49 results about "Coronary stenosis" patented technology

The present disclosure provides a method for correcting a resting blood flow velocity on the basis of an interval time between angiogram images, comprising: acquiring, in an angiography state, an average blood flow velocity Vh from a coronary artery inlet to a distal end of a coronary artery stenosis (S100); acquiring a time difference Δt between start times of two adjacent bolus injections of contrast agent (S200); obtaining a correction coefficient K according to the time difference Δt (S300); obtaining a resting blood flow velocity Vj according to the correction coefficient K and the average blood flow velocity Vh (S400), as well as an apparatus configured for implementing the above method. The disclosure obtains the resting blood flow velocity Vj according to the correction coefficient K and the average blood flow velocity Vh.

The invention relates to the field of medical image processing, and discloses a coronary artery stenosis detection method and system based on an angiography image. The method comprises the following implementation steps: collecting a coronary sequence video, sampling and labeling blood vessel frames in the coronary sequence video under the guidance of a professional doctor, and obtaining a coronary angiography image set and a coronary angiography background data set; using a fine-tuned diffusion model DDPM to extract a potential feature map of the blood vessel, and performing channel remodeling on the potential feature map and an original coronary artery image; performing dimension feature extraction on the coronary angiography image, and extracting features of a corresponding layer in the backbone network; then, narrow feature enhancement and multi-scale Transform operation are carried out on the extracted feature mapping; and finally carrying out feature fusion on the feature mapping of different layers and then sending to a detection head module for prediction. According to the method, the detection effect of coronary artery stenosis can be improved, a foundation is established for subsequent quantitative detection of vascular stenosis, and the method has a great application prospect.

The invention relates to the technical field of medical information, in particular to a coronary artery stenosis intelligent diagnosis system and method based on deep learning, and realizes efficient and accurate diagnosis through data acquisition, marking, convolutional neural network model construction, evaluation and acceleration modules. The system preprocesses the contrast image, marks the narrow position and degree, constructs a convolutional neural network model combined with an attention degree graph, and optimizes parameters to improve the accuracy; through parallel processing, the diagnosis time is shortened, the problems of an existing diagnosis method are systematically and innovatively solved, and the method has high accuracy, high efficiency and good interpretability and is expected to become an important clinical auxiliary tool for early screening and emergency diagnosis of the coronary heart disease.

The invention discloses a liver transplantation perioperative concurrent myocardial ischemia risk assessment method and system, and the method comprises the steps: obtaining coronary artery image data of a liver transplantation patient, carrying out the three-dimensional reconstruction according to the coronary artery image data, and generating a personalized coronary artery geometric model; based on the geometric model of the coronary artery, performing numerical simulation on the flowing condition of blood flow in the coronary artery by utilizing a computational fluid mechanics method, obtaining the speed and pressure distribution of the blood flow of the coronary artery through the numerical simulation, and calculating the pressure ratio before and after the coronary artery stenosis; evaluating the coronary ischemia condition according to the pressure ratio, and generating a fractional flow reserve value and a myocardial ischemia risk evaluation report. The coronary artery blood flow reserve score value is calculated by simulating the coronary artery blood flow conditions under different physiological conditions, so that the myocardial ischemia risk possibly faced by a patient during an operation is comprehensively evaluated, and intra-operation decision support is provided for clinicians.

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 provides a calcification section coronary artery risk prediction method and device based on chest CT (Computed Tomography) plain scanning. The method comprises the following steps: firstly, acquiring and screening chest CT (Computed Tomography) plain-scan image data; secondly, performing calcification section coronary artery labeling on the image data; thirdly, performing CCTA image interpretation and grouping on the marked areas; and finally, collecting a CT image and a classification label model corresponding to the CT image, using a ResTransNet network to extract features from the calcification section coronary artery label, training the ResTransNet model, and predicting a risk state. According to the method, the deep learning method is applied to the coronary artery of the calcification section of chest CT flat scanning, the plaque type and the blood vessel stenosis degree are accurately predicted, and effective information is provided for subclinical coronary atherosclerosis risk screening and prevention; constructing plaque types and coronary artery stenosis degrees gt; and the prediction device has great clinical significance when being used for screening the risk of subclinical coronary atherosclerosis.

