Method and device for identifying plaque in blood vessel

Abstract

A method and device for identifying plaque in a blood vessel. The method comprises: when there is plaque in a target blood vessel to be identified, determining raw plaque medical image data corresponding to each plaque (S101); for each branch blood vessel, on the basis of pixel values and a region type division rule in the raw plaque medical image data, dividing each plaque region, and determining at least one plaque sub-region (S102); for each target branch blood vessel having target plaque sub-regions of any specified region type, on the basis of a high-risk plaque evaluation rule and corresponding image parameters, identifying a target plaque sub-region in the target branch blood vessel, and determining whether the target branch blood vessel has high-risk plaque (S103); and if yes, outputting a high-risk plaque identification result of the target branch blood vessel, the plaque identification result comprising the type of the high-risk plaque, the position of the plaque, and a blood vessel name (S104).

Description

Method and device for identifying plaque in blood vessels

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on October 24, 2023, with application number 202311385251.1, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of medical technology, for example, to a method and device for identifying plaques in blood vessels. Background Art

[0003] With the rapid development of society and the economy, vascular disease has become a highly discussed topic. High-risk / vulnerable plaques within coronary atherosclerotic plaques often occur in the early stages of coronary plaque formation and are a major risk factor for acute cardiovascular events. Therefore, improving the treatment and prevention of vascular disease is of paramount importance. However, both treatment and prevention rely heavily on the study of vascular morphology. Therefore, automated detection of vascular plaques holds significant research, clinical, and practical significance.

[0004] Most related technical studies use traditional machine learning methods to detect high-risk plaques in coronary arteries. This involves first manually designing features by experienced experts, and then feeding the extracted features into a machine learning classifier to predict the plaque type. However, this process relies heavily on the operator's experience and extensive expertise, and is time-consuming and labor-intensive. However, given the diversity of features in coronary plaque samples, it is difficult for machine learning methods to design specific features for high-risk plaques when designing features. These methods rely heavily on the operator's experience and extensive expertise, which is time-consuming and labor-intensive. Furthermore, due to the characteristics of high-risk coronary plaques, a single plaque typically spans multiple coronary CT angiography (CCTA) scans. Deep learning training methods that use a single image for a single label are subject to errors and are unsuitable for high-risk plaque detection tasks.

[0005] Therefore, there are still certain limitations in using traditional machine learning methods to detect high-risk coronary artery plaques.

[0006] Summary of the Invention

[0007] The present application provides a method and device for identifying plaques in blood vessels, which can effectively identify high-risk plaques in blood vessels.

[0008] The present embodiment provides a method for identifying plaque in a blood vessel, the method comprising:

[0009] In response to the presence of plaque in the target blood vessel to be identified, determining original plaque medical image data corresponding to each plaque based on an original total medical image of the target blood vessel, wherein the target blood vessel includes at least one branch blood vessel;

[0010] For each branch vessel, the plaque region corresponding to each plaque in the branch vessel is divided according to the pixel values ​​and region type division rules in the original plaque medical imaging data, and at least one plaque subregion and the region type of each plaque subregion are determined;

[0011] For each target branch vessel containing a target plaque sub-region of any specified region type, identifying the plaque region containing the target plaque sub-region in the target branch vessel according to high-risk plaque assessment rules and corresponding imaging parameters to determine whether the target branch vessel contains a high-risk plaque;

[0012] In response to determining that a high-risk plaque exists in the target branch vessel, a high-risk plaque identification result of the target branch vessel is output; the plaque identification result includes the high-risk plaque type, the plaque location, and the vessel name of the target branch vessel.

[0013] In some embodiments, the presence of plaque in the target blood vessel to be identified is determined by:

[0014] obtaining a three-dimensional reconstructed vascular model of the target blood vessel;

[0015] identifying, based on a morphological processing method, whether there is a mutation region in the three-dimensional reconstructed vascular model of the target blood vessel;

[0016] In response to identifying the presence of a mutation region in the three-dimensional reconstructed blood vessel model of the target blood vessel, it is determined that a plaque exists in the target blood vessel.

[0017] In some embodiments, the three-dimensional reconstructed vascular model of the target blood vessel is constructed by:

[0018] performing centerline extraction processing on the original total medical image of the target blood vessel to determine the centerline of the target blood vessel;

[0019] Performing contour recognition based on the centerline of the target blood vessel and the original total medical image to determine the intima contour and adventitia contour of the target blood vessel;

[0020] performing lofting processing on the intima contour and the adventitia contour of the target blood vessel respectively to obtain an intima curved surface model and an adventitia curved surface model of the target blood vessel;

[0021] Based on the intima curved surface model and the adventitia curved surface model of the target blood vessel, volume filling processing is performed to obtain a three-dimensional reconstructed blood vessel model of the target blood vessel; the three-dimensional reconstructed blood vessel model of the target blood vessel includes a three-dimensional intima model and a three-dimensional adventitia model.

