32 results about "Stenosis degree" patented technology

The invention relates to the technical field of medical data processing, and discloses a non-invasive hemodynamic assessment system for coronary artery stenosis degree, comprising an image acquisition module used for acquiring a coronary artery computed tomography angiography image of a patient; the physiological signal acquisition module is used for synchronously acquiring physiological signals of a patient, and the physiological signals comprise electrocardiosignals and non-invasive arterial blood pressure signals; and the data processing module is used for preprocessing and segmenting the coronary artery computed tomography angiography image. Electrocardiogram and non-invasive arterial blood pressure signals are collected through the sensor, risks and complications caused by invasive examination of traditional coronary artery radiography are avoided, the problem that patients unsuitable for invasive examination such as contrast agent allergy and renal insufficiency are difficult to evaluate is solved, the pain of the patients is relieved, and the acceptability of the patients is improved.

The invention relates to the technical field of blood vessel stenosis grading, in particular to a blood vessel stenosis degree grading system based on coronary angiography images. The method comprises the following steps: dividing a vascular skeleton line of an angiography image into vascular segments based on a bifurcation point, and determining a stenosis point on the vascular segment according to the blood flow velocity difference of the vascular segment at the front edge position of a contrast agent in each frame of angiography image and a neighborhood angiography image; according to the blood vessel diameter difference between each bifurcation point and the other bifurcation points and the distance between the other bifurcation points and the main blood vessel section, a reference adaptation degree is obtained, a reference point of each bifurcation point is selected, and according to the blood flow speed difference between each bifurcation point and the branch blood vessel section of the reference point and the reference adaptation degree, narrow points in the bifurcation points are screened; and grading the blood vessel stenosis degree based on the blood vessel diameter of the stenosis point. The blood vessel skeleton line is divided into two conditions of the blood vessel segment and the bifurcation point to analyze the blood vessel physiological stenosis position, and the accuracy of positioning the pathological stenosis position in the coronary artery is improved.

The application discloses a kind of iron agent enhanced whole body blood vessel multi-modal magnetic resonance imaging method, system, the method comprises: after target is divided into multiple scanning stations from head to foot, iron agent enhanced whole body blood vessel bright blood imaging is carried out;Automatic positioning whole body blood vessel stenosis and quantifying its stenosis degree, and output the scanning station and position range information of blood vessel stenosis region;Judge whether there is blood vessel stenosis region meeting condition;According to its spatial position information, guide MRI system examination bed to move for blood vessel stenosis region meeting condition, and carry out local black blood imaging with not less than the spatial range of blood vessel stenosis region;Blood vessel stenosis region exists in interval period and carries out local bright blood imaging to the scanning station, obtains blood vessel cavity structure image;Local bright blood imaging is registered with whole body blood vessel bright blood imaging, and the information transmission of blood vessel stenosis region is completed, and local inflammation imaging is carried out.The application realizes the multi-modal imaging evaluation of whole body blood vessel in clinically acceptable scanning time.

The application discloses an old person free breathing coronary artery scanning image lesion analysis system and relates to the technical field of scanning image lesion analysis. In order to solve the problem that the accurate condition of a patient cannot be obtained according to a scanning image. The application adopts a diameter method, an area method and a contrast agent filling condition to judge the stenosis degree and the hemodynamic change, analyzes the lesion from the morphological and functional double angles, provides comprehensive information for clinical decision-making, identifies the R wave peak value and divides the cardiac phase, matches the respiratory signal and the projection data in time, can accurately capture the characteristics of the heart in different motion states and the respiratory stage, effectively avoids the interference of the artifacts caused by the heart beat and the respiratory motion, makes the reconstructed image clearer and more accurate, sets the CT and the injector parameters in sequence from the scanning type confirmation, each link is closely related and the target is clear, improves the work efficiency, and is convenient for quality control and process management.

The invention relates to the technical field of image processing, in particular to a coronary artery angiography image blood vessel stenosis degree intelligent grading system which is used for obtaining angiography images of a coronary artery at multiple visual angles and the image quality score of each angiography image. Identifying a narrow point in each contrast image, and determining the single-view-angle narrow degree of the narrow point in each contrast image; matching the narrow points in the contrast images of different visual angles to obtain a matched narrow point of each narrow point in the contrast image of each visual angle in the contrast images of other visual angles; determining the final stenosis of each actual stenosis point in the coronary artery based on the single-view stenosis of each stenosis point, the single-view stenosis of the matched stenosis point of each stenosis point and the image quality score of each contrast image; and evaluating the stenosis level of each actual stenosis point based on the final stenosis degree of each actual stenosis point. The system can improve the accuracy, objectivity and reliability of narrowness measurement.