The invention belongs to the field of biological medicine industry, and discloses a peripheral blood marker and system for identifying the severity of viral myocarditis or coronary artery stenosis. The inventor finds for the first time that the average value of LRG1 in blood is not obviously changed compared with that of a normal person when mild and moderate myocarditis and microcirculation angina pectoris with normal coronary artery or coronary artery stenosis smaller than 10% exist, and the blood LRG1 is obviously increased when the coronary artery stenosis of the coronary heart disease exceeds 70%; when severe myocarditis occurs, the blood LRG1 is also obviously increased, so that the increase of the blood LRG1 is a blood marker for patients with severe myocarditis or severe coronary artery lesion, and the blank that blood indexes are lacked in the prior art to prompt the patients to have severe coronary artery stenosis is filled. By detecting the LRG1 content of the peripheral blood and combining with other conventional examinations, the severity of myocarditis or coronary stenosis can be simply, accurately and quickly distinguished, and a corresponding basis is provided for subsequent accurate treatment.

The invention discloses a coronary artery stenosis detection method based on coronary angiography and space-time collaborative learning, and belongs to the field of intelligent medical treatment. According to the method, through a deep feature extraction network based on a channel shuffling structure re-parameterization convolution single aggregation module, through channel segmentation and multi-branch training, feature multiplexing and information flowing are enhanced, a double-flow path is introduced into a feature fusion network to realize adaptive weighted fusion of features of different scales, and the feature fusion efficiency is improved. The representation of spatial features is improved; a space-time collaborative analysis module capable of fusing time information and local space information at the same time is adopted, and blood vessel shape deformation and hemodynamic information in a space-time sequence are effectively captured and effective feature information is enhanced by constructing time sequence connection and fusing a space-time feature graph, so that the detection accuracy and robustness of coronary artery stenosis lesions are improved. According to the method, the time and space information of the coronary angiography sequence image is comprehensively considered, the number of false positive detection is reduced, and the detection capability of the model coronary artery stenosis lesion is comprehensively improved.

Methods and systems for the diagnosis and selection for treatment of a coronary artery stenosis based on a multi-level perfusion model of the cardiovascular system are disclosed. The method further includes estimating an occluded myocardial perfusion using the multi-level perfusion model, modifying the 3D epicardial mesh to represent the treatment of the stenosis, estimating a post-treatment myocardial perfusion using the modified multi-level cardiac perfusion model, estimating an absolute perfusion reserve (APR) based on the estimated occluded myocardial perfusion and post-treatment myocardial perfusion, and selecting a treatment for the subject if the APR is greater than a threshold value.

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 discloses a dynamic quantization recognition method for coronary stenosis based on a diffusion model and YOLO hypergraph optimization, and provides a self-supervised blood vessel segmentation method for solving the problems that manual annotation of a blood vessel mask is challenging and needs to consume a large number of resources due to the fact that blood vessel branches are fine and the structure is complex. The method comprises the following steps of: performing blood vessel segmentation by adopting a diffusion adversarial representation learning (DARL) model based on Transform; and aiming at the limitation of a traditional YOLO model in the aspect of feature fusion, difficulty in fully capturing a cross-level and cross-position complex feature relationship and incapability of providing accurate information of a narrow position, a Hyper-YOLO algorithm for locally enhancing attention is provided, so that blood vessel stenosis lesion identification can be effectively realized, and a good identification effect is achieved.

The invention relates to the technical field of medical care, in particular to a cardiovascular disease risk dynamic early warning system based on multi-source data fusion, and the system comprises the steps: obtaining ejection fraction, coronary artery stenosis percentage and calcification integral data of cardiovascular examination of a monitored object, and heart rate data and pulse pressure difference data of periodic signs; determining a physical examination abnormal coefficient according to the difference between the ejection fraction and the standard value and the difference between the calcification integral data and the standard value and the coronary artery stenosis percentage; according to the height and slope difference of the ST wave band and the PR wave band, the heart rate analysis abnormal index is determined by combining the time interval of the adjacent electric activity wave bands; according to the time sequence change correlation of the heart rate data and the pulse pressure difference data, a physical sign abnormal coefficient is determined; determining a dynamic risk score in combination with the physical examination abnormal coefficient and the physical sign abnormal coefficient; and carrying out risk early warning based on the dynamic risk score. According to the invention, fusion analysis of multi-source and multi-dimensional data can be realized, and the dynamic early warning precision of cardiovascular risks is improved.