[0022] In some embodiments, determining the original plaque medical image data corresponding to each plaque includes:

[0023] Determining a three-dimensional reconstruction result of all plaques in the target blood vessel using a result obtained by subtracting a three-dimensional model of the intima from a three-dimensional model of the adventitia of the target blood vessel;

[0024] According to the three-dimensional reconstruction results of all plaques, medical images at corresponding positions are extracted from the original total medical image of the target blood vessel to determine the original plaque medical image data corresponding to each plaque.

[0025] In some embodiments, the region type classification rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.

[0026] In some embodiments, for each target branch vessel having a target plaque sub-region of any specified region type, identifying the plaque region in the target branch vessel having the target plaque sub-region according to high-risk plaque assessment rules and corresponding imaging parameters, and determining whether the target branch vessel has a high-risk plaque, includes:

[0027] For each plaque region in each target branch vessel where a target plaque sub-region exists, determining an identification order of high-risk plaques according to the region type of the target plaque sub-region in the plaque region;

[0028] According to the determined identification order of high-risk plaques, the imaging parameters required for identifying each high-risk plaque are sequentially obtained;

[0029] Identify the high-risk plaque according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque, until all high-risk plaques in the identification sequence are identified, and obtain the high-risk plaque identification result corresponding to the plaque area;

[0030] In response to a high-risk plaque identification result corresponding to any plaque region in the target branch vessel indicating the presence of a high-risk plaque, it is determined that the target branch vessel has a high-risk plaque.

[0031] In some embodiments, the plaque location includes the plaque location of each high-risk plaque, and the plaque location of each high-risk plaque is determined by:

[0032] In response to identifying the presence of a high-risk plaque in the target branch vessel, determining the plaque region to which the high-risk plaque belongs;

[0033] According to the determined plaque area and the original total medical image, a shortest distance mapping processing method is adopted to determine the plaque position of the high-risk plaque.

[0034] The present application also provides a device for identifying plaque in a blood vessel, the device comprising:

[0035] A first determining module is configured to determine original plaque medical image data corresponding to each plaque based on an original total medical image of the target vessel in response to the presence of a plaque in the target vessel to be identified; the target vessel includes at least one branch vessel;

[0036] a segmentation module configured to segment the plaque region corresponding to each plaque in each branch vessel according to the pixel values ​​and region type segmentation rules in the original plaque medical imaging data, and determine at least one plaque subregion and a region type for each plaque subregion;

[0037] an identification module configured to, for each target branch vessel having a target plaque sub-region of any specified region type, identify the plaque region in the target branch vessel having the target plaque sub-region based on a high-risk plaque assessment rule and corresponding imaging parameters, and determine whether the target branch vessel has a high-risk plaque;

[0038] The output module is configured to output a high-risk plaque identification result of the target branch vessel in response to determining that a high-risk plaque exists in the target branch vessel; the plaque identification result includes the high-risk plaque type, the plaque location, and the vessel name of the target branch vessel.

[0039] In some embodiments, the identification device further includes a second determination module, and the second determination module is configured to:

[0040] obtaining a three-dimensional reconstructed vascular model of the target blood vessel;

[0041] identifying, based on a morphological processing method, whether there is a mutation region in the three-dimensional reconstructed vascular model of the target blood vessel;

[0042] In response to identifying the presence of a mutation region in the three-dimensional reconstructed blood vessel model of the target blood vessel, it is determined that a plaque exists in the target blood vessel.

[0043] In some embodiments, the identification device further includes a building module, and the building module is configured to:

[0044] Performing centerline extraction processing on the original total medical image of the target blood vessel to determine the centerline of the target blood vessel;

[0045] Performing contour recognition based on the centerline of the target blood vessel and the original total medical image to determine the intima contour and adventitia contour of the target blood vessel;

[0046] performing lofting processing on the intima contour and the adventitia contour of the target blood vessel respectively to obtain an intima curved surface model and an adventitia curved surface model of the target blood vessel;

[0047] Based on the intima curved surface model and the adventitia curved surface model of the target blood vessel, volume filling processing is performed to obtain a three-dimensional reconstructed blood vessel model of the target blood vessel; the three-dimensional reconstructed blood vessel model of the target blood vessel includes a three-dimensional intima model and a three-dimensional adventitia model.

[0048] In some embodiments, when the first determining module is configured to determine the original plaque medical image data corresponding to each plaque in the following manner, the first determining module is configured to:

[0049] Determining a three-dimensional reconstruction result of all plaques in the target blood vessel using a result obtained by subtracting a three-dimensional model of the intima from a three-dimensional model of the adventitia of the target blood vessel;

[0050] According to the three-dimensional reconstruction results of all plaques, medical images at corresponding positions are extracted from the original total medical image of the target blood vessel to determine the original plaque medical image data corresponding to each plaque.

[0051] In some embodiments, the region type classification rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.

