Real-time image guiding method and system for coronary artery interventional therapy

Abstract

The invention relates to the technical field of image analysis, in particular to a real-time image guiding method and system for coronary artery interventional therapy, and the method comprises the steps: obtaining the DSA image data and OCT cross section image data of the coronary artery of a patient; performing lesion block segmentation on static frames in the DSA image data, determining lesion blood vessel segments, and extracting target static frames of the lesion blood vessel segments in the OCT cross section image data; determining a lipid calcification co-occurrence index and a lesion fracture confidence coefficient of each target static frame, and further determining a lesion fracture index of each target static frame; and carrying out edge detection on each target static frame, modifying a segmentation edge screening index in an edge detection process based on a lesion fracture index so as to obtain an edge detection result, and constructing a coronary artery blood vessel three-dimensional model based on the edge detection result. According to the method, the situation that multiple layers of discrete lesion areas are combined into a pseudo-continuous lesion plate is effectively avoided, and the modeling accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image analysis, and in particular to a real-time image-guided method and system for coronary artery interventional treatment. Background Art

[0002] Percutaneous coronary intervention (PCI) is the cornerstone of treating myocardial ischemia caused by coronary artery disease. Its core goal is to restore coronary blood flow blocked by atherosclerotic plaques through minimally invasive catheter-based techniques. In this highly delicate procedure, real-time image guidance plays an indispensable role as both the "eye" and "navigator," with its accuracy directly impacting surgical success and patient outcomes.

[0003] In existing PCI procedures, image-guided PCI systems rely on fusion modeling of X-ray angiography and intravascular images for instrument navigation. However, during real-time image guidance, due to the multi-layered nested structure of coronary artery lesions and the discontinuous distribution of lesion layers in three-dimensional space, common reconstruction algorithms cannot accurately interpret the relationships between lesions. They can easily mistakenly merge multiple discrete lesion regions into a pseudo-continuous lesion plate, resulting in inaccurate 3D vascular models. For example, significant deviations between lesion thickness and spatial orientation can occur in the model, ultimately causing planning and guidance based on the distorted model to fail. Summary of the Invention

[0004] To address the aforementioned technical problem in which, during real-time image-guided treatment, common reconstruction algorithms cannot accurately analyze the relationships between lesions, easily merging multiple discrete lesion regions into a pseudo-continuous lesion plate, thereby resulting in inaccurate constructed three-dimensional vascular models, the present invention aims to provide a real-time image-guided method and system for coronary interventional treatment. The technical solutions employed are as follows: In a first aspect, the present invention provides a real-time image-guided method for coronary artery intervention, comprising the following steps: Obtain DSA imaging data and OCT cross-sectional imaging data of the patient's coronary arteries; Performing lesion block segmentation on the static frames in the DSA image data to determine the lesion vascular segment, and extracting each target static frame of the lesion vascular segment in the OCT cross-sectional image data; determining a lipid calcification co-occurrence index for each target static frame based on pixel distribution characteristics of each layer of the coronary lesion structure in each target static frame; determining a lesion fracture confidence level of each target static frame based on structural change characteristics of the coronary lesion layer structure in each target static frame; determining a lesion fracture index of each target static frame based on the lipid calcification co-occurrence index and the lesion fracture confidence; Edge detection is performed on the target static frames, and a segmentation edge screening index in the edge detection process is modified based on the lesion fracture index, so as to obtain an edge detection result, and based on the edge detection result, a three-dimensional model of the coronary artery is constructed.

[0005] In some possible implementation manners, in combination with the first aspect, the lipid calcification co-occurrence index of each target static frame is determined by: a lesion block lipid heterogeneity index is determined based on a pixel distribution difference in an intima region in a layer structure region of the coronary lesion in each target static frame; a lesion block calcification density coefficient is determined based on a pixel distribution level in the intima region in the layer structure region of the coronary lesion in each target static frame; the lipid calcification co-occurrence index of each target static frame is determined based on the lesion block lipid heterogeneity index and the lesion block calcification density coefficient.

[0006] In some possible implementation manners, in combination with the first aspect, the lesion block lipid heterogeneity index is determined by: a pixel variance in the intima region in the layer structure region of the coronary lesion in each target static frame is determined, and the pixel variance is determined as the lesion block lipid heterogeneity index.

[0007] In some possible implementation manners, in combination with the first aspect, the determination of the layer structure region of the coronary lesion in each target static frame includes: a distance and a pixel difference value between any two pixel points in the target static frame are determined; a distance metric improvement value between the any two pixel points is determined based on the distance and the pixel difference value; all pixel points in the target static frame are clustered based on the distance metric improvement value, to obtain each cluster; a specific type of a layer structure region corresponding to each cluster is determined according to a pixel mean value of pixel points in each cluster.

[0008] In some possible implementation manners, in combination with the first aspect, the lesion fracture confidence of each target static frame is determined by: a lipid pool curvature change rate corresponding to each target static frame is determined based on a contour change of an intima region in a layer structure of the coronary lesion in each target static frame; a fiber cap thickness corresponding to each target static frame is determined based on a distance from a boundary point of the intima region in the layer structure of the coronary lesion in each target static frame to a boundary of a blood vessel structure. determine an outer membrane rupture index corresponding to each of the target static frames based on a distribution of edge curves of an outer membrane region in the layered structure of the coronary lesion in each of the target static frames; determine a lesion rupture confidence of each of the target static frames based on the lipid pool arc variation rate and a correlation between the variation trends of the fibrous cap thickness and the outer membrane rupture index.