The present application relates to the technical field of blood vessel stenosis grading, and particularly relates to a blood vessel stenosis degree grading system based on coronary angiography images. The present application divides the blood vessel skeleton line of the angiography images into blood vessel segments based on bifurcation points, determines the stenosis points on the blood vessel segments according to the blood flow velocity difference of the contrast agent front position of the blood vessel segments in each frame of the angiography images and its neighboring angiography images, obtains the reference adaptation degree according to the blood vessel diameter difference of each bifurcation point and the rest bifurcation points, and the distance between the rest bifurcation points and the main blood vessel segment, selects the reference point of each bifurcation point, screens the stenosis points in the bifurcation points according to the blood flow velocity difference of the branch blood vessel segments of each bifurcation point and its reference point and the reference adaptation degree, and grades the blood vessel stenosis degree based on the blood vessel diameter of the stenosis points. The present application divides the blood vessel skeleton line into blood vessel segments and bifurcation points to analyze the physiological stenosis position of the blood vessel, and improves the accuracy of positioning the pathological stenosis position in the coronary artery.

This invention relates to the field of intelligent medical technology, and more particularly to a method for early warning of intestinal fibrosis in Crohn's disease based on a large model. The method includes: acquiring intestinal MRI image sequences of Crohn's disease patients; inputting the intestinal MRI image sequences into a pre-trained large model for intestinal structure analysis to locate the initial intestinal fibrosis region; determining whether the degree of intestinal luminal stenosis in Crohn's disease patients meets the standard based on the intestinal stenosis progression index, and adjusting the feature contrast threshold of the large model based on the difference; determining whether the acute inflammation masking effect of Crohn's disease patients is adequate based on intestinal wall edema characterization parameters, and adjusting the T2-weighted imaging modal weights of the large model based on the relative difference; and determining whether the patency of the fibrotic permeation channel in Crohn's disease patients is adequate based on the fibrotic permeation resistivity, and adjusting the boundary loss weights of the large model based on the ratio. This invention improves the accuracy of early warning of intestinal fibrosis in Crohn's disease.

The invention provides a method for predicting coronary artery stenosis degree based on a tongue picture of a patient with coronary heart disease. The method comprises the steps of obtaining a data set; the data set comprises electronic medical record information, blood examination indexes, cardiac ultrasound indexes, electrocardiograms and tongue pictures of the coronary heart disease patient; dividing the preprocessed data set into a training set and a test set according to a preset proportion; building a deep learning network model; inputting the training set into the deep learning network model for training and optimization, and storing the final optimized deep learning network model; and inputting the test set into the finally optimized deep learning network model to obtain a prediction result of the coronary artery stenosis degree of the coronary heart disease patient. According to the method, the coronary artery stenosis degree is judged according to the tongue picture, the electrocardiogram, the cardiac ultrasound index and the blood examination index of the patient with the coronary heart disease, tongue picture features extracted from the tongue picture are converted into quantifiable coronary artery stenosis noninvasive screening targets, and traditional Chinese and western medicine combined diagnosis and treatment intellectualization is promoted.

The invention discloses a coronary heart disease noninvasive early warning system and method based on electrocardiogram and heart sound time sequence parameter fusion, and relates to the technical field of computer medical assistance. The method is used for solving the problems of inaccurate parameter measurement and insufficient risk assessment precision caused by signal time sequence asynchronization in coronary heart disease screening. The method comprises the following steps: firstly, synchronously collecting electrocardio and heart sound signals in a stepped exercise load process, extracting morphological characteristics of a ventricular depolarization wave group and a first heart sound, and realizing accurate time sequence alignment by constructing a cost matrix and dynamic planning; then, coronary artery function parameters are extracted from the alignment signals, and dynamic trend indexes are calculated; constructing a graph structure by using the dynamic trend indexes, fusing spatial-temporal features by using graph convolution and time convolution, and outputting risk levels of different stenosis degrees; and finally, an early warning index is generated through abnormal index extraction and back propagation optimization, and early monitoring and dynamic evaluation of the coronary heart disease risk are realized.

