51 results about "Stenosis degree" patented technology

The invention relates to the technical field of carotid artery stent simulation, and relates to a carotid artery stent simulation method and system based on a virtual intervention algorithm, and the method comprises the steps: obtaining first image information which comprises carotid artery image information; determining the stenosis degree of the carotid artery and the component index of each plaque area according to the first image information; constructing a virtual carotid artery blood vessel three-dimensional model according to the stenosis degree of the carotid artery and the component index of each plaque region; obtaining a preset virtual support three-dimensional model; the contact between a preset virtual stent three-dimensional model and a virtual carotid artery blood vessel three-dimensional model is analyzed through a mechanical analysis method, a simulation result is obtained, and the simulation result comprises the unfolded form of the stent in the body of the patient; the dynamic constraint change between the nodes and the mutual influence between the blood vessel stress and the stent expansion can be accurately captured, and the simulation precision is greatly improved, so that the operation safety is improved.

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 medical instruments, in particular to an internal fistula blood vessel detection system for hemodialysis, which comprises a data acquisition module for acquiring ultrasonic data, an impedance value, tremor intensity and a pressure spectrum; the mode determining module is used for determining a detection mode of the internal fistula blood vessel based on the tortuosity index and determining massage frequency and air bag pressure according to the blood vessel compliance index; the stenosis analysis module is used for determining whether the internal fistula blood vessel is stenosis or not based on the stenosis risk characterization parameter and determining whether the stenosis degree of the internal fistula blood vessel is in a reasonable range or not according to the blood flow velocity characterization value; the frequency adjusting module determines and adjusts the massage frequency based on the ratio; the injury analysis module is used for determining whether the internal fistula blood vessel is injured or not in the dialysis process on the basis of the bioelectrical impedance change rate and determining whether the internal fistula blood vessel injury is in a reasonable range or not after dialysis according to the tremor intensity change value; the pressure adjusting module determines and adjusts the pressure of the air bag based on the difference value. The accuracy of internal fistula blood vessel detection is improved.

The invention relates to the technical field of image processing, in particular to a cerebrovascular image recognition and analysis system based on deep learning. The system comprises a cerebrovascular image registration module, a CT pixel communication module, a lesion area identification and segmentation module and a lesion risk assessment module, and can obtain a cerebrovascular CT angiography image, perform image enhancement and multi-modal image registration, and generate a cerebrovascular CT multi-modal registration image sequence; acquiring cerebrovascular CT pixel point clouds in the corresponding modals and performing blood vessel cluster connectivity analysis to generate corresponding cerebrovascular CT pixel cluster connectivity graphs in the respective modals; and based on a deep learning multi-branch network architecture, introducing an attention mechanism to carry out lesion region identification and segmentation, obtaining a corresponding cerebrovascular stenosis degree and a lesion thrombus area, and carrying out lesion risk number evaluation on the corresponding cerebrovascular CT image lesion region to obtain a cerebrovascular CT lesion risk number. According to the invention, accurate recognition of the cerebral vascular image lesion condition can be realized.

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 invention provides a target detection-based blood vessel feature analysis method and device, equipment and a medium, and the method comprises the steps: carrying out the processing of an input blood vessel image through an initialized feature extraction module, generating a pre-training model, carrying out the feature extraction of the blood vessel image through the pre-training model, and obtaining image feature data; inputting the lesion area into an analysis module, and performing image segmentation on the lesion area through the analysis module to obtain segmented image features; the segmented image features comprise image data of a calcified region and / or a non-calcified region; and respectively inputting the segmented image features into a calcified plaque analysis module and a non-calcified plaque analysis module to obtain the type of a vascular lesion area, further analyzing the density distribution of mixed plaques to determine whether the mixed plaques are in a splicing type or a uniform type, and finally generating an analysis result containing the lesion type and the stenosis degree. According to the invention, accurate positioning, segmentation and classification of vasculopathy can be realized without labeling data, and the automation level and accuracy of vasculopathy diagnosis are improved.

The present invention belongs to the field of hemodialysis monitoring equipment, and provides a hemodialysis patient arteriovenous fistula function monitoring system and terminal equipment. The system comprises: an acquisition module, which is used to obtain the intensity of reflected light returned after detection light is irradiated to blood vessels under the skin of a human forearm, generate a current signal according to the reflected light, and convert the current signal into a measurement signal; a feature extraction module, which is used to obtain an intensity change sequence of the reflected light based on the measurement signal, separate a DC component and an AC component from the intensity change sequence, and obtain a blood perfusion index, a heart rate, and blood oxygen saturation, systolic pressure and diastolic pressure; a function evaluation module, which uses the maximum and minimum values ​​of the blood perfusion index, blood oxygen saturation, systolic pressure and diastolic pressure as first features, and adds heart rate as a second feature, and obtains the stenosis degree and blood flow of the blood vessel based on a machine learning classifier evaluation; the present invention can solve the problem in the prior art that the function of the arteriovenous fistula of a hemodialysis patient is difficult to intuitively monitor.

The invention relates to the technical field of automatic image analysis, and provides an automatic evaluation system and method for laryngotracheal stenosis, and the system comprises a laryngotracheal segmentation module which is configured to carry out the laryngotracheal region segmentation of an obtained CT image, and generates a laryngotracheal segmentation mask; the laryngotracheal reconstruction module is configured to extract a laryngotracheal center line by adopting a method of combining morphological processing and curve optimization based on the segmentation mask and generate a three-dimensional straightened image; the feature extraction module is configured to extract depth features and geometric features from the straightened image and fuse the depth features and the geometric features; and the stenosis degree evaluation module is configured to predict the stenosis degree of the laryngeal trachea through a deep learning network based on the fusion features to obtain a detection result. According to the system disclosed by the invention, the laryngotracheal region can be accurately extracted, and the stenosis degree of each layer is accurately calculated in combination with an airway straightening method, so that automatic and accurate stenosis grading is provided.