[0009] In some possible implementation manners of the first aspect, the lipid pool arc variation rate corresponding to each of the target static frames is determined by: determine a long axis inclination of a minimum circumscribed ellipse of an intimal region in the layered structure of the coronary lesion in each of the target static frames; determine the lipid pool arc variation rate corresponding to each of the target static frames based on a difference between the long axis inclinations corresponding to adjacent target static frames and a time interval between the acquisition.

[0010] In some possible implementation manners of the first aspect, the outer membrane rupture index corresponding to each of the target static frames is determined by: determine a slope of an edge curve of an outer membrane region in the layered structure of the coronary lesion in each of the target static frames; determine an average slope of the slopes of all the edge curves in each of the target static frames, and determine the average slope as the outer membrane rupture index corresponding to each of the target static frames.

[0011] In some possible implementation manners of the first aspect, the lesion rupture confidence of each of the target static frames is determined by: determine a Pearson correlation coefficient between a first sequence constituted by the fibrous cap thicknesses corresponding to each of the target static frames and a second sequence constituted by the outer membrane rupture indexes corresponding to each of the target static frames; determine the lesion rupture confidence of each of the target static frames based on the lipid pool arc variation rate and a negative correlation mapping result of the Pearson correlation coefficient.

[0012] In some possible implementation manners of the first aspect, edge detection is performed on the target static frames, and a segmentation edge screening index in the edge detection process is modified based on the lesion rupture index, so as to obtain an edge detection result, including: perform negative correlation normalization on the lesion rupture index to obtain an edge segmentation correction parameter; perform edge segmentation on the target static frames by using a U-Net image segmentation method, in which the segmentation edge screening index is modified by using the edge segmentation correction parameter, and an adaptive segmentation edge screening index is obtained based on the modified segmentation edge screening index, and an edge segmentation result is obtained based on the adaptive segmentation edge screening index.

[0013] In a second aspect, the present application also provides a real-time image guidance system for coronary intervention, comprising a memory and a processor. The memory is configured to store executable computer program code, and the processor is configured to call and run the executable computer program code from the memory, so that the system executes the method in the first aspect or any possible implementation manner of the first aspect.

[0014] In a third aspect, the present application also provides a real-time image guidance device for coronary intervention, comprising: an image data acquisition module configured to acquire DSA image data and OCT cross-sectional image data of a patient's coronary artery; a target frame acquisition module configured to perform lesion block segmentation on a static frame in the DSA image data, determine a lesion vessel segment, and extract each target static frame of the lesion vessel segment in the OCT cross-sectional image data; a co-occurrence index acquisition module configured to determine a lipid calcification co-occurrence index of each target static frame based on pixel distribution characteristics of each layer structure region of the coronary lesion in the target static frame; a confidence degree acquisition module configured to determine a lesion rupture confidence degree of each target static frame based on structure change characteristics of the layered structure of the coronary lesion in the target static frame; a rupture index acquisition module configured to determine a lesion rupture index of each target static frame based on the lipid calcification co-occurrence index and the lesion rupture confidence degree; a three-dimensional construction module configured to perform edge detection on the target static frames, modify a segmentation edge screening index in the edge detection process based on the lesion rupture index, thereby obtaining an edge detection result, and construct a three-dimensional model of the coronary vessel based on the edge detection result.

[0015] In a fourth aspect, the present application also provides a computer program product, which comprises computer program code. When the computer program code is run on a computer, the computer program code causes the computer to execute the real-time image guidance method for coronary intervention in the first aspect or any possible implementation manner of the first aspect.

[0016] In a fifth aspect, the present application also provides a computer-readable storage medium, which stores computer program code. When the computer program code is run on a computer, the computer program code causes the computer to execute the real-time image guidance method for coronary intervention in the first aspect or any possible implementation manner of the first aspect.

[0017] The present application has the following beneficial effects: by simultaneously acquiring multi-dimensional image data of the coronary artery of the patient, i.e., DSA image data and OCT cross-sectional image data, and by performing lesion block segmentation on the static frames in the DSA image data to determine the lesion vessel segment, and then extracting each target static frame of the lesion vessel segment in the OCT cross-sectional image data, since the coronary artery exhibits a multi-layer structure, such as the intima, media and adventitia, based on the pixel distribution characteristics of each layer structure region of the coronary artery lesion in each target static frame, the pathological coupling strength of the lipid disorder degree and the calcification density is dynamically analyzed to determine the lipid calcification co-occurrence index of each target static frame; at the same time, since the rupture nature of the coronary artery lesion is the structural collapse caused by the mechanical instability of the lipid pool, based on the structural change characteristics of the layered structure of the coronary artery lesion in each target static frame, the lesion rupture confidence of each target static frame is determined to quantify the lesion block rupture possibility of the lesion vessel segment in each target static frame; the lipid calcification co-occurrence index and the lesion rupture confidence are fused to determine the lesion rupture index of each target static frame, when the lesion rupture index is larger, it indicates that the structure of the lesion region is more complex, and there is significant rupture or discontinuity, in this case, the degree of edge pixel point presentation should be limited to the detection algorithm of the rupture region, to realize the structural independence of the discrete lesion region and the clarity of the rupture boundary; then, edge detection is performed on each target static frame, and based on the lesion rupture index, the segmentation edge screening index in the edge detection process is modified, so that an accurate edge detection result is obtained, and finally based on the edge detection result, an accurate three-dimensional model of the coronary artery is constructed. The present application can effectively retain the true edge structure of the independent discrete lesion block in the lesion block, and can realize accurate differentiation between the real lesion structure edge pixel and the cross-rupture region pseudo-boundary pixel point, thereby improving the coronary artery modeling accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, and the advantages thereof, a brief introduction will be given to the drawings needed in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0019] Figure 1 A step flow chart of a real-time image guided method for coronary artery intervention treatment according to an embodiment of the present application; Figure 2 A schematic diagram of a static frame in DSA image data according to an embodiment of the present application; Figure 3 A schematic diagram of a static frame in OCT cross-sectional image data of an embodiment of the application; Figure 4 A schematic diagram of a structure of a real-time image guiding device for coronary intervention of an embodiment of the application. DETAILED DESCRIPTION

[0020] In order to clearly illustrate the technical features of the scheme, specific implementation manners will be described below in conjunction with the accompanying drawings.