[0052] In some embodiments, when the identification module is configured to identify, for each target branch vessel containing a target plaque sub-region of any specified region type, based on high-risk plaque assessment rules and corresponding imaging parameters, the plaque region containing the target plaque sub-region in the target branch vessel, and determine whether the target branch vessel contains a high-risk plaque, the identification module is configured to:

[0053] For each plaque region in each target branch vessel where a target plaque sub-region exists, determining an identification order of high-risk plaques according to the region type of the target plaque sub-region in the plaque region;

[0054] According to the determined identification order of high-risk plaques, the imaging parameters required for identifying each high-risk plaque are sequentially obtained;

[0055] Identify the high-risk plaque according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque, until all high-risk plaques in the identification sequence are identified, and obtain the high-risk plaque identification result corresponding to the plaque area;

[0056] In response to a high-risk plaque identification result corresponding to any plaque region in the target branch vessel indicating the presence of a high-risk plaque, it is determined that the target branch vessel has a high-risk plaque.

[0057] In some embodiments, the identification module is further configured to:

[0058] In response to identifying the presence of a high-risk plaque in the target branch vessel, determining the plaque region to which the high-risk plaque belongs;

[0059] According to the determined plaque area and the original total medical image, a shortest distance mapping processing method is adopted to determine the plaque position of the high-risk plaque.

[0060] An embodiment of the present application also provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the identification method as described above is performed.

[0061] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the identification method as described above is executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The following are some drawings required for use in the embodiments. The following drawings only illustrate some of the embodiments related to this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative work.

[0063] FIG1 is a flow chart of a method for identifying plaque in a blood vessel provided in an embodiment of the present application;

[0064] FIG2 is a schematic structural diagram of a blood vessel centerline provided in an embodiment of the present application;

[0065] FIG3 is a schematic structural diagram of a vascular adventitia outline provided in an embodiment of the present application;

[0066] FIG4 is a schematic diagram of the structure of a branched blood vessel with plaque provided by the present application;

[0067] FIG5 is a schematic diagram of the data results of the positive reconstruction high-risk plaque identification process provided by this application;

[0068] FIG6 is a schematic diagram of a structure of a device for identifying plaque in a blood vessel provided in an embodiment of the present application;

[0069] FIG7 is a second schematic structural diagram of a device for identifying plaque in a blood vessel provided in an embodiment of the present application;

[0070] FIG8 is a schematic structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0071] The following will describe the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. The described embodiments are some embodiments related to the present application. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work falls within the scope of protection of this application.

[0072] The embodiments of the present application provide a method and apparatus for identifying plaques in blood vessels, which can effectively identify high-risk plaques in blood vessels and improve the speed and accuracy of high-risk plaque identification.

[0073] Please refer to Figure 1, which is a flow chart of a method for identifying plaque in a blood vessel provided by an embodiment of the present application. As shown in Figure 1, the identification method provided by the embodiment of the present application includes:

[0074] S101 : When plaque exists in a target blood vessel to be identified, original plaque medical image data corresponding to each plaque is determined based on an original total medical image of the target blood vessel.

[0075] The target vessel includes at least one branch vessel. For example, when the target vessel is a coronary artery, the target vessel includes branches such as the left main artery, the left anterior descending artery, the left circumflex artery, and the right coronary artery, or further includes branches such as the acute marginal branch, the sinoatrial node branch, the atrioventricular node branch, the posterior descending artery, and the posterior left ventricular branch.

[0076] The types of branch vessels included in the target vessel may be set in advance by relevant personnel.

[0077] In one embodiment provided in the present application, the presence of plaque in the target blood vessel to be identified is determined by the following steps:

[0078] S201: Acquire a three-dimensional reconstructed blood vessel model of the target blood vessel.

[0079] S202: Identify whether there is a mutation region in the three-dimensional reconstructed blood vessel model of the target blood vessel according to a morphological processing method.

[0080] S203 : In response to identifying a mutation region in the three-dimensional reconstructed blood vessel model of the target blood vessel, determine that a plaque exists in the target blood vessel.

[0081] Regarding step S201, in one embodiment provided in the present application, a three-dimensional reconstructed vascular model of the target blood vessel is constructed by the following steps:

[0082] S301 : Perform centerline extraction processing on the original total medical image of the target blood vessel to determine the centerline of the target blood vessel.

[0083] S302: Perform contour recognition based on the centerline of the target blood vessel and the original total medical image to determine the intima contour and adventitia contour of the target blood vessel.

[0084] S303 , performing lofting processing on the intima contour and the adventitia contour of the target blood vessel respectively to obtain an intima curved surface model and an adventitia curved surface model of the target blood vessel.

[0085] S304: Perform volume filling processing based on the intima curved surface model and the adventitia curved surface model of the target blood vessel to obtain a three-dimensional reconstructed blood vessel model of the target blood vessel; the three-dimensional reconstructed blood vessel model of the target blood vessel includes a three-dimensional intima model and a three-dimensional adventitia model.

[0086] For example, regarding step S301, please refer to FIG. 2 , which is a schematic diagram illustrating a blood vessel centerline structure according to an embodiment of the present application. As shown in FIG. 2 , during centerline extraction, central target points at multiple locations of the target vessel can be determined to obtain multiple central target points. These multiple central target points are then sequentially connected based on the target vessel's trajectory and structural characteristics to determine the target vessel's centerline.