The invention belongs to the crossing field of biomedical engineering and computer data processing, particularly relates to the technical field of ultrasonic medical data processing, and discloses a computer implementation method and system for evaluating the coronary artery stenosis degree based on data. Collected echocardiogram data is used as a core analysis object, target coronary blood vessel positioning processing is carried out on color Doppler blood flow imaging data through a computer, and computer-readable multi-dimensional clinical data and a preset coronary flow velocity influence factor database are fused; and performing dynamic optimization of a linear regression equation and subject working characteristic curve analysis by a computer to realize positioning correlation and quantitative calculation of related degree parameters of coronary artery stenosis. Through database dynamic iteration and parameter correction, interference of individual differences on a processing result is effectively avoided, a brand new computer technical scheme is provided for noninvasive accurate analysis of coronary artery disease related data, and the safety and universality of data processing are remarkably improved.

The present application relates to the technical field of medical data analysis, and particularly relates to a coronary artery analysis processing method and system based on magnetic resonance CNR value, which comprises the following steps: S1: collecting coronary image data, obtaining pretreatment data after pretreating the collected coronary image data, inputting the pretreatment data into an ROI identification model to obtain a target ROI; S2: using an MRCA method based on the target ROI to obtain an MRCA image of the target ROI, and using a final dynamic adaptability formula to obtain a CNR value of a correlation reference region in the MRCA image. The present application improves the efficiency and accuracy of target region extraction by using the image enhancement and region extraction technology combining the edge preserving normal gradient vector flow with the active contour model, and improves the work efficiency of radiologists by using the method combining the MRCA image and the CNR value to diagnose the coronary artery stenosis degree.

Disclosed is an electrode of a shock wave balloon apparatus, comprising a first electrode set and a wire for electrically connecting the first electrode set. The first electrode set comprises a first electrode, a second electrode, and a third electrode that are sequentially arranged apart side by side. The wire is further electrically connected to an electrode control module to form a shock wave between the first electrode and the second electrode and a shock wave between the second electrode and the third electrode. Embodiments of the present invention use the electrode of the shock wave balloon apparatus composed of the first electrode, the second electrode, and the third electrode that are arranged apart side by side. The shock wave gap is formed by the arrangement of an axial distance of the electrodes, thereby greatly reducing the contour of the folded balloon and avoiding the issue that the radial gap arrangement used in existing shock wave balloon apparatuses causes the contour of the folded balloon to be too large to pass through a lesion with a large degree of stenosis. The present invention is ingeniously designed.

The present application relates to the technical field of blood vessel image processing, and particularly relates to a coronary artery stenosis functional evaluation method based on angiography imageomics. According to the characteristic quantitative value of the stenosis degree of each patient in the multi-dimensional image performance characteristics, the stenosis grade of each patient is obtained. According to the distribution of the characteristic quantitative value of the patient in different image performance characteristics in different stenosis grades, the key possibility of the current patient in each image performance characteristic is obtained, and the image performance key characteristics are screened out. According to the distribution of the characteristic quantitative value of each image performance key characteristic of different patients in the stenosis grade where the current patient is located, the coronary artery quality contribution degree of each image performance key characteristic in the stenosis grade where the current patient is located is obtained. The coronary artery stenosis evaluation score of the stenosis grade where the current patient is located is obtained. The present application can accurately obtain the coronary artery stenosis evaluation score of the stenosis grade where the current patient is located, and improve the accuracy of the stenosis functional evaluation.

The invention discloses a manufacturing method and application of a minimally invasive stent for chronic appendicitis, and relates to the technical field of medical instruments. According to the manufacturing method and application of the minimally invasive stent for chronic appendicitis, nickel-titanium shape memory alloy serves as a base material of the stent, and the manufacturing method comprises the steps that S1, a nickel-titanium alloy base material is prepared, specifically, a nickel-titanium shape memory alloy wire with the nickel content being 54-56 at% and the titanium content being 44-46 at% is selected, internal stress is removed through vacuum annealing treatment, the annealing temperature is 500-550 DEG C, the heat preservation time is 1-2 h, and the nickel-titanium alloy base material is prepared; and cooling to room temperature for later use. According to the manufacturing method and application of the minimally invasive stent for chronic appendicitis, nickel-titanium shape memory alloy is adopted, precise switching of the diameter of the stent between 3.5 mm (supporting state) and 0.8 mm (contraction state) is achieved through temperature response, the stent adapts to appendix lumens with different stenosis degrees, and the stent has flexibility and can adapt to an appendix bending structure, so that the stent has good application prospects. The problems that an existing alternative support is fixed in size and poor in adaptability are solved.