[0021] Embodiments of the present application will be described in more detail by referring to the accompanying drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be interpreted as being limited to the embodiments set forth herein, but rather these embodiments are provided to more thoroughly and completely understand the present application. It should be understood that the drawings and embodiments of the present application are for exemplary purposes only and are not intended to limit the scope of protection of the present application.

[0022] It should be understood that each step described in the method embodiments of the present application can be performed in different order and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0023] The term "comprising" and variations thereof as used herein are open-ended, that is, "comprising but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Related definitions will be given in the description below.

[0024] It should be noted that the concepts of "first", "second", etc. mentioned in the present application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0025] In the embodiments of the present application, although the operations or steps are described in a specific order in the accompanying drawings, it should not be understood as requiring the operations or steps to be performed in the specific order or serial order shown, or requiring all of the operations or steps to be performed to obtain the desired results. In the embodiments of the present application, these operations or steps can be performed in series; they can also be performed in parallel; and a part of them can be performed.

[0026] Meanwhile, it can be understood that the data (including but not limited to the data itself, acquisition or use of the data) involved in the technical solutions of the present application should comply with the requirements of the corresponding laws, regulations and relevant provisions. Unless otherwise defined, all technical and scientific terms used in the present application have the same meaning as understood by those skilled in the art to which the present application belongs, and all parameters or indicators in the formulas involved in the present application are normalized values after eliminating the dimension influence.

[0027] In order to solve the problem that in real-time image guidance process, due to the general reconstruction algorithm cannot accurately analyze the relationship between lesions, it is easy to merge multi-layer discrete lesion regions into pseudo-continuous lesion plates, thereby causing the constructed three-dimensional model of blood vessels to be not accurate enough, the embodiment of the present application provides a real-time image guidance method and system for coronary artery intervention treatment, which breaks through the technical bottleneck of distortion of pseudo-continuous lesion plates in traditional three-dimensional reconstruction, and first realizes multi-modal dynamic fusion modeling of pathological structure and mechanical instability characteristics, thereby effectively improving the accuracy of coronary artery vessel model construction.

[0028] In the following, a real-time image guidance method and system for coronary artery intervention treatment provided by the embodiment of the present application will be described in detail with reference to the accompanying drawings.

[0029] Figure 1 A basic flow diagram of a real-time image guidance method for coronary artery intervention treatment provided by the embodiment of the present application is shown, as shown in Figure 1 The method specifically includes the following steps: Step S100: Obtain DSA image data and OCT cross-sectional image data of the patient's coronary artery.

[0030] The existing coronary image acquisition system is used to collect multi-source image data of the patient's coronary artery, and the DSA image data and OCT cross-sectional image data of the patient's coronary artery are obtained.

[0031] In a specific example, after the guide catheter is positioned to the patient's coronary artery opening, first, the pulse type space-time encoding contrast agent injection is used to ensure the development capture of the coronary blood vessels. Then, the double-axis rotating DSA system performs 200° rotating scanning at an angular velocity of 40° / s, and the scanning parameters are configured as: tube voltage 90kV, tube current 120mA, layer thickness 0.28mm, frame rate 30fps, and the coronary angiography sequence image is obtained, that is, the DSA image data of the patient's coronary artery is obtained. Figure 2 A static frame schematic diagram in the DSA image data is shown.

[0032] The three-mode coaxial catheter is driven by a stepper motor to perform axial retraction at a constant speed of 0.5 mm / s, and the total retraction stroke is 100 mm. During this process: the middle layer OCT fiber adopts a dual-wavelength alternating scanning mode of 1310 nm and 1550 nm, and outputs a continuous sequence of coronary OCT images in the scanning period, thereby obtaining the OCT cross-sectional image data of the patient's coronary artery. Figure 3 A static frame diagram in the OCT cross-sectional image data is shown.

[0033] Step S200: Perform lesion block segmentation on the static frames in the DSA image data, determine the lesion vessel segment, and extract each target static frame of the lesion vessel segment in the OCT cross-sectional image data.

[0034] The static frames in the DSA image data are segmented to determine the lesion vessel segment in the static frames in the DSA image data.

[0035] In a specific example, first, a pre-trained U-Net image segmentation method is used to perform coronary lesion block pre-segmentation on each static frame in all DSA image data, generate a coronary lesion mask, and determine the vessel segment in the corresponding static frame as the lesion vessel segment.

[0036] Next, for all coronary lesion masks, the corresponding lesion vessel segment in the OCT cross-sectional image data is extracted, and these OTC cross-sectional images are recorded as each target static frame of the lesion vessel segment in the OCT cross-sectional image data. Each target static frame refers to the continuous frame OCT cross-sectional image corresponding to the lesion vessel segment in the OCT cross-sectional image data.

[0037] Step S300: Based on the pixel distribution characteristics of the coronary lesion structure region in each target static frame, determine the lipid calcification co-occurrence index of each target static frame.