The invention provides a coronary artery stenosis severity determination method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring actual blood fat parameters including low-density lipoprotein cholesterol of a target patient; obtaining clinical features, including identity information and past medical history, of the target patient; inputting the actual blood fat parameters and the clinical features as input information into a coronary artery stenosis severity prediction model obtained through pre-training, the coronary artery stenosis severity prediction model is obtained by training a training sample formed by sample input which is taken as actual blood fat parameters and clinical features of historical patients and sample output which is taken as a corresponding real coronary artery stenosis score, and the coronary artery stenosis severity prediction model adopts a model framework of an interpretable enhancement machine; receiving an actual coronary artery stenosis score and score interpretation information output by the coronary artery stenosis severity prediction model; and based on the actual coronary artery stenosis score and the score interpretation information, determining the actual coronary artery stenosis severity of the target patient. By applying the method, the correlation between the low-density lipoprotein cholesterol and the coronary artery stenosis severity can be more accurately determined, and the result has higher interpretability.

The application discloses a blade cutting balloon suitable for coronary artery stenosis and relates to the technical field of cutting balloons. The balloon body is internally provided with a plurality of groups of cutting blades which are slidably connected to the inner wall of the balloon body and extend to the outside of the balloon body, and the balloon body is internally provided with a fixed pipe which is fixedly connected to the inner wall of the balloon body and is internally provided with a movable block.

This invention discloses a method for predicting coronary artery stenosis wall shear stress based on graph neural networks, comprising the following steps: S1, constructing a graph neural network model based on physical constraints; S2, training the constructed graph neural network model; S3, using the trained graph neural network model to predict the coronary artery stenosis wall shear stress and outputting the prediction result; wherein, in step S2, the overall loss function constrained by the physical constraint function is used to train the model during the training process of the graph neural network model. This invention achieves high-speed and high-precision prediction of WSS distributions for complex or even unseen geometries (such as eccentricity, recurrent / multiple lesions, elliptical cross sections and their combinations) by learning from large-scale samples without explicit CFD solutions.

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 embodiment of the present invention discloses a coronary artery stenosis rate determination system and storage medium, the system includes a processor, the processor is configured to execute the following method: coarse segmentation of a coronary artery image to obtain a coarse segmentation result of the coronary artery; fine segmentation of the coarse segmentation result based on a trained fine segmentation model to obtain a fine segmentation result of the coronary artery, and filling the fine segmentation result back into the coronary artery image to obtain a coronary artery segmentation result; inputting the coronary artery image into a trained plaque detection model to obtain a plaque prediction result, and filling the plaque prediction result back into the coronary artery image to obtain a plaque segmentation result; and determining the coronary artery stenosis rate according to the coronary artery image, the coronary artery segmentation result and the plaque segmentation result. The problem of long data processing time in the existing coronary artery stenosis rate determination method is solved.

The invention discloses an intelligent assessment method and system for coronary stenosis based on chest radiography, and relates to the technical field of health risk assessment, and the method comprises the steps: obtaining chest radiography image data of a target patient; based on the time sequence fluctuation of the calcification focus area in the chest radiograph image data, calculating to obtain a calcification activity index, determining a cardiac pulsation key point in the chest radiograph image data, and based on the displacement change of the cardiac pulsation key point between each image frame, calculating to obtain a collateral circulation compensation index of the calcification focus area; based on the collateral circulation compensation index and the calcification activity index, calculating a dynamic risk factor corresponding to the coronary artery stenosis; and inputting the dynamic risk factor into a preset neural network model for iterative training, and performing prediction processing on the chest radiograph image data based on the trained preset neural network model to obtain a coronary artery stenosis risk assessment result. The technical effect of reducing the clinical misjudgment risk of coronary artery stenosis is achieved.

This specification provides a cardiac ultrasound coronary artery stenosis diagnostic system and method based on a visual language transfer model. The system includes: a database construction module for standardizing multi-view cardiac ultrasound video sequences and radiological reports; a cardiac region of interest (ROI) standardization and labeling module for segmenting myocardial segments, obtaining ROI data, and generating a coronary artery-specific perfusion region mask as the ROI for vascular-level stenosis prediction; a visual language transfer pre-training module for semantic alignment of video data and report text using a visual language transfer model; and a downstream task prediction module for utilizing the enhanced attention mechanism of the coronary artery-specific perfusion region mask, based on the global visual features output by the visual language transfer pre-training module, to output patient-level classification results for obstructive coronary artery disease and vascular-level stenosis prediction results for LAD, LCX, and RCA.