[0087] Regarding step S302 , it should be noted that the blood vessel wall tissue includes an intima and an adventitia, and the inner edge of the intima and the outer edge of the adventitia are used as the intima contour and the adventitia contour, respectively.

[0088] The characteristics of the contour are: a closed curve formed by a series of spatial points on the same spatial plane, and the normal vector of the contour plane is parallel to the tangent direction of the center line of the point. For example, please refer to Figure 3, which is a structural schematic diagram of a vascular adventitia contour provided in an embodiment of the present application. As shown in Figure 3, the adventitia contour of the target blood vessel determined here is actually composed of contour lines of multiple positions of the target blood vessel arranged in sequence. Among them, each central target point on the center line of the target blood vessel corresponds to a contour (contour curve).

[0089] Regarding step S303, the lofting process is a method of generating a smooth and continuous shape by gradually transitioning or interpolating multiple curves or surfaces with different cross sections.

[0090] The lofting process includes steps such as contour preparation, interpolation, smoothing and adjustment, connection and supplementation, and shape output. When performing interpolation: adjacent contours are interpolated to generate an intermediate shape (contour), which can be achieved by interpolating contour points, adjusting control points, parameterizing curves, and other methods. When performing smoothing and adjustment: during the interpolation and fusion process, problems such as uneven shapes and unnatural corner transitions may occur. In this step, curve fitting, smoothing algorithms and other techniques can be used to adjust the shape to make it smoother and more continuous in the transition area. When performing connection and supplementation: during the lofting process, some discontinuous connections may appear, or the shape may need to be supplemented in some places. This can be solved by curve and surface connection algorithms, or additional interpolation operations.

[0091] With respect to step S304, the performing of volume filling processing on the intimal curved surface model and the adventitia curved surface model of the target blood vessel to obtain a three-dimensional reconstructed blood vessel model of the target blood vessel includes: performing volume filling processing on the intimal curved surface model and the adventitia curved surface model of the target blood vessel respectively to obtain a three-dimensional intimal model and a three-dimensional adventitia model of the target blood vessel, and determining the three-dimensional reconstructed blood vessel model of the target blood vessel based on the three-dimensional intimal model and the three-dimensional adventitia model of the target blood vessel.

[0092] Regarding the determination of the original plaque medical image data corresponding to each plaque in step S101, in one embodiment provided in the present application, the original plaque medical image data corresponding to each plaque is determined by the following steps:

[0093] S1011. Determine a three-dimensional reconstruction result of all plaques in the target blood vessel using a result obtained by subtracting the three-dimensional model of the intima from the three-dimensional model of the adventitia of the target blood vessel.

[0094] S1012: Extract medical images at corresponding positions from the original total medical image of the target blood vessel according to the three-dimensional reconstruction results of all plaques, and determine original plaque medical image data corresponding to each plaque.

[0095] Regarding step S1011, it should be noted that the intima and adventitia of a normal plaque-free blood vessel are substantially overlapped.

[0096] When determining the three-dimensional reconstruction results of all plaques in the target blood vessel, the position of each plaque in the target blood vessel is also determined, so that step S1012 can be executed.

[0097] S102. For each branch vessel, divide the plaque region corresponding to each plaque in the branch vessel according to the pixel values ​​and region type division rules in the original plaque medical imaging data, and determine at least one plaque sub-region and the region type of each plaque sub-region.

[0098] In step S102, the plaque area in the blood vessel is divided based on the blood vessel.

[0099] The pixel values ​​in the original plaque medical image data may be, for example, computed tomography (CT) values, where the unit of the CT value is Hounsfield unit (HU).

[0100] The region type division rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.

[0101] For example, the region classification rule may be: for plaque areas, areas with CT values ​​≥ 350HU are determined as calcified plaque areas, and areas with CT values ​​< 350HU are determined as non-calcified plaque areas. The non-calcified plaque areas may further include: areas with CT values ​​in the range of -30 to 30HU are determined as non-calcified plaque areas with necrotic cores, areas with CT values ​​in the range of 31 to 130HU are determined as non-calcified plaque areas with fibrofatty tissue, and areas with CT values ​​in the range of 131 to 350HU are determined as non-calcified plaque areas with fibrous tissue.

[0102] In this way, the corresponding area type can be determined for each divided patch sub-area.

[0103] For example, please refer to Figure 4, which is a schematic diagram of the structure of a branched blood vessel with plaque provided by the present application. The plaque area can be divided to determine at least one plaque sub-area.

[0104] S103. For each target branch vessel having a target plaque sub-region of any specified region type, identify each target plaque sub-region in the target branch vessel according to high-risk plaque assessment rules and corresponding imaging parameters to determine whether the target branch vessel has a high-risk plaque.

[0105] High-risk plaques are determined based on coronary CT image interpretation and reporting guidelines, such as the Society of Cardiovascular Computed Tomography (SCCT) guidelines. The SCCT guidelines define high-risk plaques as including positive remodeling, "napkin ring" sign, punctate calcification, and low-density plaques.