[0038] Coronary arteries exhibit a three-layered structure, including the intima (lipid metabolism zone), the media (calcification zone), and the adventitia (vasa vasorum). During 3D reconstruction of rapidly calcified coronary arteries, the lipid and calcification regions within the rapidly calcified lesion are highly complex and intertwined. This 3D reconstruction requires correcting the filtering scale for edge pixels in the final 2D image of this region and removing pseudo-boundary pixels that cross fractures to ensure accurate identification of the true boundaries of discrete lesion regions. Therefore, this embodiment first identifies the structural regions of each coronary lesion layer (intima, media, and adventitia) in a target static frame. Based on the pixel distribution characteristics of each layer, a lipid calcification co-occurrence index is determined for the target static frame. This lipid calcification co-occurrence index reflects the degree of simultaneous activity of lipid inflammation and calcification deposition in the lesion segment. A significantly elevated lipid calcification co-occurrence index indicates simultaneous activity of lipid inflammation and calcification deposition in the lesion, forming a "high-fluidity lipid-high-rigidity calcification" interlayer. This induces interlayer concentration under blood shear stress, increasing the complexity of cross-layer coupling within the lesion.

[0039] Due to the structural differences between the various layers of a coronary lesion (intima, media, and adventitia), they exhibit distinct pixel variations. For example, the intima (lipid pools), media (calcification deposits), and adventitia (vascular integrity) layers appear as low, high, and medium grayscale, respectively. Therefore, by analyzing the pixel distribution characteristics of each layer of a coronary lesion in each target static frame, we can first identify the structural regions of the coronary lesion in that target static frame.

[0040] Furthermore, the process of determining each layer structure area of ​​the coronary lesion in each target static frame in the above step S300 includes: determining the distance and pixel difference value between any two pixel points in the target static frame; determining the distance metric improvement value between the any two pixel points based on the distance and pixel difference value; clustering all pixel points in the target static frame based on the distance metric improvement value to obtain each cluster; and determining the specific type of the layer structure area corresponding to each cluster according to the pixel mean value of the pixel points in each cluster.

[0041] In a specific example, first, for each target static frame of the diseased blood vessel segment in the OCT cross-sectional image data, the pixel value of each pixel in the target static frame is counted. , and calculate the distance between any two pixels , and the pixel difference value , the distance is the Euclidean distance between any two pixels, the pixel difference value It is the absolute value of the difference between the grayscale values ​​of any two pixels.

[0042] Secondly, according to the distance between any two pixels , and the pixel difference value , calculate any two pixels Improved distance metric between .in, Represents any two pixels The distance between Represents any two pixels The pixel difference between Represents the maximum-minimum normalization function, which is used to and Perform standard normalization.

[0043] Then, the elbow method is used to determine the k-means clustering algorithm. (corresponding to the intima / media / adventitia), based on the traditional k-means clustering algorithm and distance metric improvement value All pixels in the target static frame are clustered, and the clustering results are continuously updated through iteration (traditional clustering iteration) until all clustering result clusters no longer change, thereby obtaining three clusters, each of which represents pixels of the same structure in the diseased vascular segment.

[0044] Finally, the pixel mean of each pixel in each cluster is calculated , and according to the corresponding three clusters The three clusters are divided into low-grayscale, high-grayscale, and medium-grayscale pixel clusters. The regions formed by these three pixel clusters correspond to the intima (lipid pool), media (calcification deposits), and adventitia (vascular vasa vasorum integrity) layers of the diseased vessel segment, respectively. This allows the identification of the structural regions of each layer of the coronary lesion (intima, media, and adventitia) in the target static frame. It should be understood that other existing methods can also be used to identify the structural regions of each layer of the coronary lesion in the target static frame, and this is not limited here.

[0045] For each target static frame of the diseased vascular segment in the OCT cross-sectional imaging data, the pixel distribution in the intima area is disordered, indicating that the pathological activity intensity of the lipid pool in the current target static frame is high, indicating that the cell fragments / cholesterol crystals in the lipid pool of the lesion mass increase, which means that the inflammation of the lesion mass is active; at the same time, as the lesion mass worsens, the calcification deposition in the calcified area of ​​the lesion structure (the vicinity of the media layer) becomes more significant, showing a significant increase in the regional pixel mean. Therefore, the lipid calcification co-occurrence index of each target static frame can be determined by analyzing the pixel distribution characteristics of each layer of the coronary lesion structure in each target static frame.

[0046] Further, the step S300 of determining the lipid calcification co-occurrence index of each target static frame comprises: determining a plaque lipid heterogeneity index based on pixel distribution difference in the intima region in the coronary lesion layer structure region in each target static frame; determining a plaque calcification density coefficient based on pixel distribution level in the intima region in the coronary lesion layer structure region in each target static frame; and determining the lipid calcification co-occurrence index of each target static frame based on the plaque lipid heterogeneity index and the plaque calcification density coefficient.

[0047] In a specific example, first, for the coronary lesion layer structure region in each target static frame of the lesion vessel segment in the OCT cross-sectional image data, for the lipid pool pixel set i.e. the low gray pixel point cluster corresponding to the intima region, calculate the pixel variance of all pixel points in the set and take the pixel variance as the plaque lipid heterogeneity index to quantify the pathological activity intensity of the lipid pool in the current target static frame.

[0048] Secondly, for the calcification region pixel set i.e. the high gray pixel point cluster corresponding to the media, calculate the pixel mean of all pixel points in the set and take the pixel mean as the plaque calcification density coefficient to quantify the calcification deposition degree in the current target static frame. The plaque calcification density coefficient has a larger value, indicating that the calcification deposition is more significant and the brittleness increases.

[0049] Finally, the plaque lipid heterogeneity index and the plaque calcification density coefficient are combined to measure the lipid calcification co-occurrence index of the lesion vessel segment in the current target static frame, wherein represents the lipid calcification co-occurrence index of the target static frame n.

