the control unit for agreeing to establish the control communication link; The control unit performs command 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.

5. The coronary CT vascular imaging quality optimization system according to claim 4, characterized in that: The data processing module comprises: A data cleaning unit, used for cleaning the patient's coronary artery CT projection data; Acquire the patient's coronary artery CT projection data; Clean the patient's coronary artery CT projection data, including: Check the consistency of the patient's coronary artery CT projection data; According to the data consistency requirements, the inconsistent data in the patient's coronary artery CT projection data were removed; Invalid and missing values ​​were processed for the patients’ coronary artery CT projection data; According to the requirements of data validity and completeness, invalid values ​​and missing values ​​in the patient's coronary artery CT projection data that are useless for optimizing the quality of coronary artery CT vascular imaging are removed; To identify patient coronary CT projection data useful for optimizing the quality of coronary CT angiography.

6. The coronary CT vascular imaging quality optimization system according to claim 5, characterized in that: The data processing module further includes: A threshold division unit, used for performing threshold division on the patient's coronary artery CT projection data; Acquiring patient coronary artery CT projection data useful for optimizing the quality of coronary CT vascular imaging; Based on a preset threshold, the patient's coronary artery CT projection data is threshold-divided, the pixel points with grayscale values ​​less than or equal to the threshold in the patient's coronary artery CT projection data are removed, and the pixel points with grayscale values ​​greater than the threshold are retained to determine the patient's coronary artery CT segmentation image.

7. The coronary CT vascular imaging quality optimization system according to claim 6, characterized in that: The data processing module further includes: A first pixel point acquisition module is used to extract pixel points whose grayscale values ​​are greater than a threshold value in the CT projection data as first pixel points; A first comparison module, used for comparing the number of the first pixels with a preset pixel number threshold; A second pixel point acquisition module is used to retrieve pixels whose grayscale values ​​are less than or equal to the threshold value in the CT projection data as second pixels when the number of the first pixels is lower than a preset pixel point number threshold value; A grayscale determination parameter acquisition module is used to obtain a grayscale determination parameter using the grayscale value of the first pixel and the grayscale value of the second pixel; wherein the grayscale determination parameter is obtained by the following formula: Wherein, K represents the grayscale determination parameter; n represents the total number of first pixel points; m represents the total number of second pixel points; H i Represents the gray value of the i-th first pixel; R z H represents the grayscale median value corresponding to the m second pixel points; z Represents the grayscale intermediate value corresponding to the n first pixel points; R i Represents the gray value corresponding to the i-th second pixel; R y represents the preset threshold value; The image grayscale adjustment module is used to use the grayscale determination parameter to determine whether image grayscale adjustment is required for the CT projection data.

8. The coronary CT vascular imaging quality optimization system according to claim 7, characterized in that: The image grayscale adjustment module comprises: A second comparison module, used for comparing the grayscale determination parameter with a preset grayscale determination parameter threshold; A first grayscale value extraction module, configured to extract 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; A second grayscale value extraction module, used to extract the grayscale values ​​of all pixel points corresponding to the first pixel point; A grayscale adjustment coefficient acquisition module, used to acquire the grayscale adjustment coefficient using the grayscale values ​​of all pixel points corresponding to the first pixel point; The grayscale adjustment coefficient is obtained by the following formula: Where S represents the grayscale adjustment coefficient; n represents the total number of first pixels; H i Represents the gray value of the i-th first pixel; R z H represents the grayscale median value corresponding to the m second pixel points; z Represents the grayscale intermediate value corresponding to the n first pixel points; R y represents the preset threshold value; 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 H represents the grayscale median value corresponding to the m second pixel points; z Represents the grayscale intermediate value corresponding to the n first pixel points; R p represents the grayscale average value corresponding to the m second pixel points; H p Represents the grayscale average value corresponding to the n first pixel points; R max represents the maximum grayscale value corresponding to the m second pixel points; H min Indicates the minimum grayscale value corresponding to the n first pixels; A grayscale value adjustment execution module is used to adjust the grayscale values ​​of all pixel points corresponding to the second pixel point using the grayscale adjustment coefficient, wherein the adjusted grayscale value corresponding to each pixel point in the second pixel point is obtained by the following formula: Among them, R t represents the grayscale value after adjustment corresponding to each pixel in the second pixel; R0 represents the grayscale value before adjustment corresponding to each pixel in the second pixel; S represents the grayscale adjustment coefficient; R z H represents the grayscale median value corresponding to the m second pixel points; min Represents the minimum grayscale value corresponding to the n first pixels; R y represents the preset threshold; The secondary segmentation module is used to perform secondary image segmentation on the second pixel point after the grayscale value is adjusted using a preset threshold value, and obtain a secondary segmented coronary artery CT segmentation image of the patient.

9. The coronary CT vascular imaging quality optimization system according to claim 6, characterized in that: The data processing module further includes: An image filtering unit, used for filtering the patient's coronary artery CT segmentation image; Obtaining a CT segmentation image of the patient's coronary artery; Based on the median filter, the patient's coronary artery CT segmentation image is filtered; The value of a point in the patient's coronary artery CT segmentation image is replaced by the median value of each point in a region of the point, so that the pixel with a relatively large difference in grayscale value of the surrounding pixels is changed to a value close to the surrounding pixel value, and the isolated noise points are eliminated, and the noise in the patient's coronary artery CT segmentation image is removed; A contrast enhancement unit, used for contrast enhancement of a patient's coronary artery CT segmentation image; Acquire a filtered coronary artery CT segmentation image of the patient; Performing contrast enhancement on the filtered coronary artery CT segmentation images of the patient; Highlight the detailed features in the patient's coronary artery CT segmentation images; A feature extraction unit, used for extracting features from the patient's coronary artery CT segmentation image; Acquire contrast-enhanced CT segmentation images of the patient's coronary arteries; Extract features from the contrast-enhanced coronary artery CT segmentation images of patients; Based on image cropping and magnification technology, the region of interest (ROI) is extracted from the patient's coronary artery CT segmentation image; Extract the area containing important information in the patient's coronary artery CT segmentation image, remove the irrelevant background in the patient's coronary artery CT segmentation image, and focus the analysis on the key parts of the patient's coronary artery CT segmentation image; Determine the patient's coronary artery CT characteristic image.

10. The coronary CT vascular imaging quality optimization system according to claim 9, characterized in that: The image reconstruction module comprises: An image analysis unit, used for analyzing the patient's coronary artery CT characteristic image; Acquire the patient's coronary artery CT characteristic image; Analyze the patient's coronary artery CT characteristic images; Based on 3D modeling technology, a reconstruction plan for the patient's coronary CT angiography was developed; An image reconstruction unit, used for generating a coronary artery CT vascular image; Obtain the patient's coronary artery CT angiography reconstruction plan; According to the patient's coronary artery CT vascular imaging reconstruction plan, based on the patient's coronary artery CT characteristic image, the patient's coronary artery CT vascular image is generated.

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