[0106] Positive remodeling refers to the ratio of the maximum vessel diameter (including plaque and lumen) of the lesion segment to the normal average lumen diameter proximal and distal to the plaque being ≥1.1.

[0107] Low attenuation plaque: refers to a plaque with a density greater than 1 mm2. 2 ) with a CT value <30HU in the area, and low-density plaques were closely associated with severe macrophage infiltration and a large lipid necrotic core (>40% of the total plaque volume).

[0108] Spotty calcification refers to a high-density lesion with a long diameter of <3 mm and an average density of >130 HU in any plane within a non-calcified plaque, with the long diameter of the calcification being less than 1.5 times the vessel diameter and the short diameter of the calcification being less than 2 / 3 of the vessel diameter.

[0109] "Napkin-ring" sign: refers to the ring-shaped slightly higher density sign at the edge of the low-density plaque.

[0110] The selection of designated area types is based on the high-risk plaque assessment rules. The four high-risk plaque types mentioned above (positive remodeling, napkin ring sign, punctate calcification, and low-density plaque) are all non-calcified areas. Therefore, the designated area types include: non-calcified plaque areas with a necrotic core, non-calcified plaque areas with fibrofatty cores, and non-calcified plaque areas with fibrous cores.

[0111] The corresponding image parameters are determined according to the assessment indicators required for identifying each type of high-risk plaque in the high-risk plaque assessment rules.

[0112] For example, quantitative analysis indicators used to assess plaque characteristics also include plaque-induced luminal stenosis, plaque size, plaque length, total plaque volume, calcified plaque volume, non-calcified plaque volume, minimum luminal area, remodeling index, and plaque burden. Plaque size is determined based on parameters such as total plaque burden and / or non-calcified plaque burden (volume ratio) and / or calcified plaque burden (volume ratio) and / or plaque length. Determining plaque-induced luminal stenosis requires obtaining parameters such as plaque cross-sectional area and / or lumen cross-sectional area.

[0113] The step S103 of identifying each target plaque sub-region in the target branch vessel may be, for example, identifying each plaque region in the target branch vessel where a target plaque sub-region exists.

[0114] When a high-risk plaque is identified in any plaque region, it is determined that the target branch vessel has a high-risk plaque.

[0115] In one embodiment provided herein, for each target branch vessel having a target plaque sub-region of any specified region type, identifying the plaque region in the target branch vessel having the target plaque sub-region based on high-risk plaque assessment rules and corresponding imaging parameters to determine whether the target branch vessel has a high-risk plaque includes:

[0116] S1031 . For each plaque region having a target plaque sub-region in each target branch vessel, determine an identification order of high-risk plaques according to the region type of the target plaque sub-region in the plaque region.

[0117] S1032. According to the determined recognition order of the high-risk plaques, sequentially obtain the imaging parameters required for identifying each high-risk plaque.

[0118] S1033 , identifying the high-risk plaque according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque, until all high-risk plaques in the identification sequence are identified, and obtaining a high-risk plaque identification result corresponding to the plaque area.

[0119] S1034: When the high-risk plaque identification result corresponding to any plaque region in the target branch vessel indicates the presence of a high-risk plaque, determine that the target branch vessel has a high-risk plaque.

[0120] For example, regarding step S1031, assuming that the target plaque sub-region within a plaque region is a non-calcified plaque region with a necrotic core, the corresponding high-risk plaque identification order may be: low-density plaque → "napkin ring" sign → positive reconstruction. If the target plaque sub-region within a plaque region is a non-calcified plaque region with fibrofatty tissue, the corresponding high-risk plaque identification order may be: punctate calcification → positive reconstruction.

[0121] By determining the recognition order of high-risk plaques, the recognition of unnecessary types of high-risk plaques can be reduced, thereby improving the recognition speed. Moreover, by performing recognition in sequence according to the determined recognition order, the accuracy of high-risk plaque recognition can also be improved.

[0122] Regarding step S1033 , the high-risk plaque is identified according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque. Four high-risk plaque identification methods are provided below.

[0123] Method for identifying low-density plaques: If the target plaque sub-region within the plaque region is a non-calcified plaque region with a necrotic core, calculate the CT mean of the target plaque sub-region; if the CT mean is less than 30HU, continue to measure the volume of the target plaque sub-region; if the CT mean is not less than 30HU, end the low-density plaque identification; where the volume of the target plaque sub-region = the number of pixels in the necrotic core × the physical resolution of the pixel (x_spacing (horizontal pixel spacing) × y_spacing (vertical pixel spacing) × interval (interval)); if the volume of the target plaque sub-region is greater than 1 cubic millimeter (mm 3 ), it is identified as a low-density plaque, otherwise the output is the identification result of not being a high-risk plaque.