[0050] In the step S300, by accurately decoupling the coronary lesion layer structure in each target static frame and analyzing the pixel distribution characteristics in each layer structure region, the plaque lipid heterogeneity index and the plaque calcification density coefficient are determined to quantify the lipid pool pathological activity intensity and calcification deposition degree of the lesion vessel segment in each target static frame, so as to accurately determine the lipid calcification co-occurrence index of each target static frame.

[0051] Step S400: determining a lesion rupture confidence of each target static frame based on the structural change characteristics of the coronary lesion layer structure in each target static frame.

[0052] ​​In the process of coronary artery three-dimensional reconstruction, for the plaque area with multi-layer nested structure and the discrete (non-continuous) distribution of the lesion structure in space, the edge detection algorithm in the traditional segmentation model cannot accurately distinguish the true lesion structure edge pixels and the pseudo boundary pixels across the broken area (both have higher edge pixel gradient compared with normal pixels) only relying on the edge point pixel gradient, resulting in the blurring of the final broken area boundary, and the multiple discrete lesion layers are miscombined as pseudo continuous lesion area.

[0053] Since the broken nature of coronary artery lesions is the structural collapse caused by the mechanical instability of lipid pools, specifically manifested as the entanglement of the bending mutation and calcification and lipid area in the image, by analyzing the target static frame of the lesion vessel segment in the OCT cross-sectional image data, the true broken condition of the lesion can be accurately identified, thereby constraining the edge point identification in the three-dimensional reconstruction process, and finally improving the accuracy of the lesion area segmentation.

[0054] Since the coronary plaque rupture usually shows structural feature changes of the coronary lesion layered structure, for example, in the coronary plaque structure, the lipid pool is relatively weak and is easily affected by blood flow pressure and surrounding stress, and breaks when exceeding the bearing limit, resulting in sharp changes in the edge, reflecting the plaque structure rupture, therefore, by identifying the structural change characteristics of the coronary lesion layered structure in each target static frame, the lesion rupture confidence of each target static frame can be determined to quantify the plaque rupture possibility of the lesion vessel segment in each target static frame in the OCT cross-sectional image data.

[0055] Further, the step S400 of determining the lesion rupture confidence of each target static frame includes: determining the lipid pool curvature change rate corresponding to each target static frame based on the contour change of the intimal region in the coronary lesion layered structure in the target static frame; determining the fibrous cap thickness corresponding to each target static frame based on the distance from the boundary point of the intimal region in the coronary lesion layered structure in the target static frame to the blood vessel structure boundary; determining the adventitial rupture index corresponding to each target static frame based on the edge curve distribution of the intimal region in the coronary lesion layered structure in the target static frame; and determining the lesion rupture confidence of each target static frame based on the correlation between the change trend of the lipid pool curvature change rate and the fibrous cap thickness and the adventitial rupture index.

[0056] In a specific example, first, since the coronary plaque rupture usually shows the curvature mutation of the lipid pool region, by analyzing the contour change of the intimal region in the coronary lesion layered structure in each target static frame, the lipid pool curvature change rate corresponding to each target static frame can be determined.

[0057] Specifically, the long axis inclination angle of the minimum circumscribed ellipse of the intimal region in the layered structure of the coronary lesion in each target static frame is determined, that is, for the intimal region of the lipid pool region in the layered structure of the coronary lesion, that is, the cluster of low gray pixel points, the minimum circumscribed ellipse of the lipid pool region is determined, and the long axis inclination angle of the ellipse is extracted The long axis inclination angle is the angle between the long axis of the ellipse and the horizontal line, and the long axis inclination angle is used to reflect the curvature of the circumscribed ellipse. Then, based on the difference between the long axis inclination angles corresponding to adjacent target static frames and the time interval between the collection, the lipid pool curvature change rate corresponding to each target static frame is determined. For target static frame n, based on the long axis inclination angles and corresponding to target static frame n and the next target static frame n+1, and combined with the time interval between adjacent target static frames , the lipid pool curvature change rate corresponding to target static frame n is determined. The larger the lipid pool curvature change rate, the more severe the shape change of the lipid pool region of the lesion in the lesion vessel segment corresponding to the adjacent frame target static frames n and n+1, which indicates that the lesion structure distribution may be broken.

[0058] Secondly, in order to exclude false positive curvature noise caused by catheter displacement artifact and calcification shadow, mechanical instability verification is performed through the negative correlation coupling mechanism of the effective thickness of the fibrous cap in the axial sequence and the adventitial disruption index. Therefore, based on the distance from the boundary point of the intimal region in the layered structure of the coronary lesion in each target static frame to the boundary of the blood vessel structure, the fibrous cap thickness corresponding to each target static frame is determined; at the same time, based on the edge curve distribution of the adventitial region in the layered structure of the coronary lesion in each target static frame, the adventitial disruption index corresponding to each target static frame is determined.

[0059] Specifically, the discrete point set of the boundary of the intimal region in the layered structure of the coronary lesion in each target static frame is extracted, the distance from each point in the discrete point set to the boundary of the blood vessel structure (the outermost edge of Canny edge detection) is calculated, and the minimum value of all distances is taken as the fibrous cap thickness of the coronary lesion structure at the current position. The smaller the fibrous cap thickness, the weaker the intimal pressure bearing capacity of the lesion structure at the current position. At the same time, the slope of the edge curve of the adventitial region in the layered structure of the coronary lesion in each target static frame is determined. For the adventitial region in the layered structure of the coronary lesion, that is, the region composed of clusters of medium gray pixel points, all complete edge curves in the adventitial region are extracted, and the curvature of each edge curve is calculated Then, the average slope of the slopes of all edge curves in each target static frame is determined, and the average slope is determined as the adventitial rupture index corresponding to each target static frame. That is, for the adventitial region in the coronary lesion layer structure in each target static frame, the curvature mean of all edge curves in the adventitial region is calculated. , and the curvature mean The adventitial rupture index of the coronary artery disease block in the target static frame is used. A larger value of the adventitial rupture index indicates a loss of adventitial structural integrity, i.e., folding / rupture of the adventitial collagen fiber network.