The application discloses a noninvasive calculation method of coronary microcirculation resistance and resistance index, and the method comprises the following steps: firstly, based on coronary computer tomography angiography data, blood vessel segmentation is carried out by using a U-Net convolutional neural network, and then a personalized 3D coronary stenosis branch vessel model is reconstructed; secondly, the information of multiple blood vessels (physiological parameters such as age, blood pressure and left ventricular volume in the systolic phase) is integrated, and the length, cross-sectional area, volume and other geometric structure parameters of a specific part of the blood vessel are measured and calculated; thirdly, fluid dynamics simulation is utilized to obtain the invasive CMR and IMR; fourthly, correlation analysis is carried out between the characteristic parameters and the CMR and IMR; fifthly, the characteristic parameters with high correlation are selected to carry out machine learning prediction, so as to realize noninvasive calculation of the CMR and IMR. The noninvasive method for evaluating the CMR and IMR can complete measurement in a short time and has high precision, and is convenient and safe.

This paper proposes a method for analyzing coronary artery stenosis using DSA (digital subtraction angiography) based on matching measurement. This method combines image preprocessing, catheter calibration, vessel segmentation, and centerline extraction on DSA images with an improved MoM (Measure of Match) model to accurately measure vessel diameter based on matching and calculate the stenosis rate of the stenotic area. This method also features an automatic correction function, enabling automated correction of the stenotic area based on the reference diameter of a normal vessel after stenosis detection, thereby improving detection accuracy and stability. This method is suitable for rapid and accurate analysis of coronary artery stenosis, effectively reducing manual operation errors and demonstrating a high degree of automation.

Embodiments of the present application provide a training method, an image segmentation method, a computer-aided diagnosis method and system. The method comprises: obtaining a prediction annotation result and a corresponding sample image, the sample image containing a sample object, the prediction annotation result being obtained from a first prediction segmentation result satisfying a target screening condition, the first prediction segmentation result being output by a first image segmentation model trained to segment the sample object, and the target screening condition being configured based on a training target of a second image segmentation model; inputting the sample image into the second image segmentation model to obtain a second prediction segmentation result output by the second image segmentation model to segment the sample object; and training the second image segmentation model using a loss value between the prediction annotation result and the second prediction segmentation result. Embodiments of the present application can improve the accuracy of coronary artery vessel segmentation of NCCT images and lay a foundation for downstream coronary stenosis detection tasks.

A use of N-lactoyl-phenylalanine (Lac-Phe) in a preparation of a medicament for preventing and / or treating ischemic heart disease is provided. It is the first to demonstrate that Lac-Phe can improve the cardiac function in a mouse model of left anterior descending coronary artery stenosis, promote cardiac angiogenesis, improve the viability of hypoxic cardiomyocytes, and inhibit the expression of inflammatory cytokines in the heart, thereby playing an anti-myocardial ischemic injury role. Therefore, the Lac-Phe can be used to prepare a medicament for treating ischemic heart disease. A novel technical means for the treatment of ischemic heart disease is provided.

The application discloses a kind of based on chest radiograph coronary artery stenosis intelligent evaluation method and system, it is related to health risk assessment technical field, the method includes: obtaining the chest radiograph image data of target patient;Based on the time sequence fluctuation of calcification focus area in chest radiograph image data, the calcification activity index is calculated, determine the heart beat key point in chest radiograph image data, based on the displacement change between each image frame of heart beat key point, the collateral circulation compensation index of calcification focus area is calculated;Based on collateral circulation compensation index and calcification activity index, the dynamic risk factor corresponding to coronary stenosis is calculated;Dynamic risk factor is input to the preset neural network model and is iteratively trained, and based on the preset neural network model after training is completed to chest radiograph image data is predicted to process, obtains coronary stenosis risk assessment result.The application reaches the technical effect of reducing the clinical misjudgment risk of coronary artery stenosis.

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 provides a coronary artery detection method and system fusing FSRConv and Transform in the technical field of medical image processing, and the method comprises the following steps: S1, obtaining a large number of historical CT angiography images of coronary arteries, carrying out the preprocessing of each historical CT angiography image, and constructing a data set; s2, creating a coronary artery stenosis detection model based on a 3D feature space reconstruction module, a self-attention module and an output module; s3, training a coronary artery stenosis detection model through the data set; and S4, detecting the real-time CT contrast image of the coronary artery through the trained coronary artery stenosis detection model. The coronary artery stenosis detection method has the advantage that the coronary artery stenosis detection precision is greatly improved.