[0124] Method for identifying point calcifications: If the area type of the target plaque sub-area within the plaque area is a non-calcified plaque area with fibers, calculate the CT mean of the fiber and fibrofat areas; if the CT mean is greater than 130HU, continue measuring the volume of the fiber and fibrofat; if the CT mean is not greater than 130HU, end the identification of point calcifications; where the volume of fiber and fibrofat = the number of pixels of fiber and fibrofat × the physical resolution of the pixel (x_spacing × y_spacing × interval (interval)); calculate the inner diameter of the location of the fiber and fibrofat; calculate whether the farthest two pixels in the volume simultaneously meet the requirements that the long diameter of the point calcification is less than 1.5 times the inner diameter and the short diameter of the point calcification is less than 2 / 3 of the inner diameter. If both are true, it is identified as point calcification.

[0125] The "napkin ring" sign is identified as follows: if the target plaque sub-region within the plaque area is a non-calcified plaque region with a necrotic core, the necrotic core is morphologically expanded; if the ratio of fiber and fibrofat within the expanded range exceeds a threshold, it is identified as a "napkin ring" sign; otherwise, the output is an identification result that is not a high-risk plaque.

[0126] Positive remodeling is identified by arranging the contours of the target branch vessels in sequence; calculating the ratio of the current outer membrane contour diameter to the average of the proximal and distal outer diameter contour diameters; if the ratio is <1.1, identification is terminated; if the ratio is >1.1, and the diameter difference between the outer and inner diameters of the target branch vessel is >1mm, and the target plaque sub-area is a non-calcified area, it is identified as positive remodeling.

[0127] For example, please refer to Figure 5, which is a schematic diagram of the data results of the positive remodeling high-risk plaque identification process provided by this application. As shown in Figure 5, the presence of positive remodeling high-risk plaques and their location can be determined based on the contour data of the blood vessels.

[0128] S104 . In response to determining that a high-risk plaque exists in the target branch vessel, output a high-risk plaque identification result of the target branch vessel; the plaque identification result includes the high-risk plaque type, the plaque location, and the vessel name of the target branch vessel.

[0129] After the high-risk plaque identification result of each target branch vessel is determined, the high-risk plaque identification result of the target vessel can be determined based on the high-risk plaque identification result of each target branch vessel.

[0130] In one embodiment provided in the present application, the plaque location includes the plaque location of each high-risk plaque, and the plaque location of each high-risk plaque is determined by the following steps: when a high-risk plaque is identified as existing in a target branch vessel, the plaque area to which the high-risk plaque belongs is determined; based on the determined plaque area and the original total medical image, the plaque location of the high-risk plaque is determined using a shortest distance mapping processing method.

[0131] An embodiment of the present application provides a method for identifying plaque in a blood vessel, the method comprising: when plaque is present in a target blood vessel to be identified, determining original plaque medical image data corresponding to each plaque based on an original overall medical image of the target blood vessel; the target blood vessel including at least one branch blood vessel; for each branch blood vessel, dividing the plaque region corresponding to each plaque in the branch blood vessel based on pixel values ​​and region type division rules in the original plaque medical image data, and determining at least one plaque sub-region and the region type of each plaque sub-region; for each target branch blood vessel having a target plaque sub-region of any specified region type, identifying the plaque region in the target branch blood vessel having the target plaque sub-region based on a high-risk plaque assessment rule and corresponding imaging parameters, and determining whether the target branch blood vessel has a high-risk plaque; in response to determining that a high-risk plaque is present in the target branch blood vessel, outputting a high-risk plaque identification result for the target branch blood vessel; the plaque identification result including the high-risk plaque type, the plaque location, and the vessel name of the target branch blood vessel.

[0132] The present application divides the plaque area according to pixel values, determines the type of sub-areas included in each plaque area, and then determines whether to identify high-risk plaques and the identification order according to the type of sub-areas. The corresponding image parameters are obtained in sequence according to the identification order and the high-risk plaques are identified, thereby improving the identification speed and accuracy of high-risk plaques in blood vessels.

[0133] Please refer to Figures 6 and 7. Figure 6 is a schematic diagram of the structure of a device for identifying plaque in a blood vessel according to an embodiment of the present application. Figure 7 is a schematic diagram of the structure of a device for identifying plaque in a blood vessel according to an embodiment of the present application. As shown in Figure 6, the identification device 600 includes:

[0134] A first determining module 610 is configured to determine original plaque medical image data corresponding to each plaque based on an original total medical image of the target vessel in response to the presence of plaque in the target vessel to be identified; the target vessel includes at least one branch vessel;

[0135] a segmentation module 620 configured to segment the plaque region corresponding to each plaque in each branch vessel according to the pixel values ​​and region type segmentation rules in the original plaque medical image data, and determine at least one plaque subregion and a region type for each plaque subregion;

[0136] The identification module 630 is configured to identify, for each target branch vessel containing a target plaque sub-region of any specified region type, the plaque region containing the target plaque sub-region in the target branch vessel based on high-risk plaque assessment rules and corresponding imaging parameters, and determine whether the target branch vessel contains a high-risk plaque;

[0137] The output module 640 is configured to output a high-risk plaque identification result of the target branch vessel in response to determining that a high-risk plaque exists in the target branch vessel; the plaque identification result includes the high-risk plaque type, plaque location, and vessel name of the target branch vessel.