[0060] Finally, the change trend correlation between the fibrous cap thickness and the outer membrane rupture index corresponding to each adjacent target static frame of the target static frame was used to correct the arc change rate of the lipid pool corresponding to the target static frame, thereby determining the lesion rupture confidence of each target static frame.

[0061] Specifically, the Pearson correlation coefficient between the first sequence consisting of the fiber cap thickness corresponding to each adjacent target static frame of each target static frame and the second sequence consisting of the adventitial rupture index is determined. That is, for any target static frame, the nearest several target static frames (such as 5 frames) are taken as its adjacent target static frames, and the fiber cap thickness corresponding to these adjacent target static frames is calculated. Adventitial rupture index They are arranged in the order of adjacent target static frames to obtain the first sequence and the second sequence . Calculate the first sequence and the second sequence Pearson correlation coefficient , the value range of the correlation coefficient is from -1 to 1. The closer the value is to -1, the more it indicates that the thinning of the fibrous cap and the tearing of the outer membrane in the lesion segment occur simultaneously, indicating that there is significant mechanical instability in the lesion structure in the lesion segment, indicating that the possibility of a fracture point in the lesion structure at the corresponding position of the current OCT target static frame is greater. Furthermore, based on the negative correlation mapping results of the lipid pool curvature change rate and the Pearson correlation coefficient, the lesion fracture confidence of each target static frame is determined. That is, for each target static frame distributed along the axial direction of the blood vessel, the lesion fracture confidence at the corresponding position of each target static frame is jointly determined based on the lipid pool curvature change rate and the Pearson correlation coefficient of the fibrous cap thickness of the corresponding coronary lesion structure and the outer membrane rupture index. For target static frame n, based on the lipid pool curvature change rate corresponding to the target static frame n and the first sequence and the second sequence Pearson correlation coefficient , calculate the corresponding lesion fracture confidence The value of the lesion rupture confidence is larger, indicating that the lipid pool of the current position of the lesion structure has a high degree of deformation, the fiber cap is thinned, and the synchronization of the outer membrane rupture is enhanced, meaning that the corresponding target static frame corresponds to a coronary lesion in a physically unstable state, indicating that there is a significant breaking point or breaking area at this position.

[0062] In the above step S400, by determining the lipid pool arc rate of change, the fiber cap thickness and the outer membrane rupture index corresponding to each target static frame, and finally based on the change trend correlation between the lipid pool arc rate of change and the fiber cap thickness and the outer membrane rupture index, the lesion rupture confidence of each target static frame is accurately determined.

[0063] Step S500: Based on the lipid calcification co-occurrence index and the lesion rupture confidence, determine the lesion rupture index of each target static frame.

[0064] The lipid calcification co-occurrence index and the lesion rupture confidence of each target static frame determined above are fused to obtain the lesion rupture index of each target static frame. The lesion rupture index is a comprehensive quantitative index of the lesion blood vessel segment at the position corresponding to the target static frame, and its value directly reflects the decision basis for the final edge pixel point retention degree of the rupture region in the region segmentation in the three-dimensional reconstruction process. The larger the value, the more complex the structure of the lesion region, and there is a significant rupture or discontinuity. In this case, the presentation retention degree of the edge pixel point detected by the rupture region algorithm should be limited to achieve the structural independence of the discrete lesion region and the clarity of the rupture boundary, and to ensure the accuracy of the subsequent three-dimensional modeling result.

[0065] In a specific example, for any target static frame n, based on its corresponding lipid calcification co-occurrence index and the lesion rupture confidence , its corresponding lesion rupture index is determined. Wherein, the logarithmic function aims to compress the lipid calcification co-occurrence index to a reasonable range.

[0066] Step S600: Perform edge detection on the target static frames, and based on the lesion rupture index, modify the segmentation edge screening index in the edge detection process to obtain the edge detection result. Based on the edge detection result, a three-dimensional model of the coronary artery is constructed.

[0067] In the U-Net image segmentation method, first, the input image is feature extracted by a convolutional neural network (CNN). The U-Net network structure has an encoder and a decoder, the encoder gradually compresses the features of the input image, and the decoder restores the compressed features to the size of the original image. In this process, the U-Net network combines the shallow features in the encoder with the deep features in the decoder through a skip connection to retain more detailed information.

[0068] Based on this, the U-Net image segmentation method can effectively extract the detailed information in the image by fusing features of different scales and accurately predict the pixel-level segmentation result at the boundary. In the segmentation process, all edge pixels are sorted according to the edge point pixel gradient to obtain an edge point gradient sequence, and then when obtaining the edge curve of the final image, the edge pixel points ranked in the top proportion (the proportion is also called a segmentation edge screening index) is selected as the final segmentation edge pixel point of the image (segmentation method in the prior art). In order to further optimize the segmentation result, the lesion fracture index is used to further constrain and screen the final edge pixel points of the lesion vessel region of each target static frame during algorithm segmentation, so as to effectively retain more detailed information and reduce information loss caused by edge overlap or small edge.