The invention provides a coronary artery stenosis degree identification method and system based on electrocardio and heart sound signals, and belongs to the technical field of physiological signal analysis. The method comprises the steps that electrocardiosignals and heart sound signals which are to be recognized and synchronously collected are obtained and preprocessed; respectively extracting an amplitude sequence and an interval sequence of the preprocessed electrocardiosignal and heart sound signal; extracting multi-domain features and graphical features based on the amplitude sequence and the interval sequence, and constructing a joint feature set; and inputting the joint feature set into a trained recognition model to obtain a coronary artery stenosis degree recognition result. According to the coronary artery stenosis degree evaluation method, the performance of coronary artery stenosis on two sequences of electrocardio and heart sound signals, amplitude intensity and interval rhythm is fully considered, multi-domain features such as time domain, frequency domain and nonlinearity and detail texture features of a graphical method are extracted, an accurate identification and classification result is obtained, and the coronary artery stenosis degree is accurately evaluated.

The invention relates to the technical field of medical data analysis, in particular to a coronary artery analysis processing method and system based on a magnetic resonance CNR value, and the method comprises the following steps: S1, collecting coronary artery image data, carrying out the preprocessing of the collected coronary artery image data, obtaining the preprocessing data, inputting the preprocessing data into an ROI recognition model, and obtaining a target ROI; and S2, based on the target ROI, using an MRCA method to obtain an MRCA image of the target ROI, and using a final dynamic adaptability formula to obtain a CNR value of an associated reference region in the MRCA image. According to the method, the efficiency and the precision of target region extraction are improved by adopting the image enhancement and region extraction technology combining the edge-preserving normal gradient vector flow and the active contour model, the coronary artery stenosis degree is diagnosed by using the method combining the MRCA image and the CNR value, and the working efficiency of imaging doctors 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 invention discloses an automatic coronary artery Syntab scoring method and system based on coronary artery CTA, and relates to the technical field of medical image processing and intelligent diagnosis, and the method comprises the steps: obtaining the image data of a patient, and uploading the image data to a coronary artery automatic processing platform; the coronary artery automatic processing platform automatically identifies coronary artery advantage types; coronary artery segments are identified, and segment weight factors are calculated; automatically identifying plaque segments, and determining a stenosis degree multiplication factor; identifying undesirable feature factors of the plaques, and recording scores; calculating a lesion score based on the segment weight factor and the stenosis degree multiplication factor; a total score is calculated based on the lesion score and the score of the plaque undesirable feature factor. According to the method, the computer technology and the artificial intelligence algorithm are introduced, the coronary artery anatomical features are combined, the blood flow weight factors are given to different blood vessel segments, the actual influence of lesions on the health of the patient is scientifically quantified, the limitation that a traditional method is only based on simple classification scoring is overcome, and the scoring accuracy and individuation degree are remarkably improved.

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.

The invention relates to the technical field of esophagus detection, in particular to an intelligent detection method and system for esophageal early cancer ESD postoperative stenosis, and the method comprises the steps: obtaining a postoperative endoscopic image set of a patient, carrying out the sharpening processing, and outputting a clear image; according to the clear image, the postoperative stenosis trend degree of the patient is calculated, and when the stenosis trend degree is larger than a preset threshold value, subsequent steps are carried out; performing narrow edge detection on the clear image, calculating a narrow rate, establishing a three-dimensional model of a narrow area, and calculating a narrow inner diameter curve equation; and based on the curvilinear equation of the stenosis rate and the stenosis inner diameter, performing stenosis degree grading and morphological characteristic analysis.

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 invention relates to the technical field of image data processing, in particular to an angiocardiography image-based vascular stenosis assessment method, which comprises the following steps of: acquiring a suspected blood vessel contour region in an angiocardiography image of a patient, and segmenting the suspected blood vessel contour region into a plurality of suspected blood vessel branch regions so as to screen out angiocardiography branch regions; according to the change of the blood vessel width in the cardiac blood vessel branch areas, segmenting a first type of cardiac blood vessel stenosis areas, and according to the similarity between the end points of the disconnected skeleton lines between the cardiac blood vessel branch areas, determining a second type of cardiac blood vessel stenosis areas at the disconnected positions, and performing linear transformation on the cardiac vessel stenosis area in the cardiac angiography image to obtain a cardiac angiography enhanced image, and performing quantitative analysis on the cardiac angiography enhanced image to obtain the stenosis degree of the cardiac vessel. According to the invention, contrast enhancement is carried out on an unclear heart blood vessel stenosis area, so that the accuracy of blood vessel stenosis evaluation is improved.

The present invention discloses an optical probe for detecting and imaging aspartate aminopeptidase activity and its application. The optical probe is obtained by condensing a hemicyanine contrast agent with a sulfonic acid group and a polypeptide chain that can be specifically recognized by DNPEP. The optical probe can be used for dynamically detecting and imaging DNPEP activity in mouse plaques and DNPEP activity in biological samples. The optical probe has the advantage of being able to accurately detect aspartyl aminopeptidase activity in atherosclerotic plaques, providing strong data support for predicting plaque vulnerability, and improving the accuracy of plaque vulnerability when judging plaque vulnerability in conjunction with the degree of stenosis of the imaged blood vessels.

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.