[0138] Optionally, as shown in FIG7 , the identification device 600 further includes a second determination module 650, and the second determination module 650 is configured to:

[0139] obtaining a three-dimensional reconstructed vascular model of the target blood vessel;

[0140] identifying, based on a morphological processing method, whether there is a mutation region in the three-dimensional reconstructed vascular model of the target blood vessel;

[0141] In response to identifying the presence of a mutation region in the three-dimensional reconstructed blood vessel model of the target blood vessel, it is determined that a plaque exists in the target blood vessel.

[0142] Optionally, the identification device 600 further includes a construction module 660, and the construction module 660 is configured to:

[0143] performing centerline extraction processing on the original total medical image of the target blood vessel to determine the centerline of the target blood vessel;

[0144] Performing contour recognition based on the centerline of the target blood vessel and the original total medical image to determine the intima contour and adventitia contour of the target blood vessel;

[0145] performing lofting processing on the intima contour and the adventitia contour of the target blood vessel respectively to obtain an intima curved surface model and an adventitia curved surface model of the target blood vessel;

[0146] Based on the intima curved surface model and the adventitia curved surface model of the target blood vessel, volume filling processing is performed to obtain a three-dimensional reconstructed blood vessel model of the target blood vessel; the three-dimensional reconstructed blood vessel model of the target blood vessel includes a three-dimensional intima model and a three-dimensional adventitia model.

[0147] Optionally, when the first determining module 610 is configured to determine the original plaque medical image data corresponding to each plaque in the following manner, the first determining module 610 is configured to:

[0148] Determining a three-dimensional reconstruction result of all plaques in the target blood vessel using a result obtained by subtracting a three-dimensional model of the intima from a three-dimensional model of the adventitia of the target blood vessel;

[0149] According to the three-dimensional reconstruction results of all plaques, medical images at corresponding positions are extracted from the original total medical image of the target blood vessel to determine the original plaque medical image data corresponding to each plaque.

[0150] Optionally, the region type division rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.

[0151] Optionally, when the identification module 630 is configured to identify, for each target branch vessel having a target plaque sub-region of any specified region type, based on a high-risk plaque assessment rule and corresponding imaging parameters, the plaque region in the target branch vessel having the target plaque sub-region, and to determine whether the target branch vessel has a high-risk plaque, the identification module 630 is configured to:

[0152] For each plaque region in each target branch vessel where a target plaque sub-region exists, determining an identification order of high-risk plaques according to the region type of the target plaque sub-region in the plaque region;

[0153] According to the determined identification order of high-risk plaques, the imaging parameters required for identifying each high-risk plaque are sequentially obtained;

[0154] Identify the high-risk plaque according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque, until all high-risk plaques in the identification sequence are identified, and obtain the high-risk plaque identification result corresponding to the plaque area;

[0155] In response to a high-risk plaque identification result corresponding to any plaque region in the target branch vessel indicating the presence of a high-risk plaque, it is determined that the target branch vessel has a high-risk plaque.

[0156] Optionally, the identification module 630 is further configured to:

[0157] In response to identifying the presence of a high-risk plaque in the target branch vessel, determining the plaque region to which the high-risk plaque belongs;

[0158] According to the determined plaque area and the original total medical image, a shortest distance mapping processing method is adopted to determine the plaque position of the high-risk plaque.

[0159] Please refer to Figure 8 , which is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. As shown in Figure 8 , the electronic device 800 includes a processor 810 , a memory 820 , and a bus 830 .

[0160] The memory 820 stores machine-readable instructions executable by the processor 810. When the electronic device 800 is running, the processor 810 communicates with the memory 820 through the bus 830. When the machine-readable instructions are executed by the processor 810, the steps in the method embodiment shown in Figure 1 above can be executed. For specific implementation methods, please refer to the method embodiment.

[0161] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the method embodiment shown in Figure 1 above can be executed. For specific implementation methods, please refer to the method embodiment.

[0162] Those skilled in the art will appreciate that, for the convenience and brevity of description, the working processes of the above-described systems, devices, and units may refer to the corresponding processes in the aforementioned method embodiments.

[0163] In some embodiments provided in this application, the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are schematic. For example, the division of the units is a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0164] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to implement the solution of this embodiment according to actual needs.

[0165] In each embodiment of the present application, multiple functional units may be integrated into one processing unit, or multiple functional units may exist physically separately, or two or more functional units may be integrated into one processing unit.

[0166] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the solution of the present application, or the part that contributes to the relevant technology or the part of the solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes at least one instruction for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

Claims

1. A method for identifying plaque in a blood vessel, comprising: In response to the presence of plaques in the target blood vessel to be identified, original plaque medical image data corresponding to each plaque is determined according to an original total medical image of the target blood vessel; the target blood vessel includes at least one branch blood vessel; For each branch blood vessel, according to the pixel value and region type division rule in the original plaque medical image data, the plaque region corresponding to each plaque in the branch blood vessel is divided to determine at least one plaque sub-region and the region type of each plaque sub-region; For each target branch vessel having a target plaque sub-region of any specified region type, identifying the plaque region of the target branch vessel having the target plaque sub-region according to the high-risk plaque assessment rule and the corresponding imaging parameters, and determining whether the target branch vessel has a high-risk plaque; In response to determining that a high-risk plaque exists in the target branch vessel, a high-risk plaque identification result of the target branch vessel is output; the plaque identification result includes the high-risk plaque type, the plaque location, and the vessel name of the target branch vessel.