[0069] Further, the step S600 detects the edge of each target static frame, and modifies the segmentation edge screening index in the edge detection process based on the lesion fracture index, so as to obtain an edge detection result, including: performing negative correlation normalization on the lesion fracture index to obtain an edge segmentation correction parameter; using the U-Net image segmentation method to perform edge segmentation on the target static frame, in the edge segmentation process, the edge segmentation correction parameter is used to correct the segmentation edge screening index, and based on the adaptive segmentation edge screening index obtained after correction, the edge segmentation result is obtained.

[0070] In a specific example, the U-Net image segmentation method is used to perform edge segmentation on each target static frame, and in the segmentation process, for any target static frame n, based on the corresponding lesion fracture index , the segmentation edge screening index in the U-Net image segmentation method is corrected . Wherein, represents the initial segmentation edge screening index of the target static frame n using the U-Net image segmentation method, that is, the above-mentioned proportion; represents the adaptive segmentation edge screening index of the target static frame n after correction, represents a linear normalization function, which is used to normalize the lesion fracture index The value of the lipid calcification co-occurrence index is normalized to the range of (0, 1). Thus, based on the adaptive segmentation edge screening index obtained after correction of each target static frame, the edge of each target static frame is segmented by using a U-Net image segmentation method to obtain a final edge pixel set of each target static frame, and then a real edge curve of each target static frame is obtained, so that an edge segmentation result is obtained, and finally the precise segmentation of the discrete lesion area is realized.

[0071] Based on the real edge curve of each target static frame, i.e. the edge segmentation result, a three-dimensional reconstruction algorithm is used to perform three-dimensional reconstruction of the blood vessels, so that a three-dimensional model of the coronary artery blood vessels is obtained. The three-dimensional model of the blood vessels includes two types of structures: a continuous lesion area (a smooth over-surface) and a discrete lesion block (an independent geometric body separated by a broken boundary), thereby effectively eliminating the modeling error of the "pseudo-continuous lesion plate" in the traditional method, and helping the doctor to perform subsequent interventional treatment based on the three-dimensional model of the coronary artery.

[0072] In the real-time image guidance method for coronary artery interventional treatment provided in the embodiments of the present application, first, the three-layer lesion structure is accurately decoupled based on the clustering algorithm for OCT cross-section influence, a lipid calcification co-occurrence index quantification model is created, the pathological coupling strength of lipid disorder degree and calcification density is dynamically analyzed, and the problem of mis-merging of discrete lesion structures in the nested lesion layer is solved; secondly, a lesion fracture confidence double-factor verification mechanism is proposed, the multi-dimensional cross verification of the fracture boundary is realized through the lipid pool arc mutation detection and the correlation analysis of the fibrous cap outer membrane mechanical instability; finally, a lesion connectivity index dynamic constraint engine is creatively constructed, the pathological complexity and the mechanical instability signal are converted into a spatial edge pixel screening rule, i.e. the value of the lesion fracture index determined based on the lipid calcification co-occurrence index and the lesion fracture confidence is used to correct the edge pixel screening scale in the original segmentation model, and the number of edge points output by the algorithm is reduced for the high lesion fracture index area, so as to realize the preservation of the real edge structure of the independent discrete lesion block in the lesion block, provide the fracture boundary navigation ability with millimeter-level precision for the coronary artery interventional surgery, and effectively weaken the problem of modeling distortion of vulnerable plaques caused by the fact that the traditional method cannot accurately distinguish the real lesion structure edge pixels and the pseudo-boundary pixels across the fracture area, so that the final fracture area boundary is blurred and cannot be identified, and then the modeling distortion of vulnerable plaques is caused.

[0073] Based on the same inventive concept, as shown in Figure 4 The present application also provides a real-time image guidance device for coronary artery interventional treatment, which comprises: An image data acquisition module for acquiring DSA image data and OCT cross-sectional image data of the coronary artery of a patient; a target frame acquisition module, configured to perform lesion block segmentation on static frames in the DSA image data, determine a lesion blood vessel segment, and extract each target static frame of the lesion blood vessel segment in the OCT cross-sectional image data; a co-occurrence index acquisition module, configured to determine a lipid calcification co-occurrence index of each target static frame based on pixel distribution characteristics of a coronary artery lesion layer structure region in the target static frame; a confidence degree acquisition module, configured to determine a lesion fracture confidence degree of each target static frame based on structure change characteristics of a coronary artery lesion layer structure in the target static frame; a fracture index acquisition module, configured to determine a lesion fracture index of each target static frame based on the lipid calcification co-occurrence index and the lesion fracture confidence degree; a three-dimensional construction module, configured to perform edge detection on the target static frames, modify a segmentation edge screening index in the edge detection process based on the lesion fracture index, so as to obtain an edge detection result, and construct a three-dimensional model of a coronary artery based on the edge detection result.

[0074] It should be noted that the apparatus provided in the above embodiments is only used as an example for the division of the above functional modules, and in actual applications, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the above-described functions.

[0075] Based on the same inventive concept, the embodiments of the present application also provide a real-time image guidance system for coronary intervention treatment, which comprises a memory, a processor and computer program code stored in the memory and running on the processor, wherein when the processor executes the computer program code, the system can execute any one of the real-time image guidance methods for coronary intervention treatment introduced above.

[0076] The embodiments of the present application can divide the system into functional modules according to the above method examples, for example, each functional module can be corresponding, or two or more functions can be integrated in one processing module, and the integrated module can be realized in the form of hardware. It should be noted that the division of the modules in the present embodiment is illustrative, and is only a logical function division, and another division mode can be used in actual implementation.

[0077] Based on the same inventive concept, the embodiments of the present application also provide a computer program product, which comprises computer program code, when the computer program code runs on a computer, the computer executes any one of the real-time image guidance methods for coronary intervention treatment introduced above.