A Percutaneous Coronary Intervention (PCI) guidewire and method of use includes at least two pairs of small surface area, closely spaced electrodes spaced axially along the length of the guidewire to allow one electrode pair to be positioned on each side of a coronary stenosis. The electrodes can sense electrical signals from the myocardium immediately adjacent to the electrodes, enabling the guidewire to record and analyze an Intra-Coronary Electrogram (ICEG) and heart pacing, and, therefore, facilitate more accurately diagnosis of an individual patient's stenosis and provide a more patient specific diagnosis of clinical need.

The invention discloses a system for evaluating coronary artery stenosis through a photon CT low-dose low-concentration contrast agent scheme, and belongs to the field of medical images. The invention discloses a system for evaluating coronary artery stenosis by using a photon CT low-dose low-concentration contrast agent scheme. The system comprises a multi-energy spectrum data acquisition unit, a data preprocessing unit, an image generation unit, an image segmentation unit, a plaque analysis unit and a stenosis detection unit. The problems that in existing coronary artery CT examination, the radiation dosage is high, the contrast agent dosage is large, and the diagnosis process highly depends on physician experience, so that efficiency is low, and subjectivity is high are solved, the photon counting CT detector and optimized low-dosage scanning parameters are adopted, the deep learning algorithm and the multi-material decomposition technology are combined, and the CT examination efficiency is improved. High-quality coronary artery imaging and accurate stenosis evaluation under the conditions of low dosage and low concentration of contrast agents are realized, radiation exposure and contrast agent related risks of patients are remarkably reduced, and accuracy and reliability of coronary artery stenosis detection and plaque component analysis are improved.

The embodiment of the present invention relates to a method and device for processing a coronary magnetic resonance imaging reconstruction model, the method comprising: constructing a coronary magnetic resonance imaging reconstruction model; performing model training on the coronary magnetic resonance imaging reconstruction model; after the model training is completed, using a magnetic resonance scanning device to perform a full-heart three-dimensional coronary imaging scan on a first subject according to a configured scanning sequence type, and performing K-space data sampling on the obtained scanning sequence data based on a preset undersampling method during the scanning process to obtain corresponding first undersampling K-space data; and inputting the first undersampling K-space data into the coronary magnetic resonance imaging reconstruction model to perform magnetic resonance imaging image reconstruction and noise reduction processing to obtain a corresponding first imaging image; and performing coronary stenosis feature recognition based on the first imaging image to generate a corresponding first recognition report. The present invention can shorten the duration of coronary magnetic resonance imaging and improve imaging clarity.

The invention discloses a traditional Chinese medicine composition for preventing and treating coronary artery stenosis and / or blockage and application of the traditional Chinese medicine composition. The traditional Chinese medicine composition is prepared from herba ephedrae, radix puerariae, radix aconiti carmichaeli preparata, rhizoma zingiberis, herba asari and the like. Pinellia ternate, radix puerariae and pseudo-ginseng are used as monarch drugs, the pinellia ternate, the pseudo-ginseng and the radix puerariae are used for eliminating dampness and phlegm, promoting blood circulation and removing blood stasis, and the radix puerariae is used for relieving muscles and dredging collaterals, expanding blood vessels and promoting blood circulation, so that phlegm and stagnation in the pulse are removed; radix aconiti lateralis praeparata, rhizoma zingiberis, herba asari and herba ephedrae are used as ministerial drugs for warming kidney yang, dispelling cold, removing dampness, boosting yang qi and promoting qi and blood circulation; the cassia twig has the effect of warming and dredging meridians, assists the ministerial drug in regulating qi and blood circulation and strongly removing blood stasis, and the red date has the effect of tonifying middle-jiao and reducing generated phlegm; and honey-fried licorice root is used as a conductant drug to harmonize stomach and spleen and harmonize the medicines. According to pathogenesis, the traditional Chinese medicine composition is reasonably combined to achieve the effects of warming yang, dispelling cold, activating blood and reducing phlegm, and can improve blood vessel microcirculation in multiple ways to remove turbid phlegm and block in the pulse and effectively treat coronary artery stenosis or blockage.