The present application relates to the technical field of image data processing, in particular to a kind of blood vessel stenosis evaluation method based on cardiac angiography image, comprising: obtaining the suspected blood vessel contour region in the cardiac angiography image of patient, segmentation into several suspected blood vessel branch region, to filter out heart blood vessel branch region, according to the change of blood vessel width in heart blood vessel branch region, first type heart blood vessel stenosis region is segmented out, according to the similarity between the end points of the broken skeleton line between heart blood vessel branch region, determine the second type heart blood vessel stenosis region of broken place, linear transformation is carried out to the heart blood vessel stenosis region in cardiac angiography image, obtain cardiac angiography enhancement image, quantitative analysis is carried out to cardiac angiography enhancement image, obtain the stenosis degree of heart blood vessel.The present application improves the accuracy of blood vessel stenosis evaluation by contrast enhancement to unclear heart blood vessel stenosis region.

The invention relates to the technical field of medical image processing, and particularly discloses a coronary CTA stenosis degree intelligent evaluation method based on reinforcement learning, which comprises the following steps: firstly, carrying out preprocessing and blood vessel segmentation on a coronary CTA three-dimensional image to obtain three-dimensional segmentation data containing blood vessel probability distribution; then, constructing a blood vessel tree topological graph based on the segmentation data, and reasoning, identifying and correcting connection errors through a topological relation to obtain an optimized topological structure; iteratively optimizing the segmented data by using the optimized topological structure to obtain a three-dimensional blood vessel structure with correct topology, and calculating a connection matrix and a geometric attribute matrix of a blood vessel tree; and finally, inputting the matrix data into a stenosis evaluation model constructed based on a graph attention network and a recurrent neural network, and outputting a stenosis degree evaluation result of each blood vessel segment.

The application provides an angiography image segmentation method and device, electronic equipment and readable storage medium. The method comprises the following steps: acquiring an angiography image, and inputting the angiography image into a pre-trained neural network model; after each layer of the neural network model performs convolution operation and activation operation on the input of the layer, the output of the layer is obtained through a blood vessel edge information enhancement operation of an edge and connected domain enhancement module; and the neural network model outputs a blood vessel contour segmentation result of the angiography image. By using the proposed edge and connected domain enhancement module and adding it to the neural network model layer by layer, the blood vessel edge information of different feature scales is fused, the segmentation result can better retain the edge information of the blood vessel contour and the connectivity between the contour regions, which is conducive to realizing an accurate and complete blood vessel contour segmentation result and helps to improve the accuracy and time efficiency of evaluating the blood vessel stenosis degree.

The heart valve assessment systems described herein advantageously provide indicators of a heart valve condition, such as a pressure gradient or a valve regurgitation index. The heart valve assessment systems can provide indicators of a heart valve condition during a heart procedure. A pressure gradient indicates a severity or measurement of the narrowing (or stenosis) of a valve by the increase in pressure behind it. A valve regurgitation index indicates a leakiness measurement of a valve.

The invention discloses an intelligent auxiliary diagnosis system and method for unstable angina pectoris based on a biomarker RETN, and belongs to the technical field of biological medicine. According to the present invention, the Olink high-throughput proteomics technology screening finds that the expression of RETN and other proteins in the UA patient serum is up-regulated, and after the ELISA verification, the RETN and other proteins are combined with the clinical conventional indexes, and the machine learning algorithm is adopted to construct the multi-dimensional diagnosis system; the prediction module adopts a double-layer architecture, a base module layer parallelly runs six algorithms to capture a nonlinear relationship, a meta module layer adopts logistic regression secondary modeling, and the generalization ability is improved. The system and the method can realize accurate recognition and dynamic monitoring of UA early lesion, break through the bottleneck of traditional detection, make up for the defect of insufficient specificity, and overcome the limitation of incapability of repeated monitoring. The AUC of the diagnosis model in an independent verification queue reaches 0.842 or above, the accuracy rate exceeds 76%, UA patients can be screened, the coronary artery stenosis degree can be evaluated, and personalized treatment can be assisted.