[0078] Based on the same inventive concept, the embodiment of the present application also provides a computer readable storage medium, which stores computer program codes, and when the computer program codes are run on a computer, the computer is caused to execute any one of the aforementioned real-time image guidance methods for coronary intervention treatment.

[0079] It should be noted that the above-mentioned embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent ones; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A real-time image-guided method for coronary artery intervention, characterized in that: The following steps are involved: Obtain DSA imaging data and OCT cross-sectional imaging data of the patient's coronary arteries; Performing lesion block segmentation on the static frames in the DSA image data to determine the lesion vascular segment, and extracting each target static frame of the lesion vascular segment in the OCT cross-sectional image data; determining a lipid calcification co-occurrence index for each target static frame based on pixel distribution characteristics of each layer of the coronary lesion structure in each target static frame; determining a lesion fracture confidence level of each target static frame based on structural change characteristics of the coronary lesion layer structure in each target static frame; determining a lesion fracture index of each target static frame based on the lipid calcification co-occurrence index and the lesion fracture confidence; Edge detection is performed on each target static frame, and based on the lesion fracture index, the segmentation edge screening index in the edge detection process is modified to obtain an edge detection result, and based on the edge detection result, a three-dimensional vascular model of the coronary artery is constructed.

2. The real-time image-guided method for coronary artery intervention according to claim 1, characterized in that: Determine the lipid calcification co-occurrence index for each target static frame, including: determining a lipid heterogeneity index of the lesion based on pixel distribution differences within the intima region of each layer of the coronary lesion in each target static frame; determining a calcification density coefficient of the lesion based on a pixel distribution level within an intima region of each layer of the coronary lesion structure in each target static frame; Based on the disease mass lipid heterogeneity index and the disease mass calcification density coefficient, a lipid calcification co-occurrence index of each target static frame is determined.

3. The real-time image-guided method for coronary artery intervention according to claim 2, characterized in that: Determine the lipid heterogeneity index of the lesion, including: The pixel variance in the intima region of each layer structure region of the coronary lesion in each target static frame is determined, and the pixel variance is determined as the lesion lipid heterogeneity index.

4. The real-time image-guided method for coronary intervention according to any one of claims 1 to 3, characterized in that: The process of determining the structural regions of each layer of the coronary lesion in each target static frame includes: Determine the distance and pixel difference between any two pixels in the target static frame; Determining a distance metric improvement value between the arbitrary two pixel points based on the distance and the pixel difference value; Clustering all pixels in the target static frame based on the distance metric improvement value to obtain clusters; The specific type of the layer structure area corresponding to each cluster is determined according to the pixel mean value of the pixel points in each cluster.

5. The real-time image-guided method for coronary interventional therapy according to claim 1, characterized in that: Determine the confidence level of lesion rupture for each target static frame, including: determining a lipid pool arc change rate corresponding to each target static frame based on a contour change of an intima region in a coronary lesion layer structure in each target static frame; determining the fibrous cap thickness corresponding to each target static frame based on the distance from the boundary point of the intima region of the coronary lesion layer structure to the boundary of the vascular structure in each target static frame; determining an adventitial rupture index corresponding to each target static frame based on a distribution of edge curves of the adventitial region in the coronary lesion layer structure in each target static frame; The confidence level of lesion rupture in each target static frame is determined based on the rate of change of the lipid pool arc and the correlation between the change trend of the fibrous cap thickness and the outer membrane rupture index.

6. The real-time image-guided method for coronary artery intervention according to claim 5, characterized in that: Determining the rate of change of the lipid pool arc corresponding to each target static frame includes: determining the major axis inclination angle of the minimum circumscribed ellipse of the intima region in the coronary lesion layer structure in each target static frame; Based on the difference between the long-axis inclination angles corresponding to adjacent target static frames and the acquisition time interval, the radian change rate of the lipid pool corresponding to each target static frame is determined.

7. The real-time image-guided method for coronary artery intervention according to claim 5, characterized in that: Determining the adventitial rupture index corresponding to each target static frame includes: determining the slope of the edge curve of the adventitial region in the coronary lesion layer structure in each target static frame; An average slope of the slopes of all the edge curves in each target static frame is determined, and the average slope is determined as the adventitia rupture index corresponding to each target static frame.

8. The real-time image-guided method for coronary artery intervention according to claim 5, characterized in that: Determine the confidence level of lesion rupture for each target static frame, including: determining a Pearson correlation coefficient between a first sequence consisting of fibrous cap thicknesses corresponding to adjacent target static frames of each target static frame and a second sequence consisting of adventitial rupture indexes; The lesion rupture confidence of each target static frame is determined based on the negative correlation mapping result of the lipid pool arc change rate and the Pearson correlation coefficient.

9. The real-time image-guided method for coronary interventional therapy according to claim 1, characterized in that: Performing edge detection on each target static frame and modifying the segmentation edge screening index in the edge detection process based on the lesion fracture index to obtain edge detection results, including: Performing negative correlation normalization processing on the lesion fracture index to obtain edge segmentation correction parameters; The U-Net image segmentation method is used to perform edge segmentation on the target static frames. During the edge segmentation process, the segmentation edge screening index is corrected using the edge segmentation correction parameter, and the edge segmentation result is obtained based on the adaptive segmentation edge screening index obtained after the correction.

10. A real-time image-guided system for coronary interventional therapy, characterized in that: The invention comprises a memory, a processor, and an executable computer program code stored in the memory and runnable on the processor, wherein when the processor executes the computer program code, the real-time image-guided method for coronary artery interventional treatment as claimed in any one of claims 1 to 9 is executed.

Images