2. The identification method according to claim 1, wherein: The presence of plaque in the target vessel to be identified is determined by: Acquiring a three-dimensional reconstructed blood vessel model of the target blood vessel; identifying, according to a morphological processing method, whether there is a mutation region in the three-dimensional reconstructed blood vessel model of the target blood vessel; In response to identifying the presence of a mutation region in the three-dimensional reconstructed blood vessel model of the target blood vessel, it is determined that a plaque exists in the target blood vessel.

3. The identification method according to claim 2, wherein: The three-dimensional reconstructed vascular model of the target blood vessel is constructed by: Performing centerline extraction processing on the original total medical image of the target blood vessel to determine the centerline of the target blood vessel; Performing contour recognition according to the centerline of the target blood vessel and the original total medical image to determine the intima contour and the adventitia contour of the target blood vessel; Performing lofting processing on the intima contour and the adventitia contour of the target blood vessel respectively to obtain an intima curved surface model and an adventitia curved surface model of the target blood vessel; According to the intima curved surface model and the adventitia curved surface model of the target blood vessel, volume filling processing is performed to obtain a three-dimensional reconstructed blood vessel model of the target blood vessel; the three-dimensional reconstructed blood vessel model of the target blood vessel includes an intima three-dimensional model and an adventitia three-dimensional model.

4. The identification method according to claim 3, wherein: The determining of the original plaque medical image data corresponding to each plaque includes: Determine a three-dimensional reconstruction result of all plaques in the target blood vessel using a result obtained by subtracting a three-dimensional model of the inner membrane from a three-dimensional model of the outer membrane of the target blood vessel; According to the three-dimensional reconstruction results of all plaques, the medical image at the corresponding position is extracted from the original total medical image of the target blood vessel to determine the original plaque medical image data corresponding to each plaque.

5. The identification method according to claim 1, wherein: The region type division rule is a rule for setting different pixel threshold ranges for different region types to perform plaque quantitative analysis.

6. The identification method according to claim 1, wherein: The method of identifying, for each target branch vessel having a target plaque sub-region of any designated region type, a plaque region in the target branch vessel having a target plaque sub-region according to a high-risk plaque assessment rule and corresponding image parameters, and determining whether the target branch vessel has a high-risk plaque includes: For each plaque region in each target branch blood vessel where a target plaque sub-region exists, determining an identification order of high-risk plaques according to a region type of the target plaque sub-region in the plaque region; According to the determined recognition order of high-risk plaques, imaging parameters required for identifying each high-risk plaque are sequentially acquired; Identify the high-risk plaque according to the high-risk plaque assessment rules and the image parameters corresponding to the high-risk plaque, until all high-risk plaques in the identification sequence are identified, and obtain the high-risk plaque identification result corresponding to the plaque area; In response to the high-risk plaque identification result corresponding to any plaque area in the target branch blood vessel indicating the existence of a high-risk plaque, it is determined that the target branch blood vessel has a high-risk plaque.

7. The identification method according to claim 6, wherein: The plaque position includes the plaque position of each high-risk plaque, and the plaque position of each high-risk plaque is determined by: In response to identifying the presence of a high-risk plaque in the target branch vessel, determining the plaque region to which the high-risk plaque belongs; According to the determined plaque area and the original total medical image, the plaque position of the high-risk plaque is determined by adopting the shortest distance mapping processing method.

8. A device for identifying plaque in a blood vessel, comprising: A first determination module is configured to determine original plaque medical image data corresponding to each plaque according to an original total medical image of the target blood vessel in response to the presence of plaque in the target blood vessel to be identified; the target blood vessel includes at least one branch blood vessel; The segmentation module is configured to segment the plaque area corresponding to each plaque in the branch vessel according to the pixel value and area type segmentation rule in the original plaque medical imaging data for each branch vessel. Determine at least one plaque sub-region and a region type of each plaque sub-region; an identification module configured to identify, for each target branch vessel having a target plaque sub-region of any specified region type, a plaque region in the target branch vessel having a target plaque sub-region according to a high-risk plaque assessment rule and corresponding imaging parameters, to determine whether the target branch vessel has a high-risk plaque; The output module is configured to output a high-risk plaque identification result of the target branch blood vessel in response to determining that the target branch blood vessel has a high-risk plaque; the plaque identification result includes the high-risk plaque type, the plaque location and the blood vessel name of the target branch blood vessel.

9. An electronic device, comprising: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and the machine-readable instructions are executed by the processor to perform the method for identifying plaques in blood vessels as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, executes the method for identifying plaque in a blood vessel according to any one of claims 1 to 7.

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