The invention discloses a system for evaluating a coronary stent inner lumen through photon CT single energy and metal artifact removal, and belongs to the technical field of medical instruments. The invention discloses a system for evaluating a coronary stent inner lumen through photon CT single energy and metal artifact removal. The system comprises a photon CT scanning module, a single energy imaging optimization module, a metal artifact removal module, an intelligent evaluation analysis module and a data storage module. The problem that in the prior art, image blurring is likely to occur, and judgment of the lumen stenosis degree is affected is solved. The method is high in artifact removal accuracy, can truly recover the lumen form, realizes automatic measurement of key parameters, introduces a standard phantom calibration model to calibrate the measurement result, further improves the measurement precision, automatically recognizes common complications based on the measurement parameters and image features, and clarifies the types, positions and degrees of abnormities. The change trend of the condition of the lumen in the stent is visually presented, potential complication risks can be found early, and the occurrence rate of adverse events is reduced.

The invention relates to the technical field of intelligent medicine, in particular to a Crohn disease intestinal fibrosis condition early warning method based on large model driving, which comprises the following steps: acquiring an intestinal MRI image sequence of a Crohn disease patient; inputting the intestinal MRI image sequence into a pre-trained intestinal structure analysis large model, and positioning the position of an initial intestinal fibrosis region; determining whether the intestinal lumen stenosis degree of the patient with the Crohn disease reaches the standard or not based on the intestinal lumen stenosis progress index, and determining a feature comparison threshold value for adjusting the large model according to the difference value; determining whether the acute inflammation shielding effect of the patient with the Crohn disease is qualified or not based on the intestinal wall edema characterization parameters, and determining and adjusting the T2 weighted imaging modal weight of the large model according to the relative difference; and determining whether the fibrosis seepage channel smoothness of the patient with the Crohn disease is qualified or not based on the fibrosis seepage resistivity, and determining and adjusting the boundary loss weight of the large model according to the ratio. The early warning accuracy of the intestinal fibrosis condition of the Crohn's disease is improved.

The invention discloses an electrode of a shock wave balloon device, which comprises a first electrode group and a wire for conducting the first electrode group, and the first electrode group comprises a first electrode, a second electrode and a third electrode which are sequentially arranged side by side at intervals; the wire is further electrically connected with an electrode control module so that shock waves can be formed between the first electrode and the second electrode and between the second electrode and the third electrode respectively. According to the embodiment of the invention, the first electrode, the second electrode and the third electrode which are arranged side by side at intervals are adopted to form the electrodes of the shock wave balloon device, and the shock wave gap is formed by the axial distance of the electrodes, so that the contour of the folded balloon is greatly reduced; the problem that the balloon cannot pass through lesions with large stenosis degree due to the fact that the contour of the folded balloon is too large after the balloon is folded due to radial clearance arrangement adopted by an existing shock wave balloon device is avoided, and the shock wave balloon device is ingenious in design.

This invention discloses an automated method for identifying and quantifying multi-indicator coronary artery stenosis, relating to the field of image recognition. It acquires multi-center coronary angiography image data and preprocesses it to obtain coronary angiography sequences. A keyframe selection algorithm is used to filter target frames from the coronary angiography sequences. The MDP-UNet semantic segmentation model is used to segment the target frames, and the YOLO-CCSA model is used to locate the stenosis lesions. The segmentation results and stenosis lesion detection results are fused, and a stenosis segment quantification algorithm is used to perform multi-indicator quantitative calculation and scale calibration to obtain quantification results. Based on the quantification results, the degree of stenosis is graded and assessed. The method provided by this invention achieves fully automated analysis from keyframe selection to stenosis assessment.

The present application relates to the technical field of image processing, in particular to a kind of cardiovascular angiography image intelligent classification method and system, the method comprises: using each branch point after cardiovascular angiography image vascular skeletonization determines the corresponding blood vessel section of each branch;Using the deviation of the inner diameter of the blood vessel in the target blood vessel section determines its stenosis region and stenosis degree, and then determines its initial abnormality index;The feature registration of multiple frames of cardiovascular angiography is carried out to obtain registration target blood vessel section, and the change of initial abnormality index of registration target blood vessel section before and after the inflow of contrast medium is used to determine its stenosis relief degree;Determine the respective blood vessel improvement response degree of the upstream and downstream branches of the registration target blood vessel section after stenosis relief, and then determine the target stenosis index of the registration target blood vessel section in combination with the stenosis relief degree, and then classify and early warn based on the target stenosis index.Through the technical scheme of the present application, more accurate quantization representation of vascular stenosis and fine classification of vascular stenosis state are realized.

The application discloses a method for comprehensive quantitative evaluation of blood vessel stenosis from angiography images, which comprises image preprocessing, automatic stenosis evaluation and interactive stenosis evaluation modules. The preprocessing module combines TOF, UM, CLAHE and Hessian matrix multi-scale enhancement algorithm to improve image quality and highlight blood vessel structure. The automatic stenosis evaluation module uses a new tracking algorithm to extract blood vessel skeleton and blood vessel segmentation, and uses CV model to extract blood vessel contour, measure diameter and stenosis degree of each part of blood vessel tree and detect stenosis. The interactive stenosis evaluation module selects target blood vessel segment by user, uses the proposed energy function-based tracking algorithm to extract target blood vessel skeleton, uses CV model to extract contour, measures diameter and stenosis degree of each part of blood vessel segment and detects stenosis. The application combines automatic and interactive stenosis evaluation, which can not only automatically detect angiography images in batches but also quantitatively evaluate specific images in detail.

The invention provides a method and system for simulating an operation expected result after a balloon is released at a blood vessel stenosis before an operation, and the method comprises the steps: obtaining a medical image containing a blood vessel before the operation, and obtaining a first segmentation result and a second center line of the blood vessel containing plaques, a blood vessel straightening image, a straightening blood vessel mask and a plaque mask are obtained, a plaque narrow region is determined through connected domain detection, and a region-of-interest mask is obtained based on the plaque narrow region; obtaining an ideal blood vessel diameter based on the interested region mask, and obtaining a target blood vessel diameter of the blood vessel of the interested region under the expected stenosis degree based on the ideal blood vessel diameter and the expected stenosis degree; and by taking each point on the second center line corresponding to the region of interest as a center, constructing a target region mask of which the diameter is the diameter of the target blood vessel point by point. In this way, the blood flow condition of the balloon after the blood vessel of the region of interest is released can be accurately simulated, and the reference size for selecting the balloon is provided for a user.

The invention relates to the technical field of ligation devices, in particular to a standardized mouse aortic arch ligation device which comprises a circular clamp, a connecting assembly and an adjusting assembly, the circular clamp is provided with an open hole, a fixing hole and a mounting groove, the connecting assembly and the circular clamp are integrated and correspond to the fixing hole, and the bottom of the adjusting assembly is connected with the mounting groove in a clamped mode. According to the device, standardized operation is achieved through a clamping type structure, the operation difficulty is reduced, the stenosis degree can be accurately controlled by means of the adjusting assembly, blood vessel injuries are reduced, the model building success rate and the experimental result stability are improved, and the device is suitable for mouse aortic arch constriction model building in cardiovascular disease research.

An apparatus for analyzing a blood vessel includes a processor configured to detect a blood vessel area corresponding to the blood vessel and a stenosis area corresponding to a stenosed portion of the blood vessel, based on a machine learning model, from a blood vessel image, determine a partial area of the blood vessel area to be a comparison area for the stenosis area, based on a length of the stenosis area, and calculate a stenosis score indicating a degree of stenosis of a blood vessel of the stenosis area compared to a blood vessel of the comparison area.

The invention relates to the technical field of medical supports, in particular to a support with adjustable supporting force. In the support, outer-layer frames and inner-layer frames are alternately arranged in the axial direction, and any outer-layer frame and any inner-layer frame are connected through a plurality of connecting rods. When the support is unfolded, the inner-layer frame is unfolded relative to the outer-layer frame, at the moment, the straight part of the support is of a single-layer structure, and the supporting force of the straight part is small; when the support is folded, the inner-layer frame is folded on the inner ring of the outer-layer frame, at the moment, the folding part of the support is of a double-layer structure, and the supporting force of the folding part is large; by controlling the overlapping area of the outer layer frame and the inner layer frame in the folding part, the supporting force of the folding part can be adjusted, so that the supporting force of different parts of the stent can be adjusted, and the stent can meet the requirements of different stenosis degrees of blood vessels for different supporting forces.

The application relates to a myocardial bridge detection device in a coronary angiography sequence, comprising: a coronary vessel segmentation module for segmenting a coronary vessel through a neural network model, the model being composed of a CNN and a transformer structure, and being capable of effectively extracting local information and global information of the vessel; a coronary vessel information calculation module for calculating information including the diameter, length and start-stop coordinates of the vessel according to a vessel segmentation map, establishing a coronary vessel tree containing all the vessels, and calculating matching vessels between the angiography sequences; and a coronary myocardial bridge detection module for realizing the detection of the coronary myocardial bridge by calculating the stenosis degree of the same vessel in the angiography sequence.