49 results about "Stenosis degree" patented technology

The invention discloses a coronary artery virtual stent implantation method and system based on haemodynamics analysis. The coronary artery virtual stent implantation method comprises the steps that CTA image data of a patient are read, plaque is identified, a vascular three-dimensional image model is constructed, the plaque is removed, and the position of the plaque is recorded; the physiological parameters of the patient is received, a CFD boundary condition is set, and CFD calculating is conducted; according to a three-dimensional vascular form, a plaque characteristic and FFR values throughout blood vessels, a lesion is identified; according to the identified lesion, a stent specification database and a virtual stent implantation strategy, an implantation scheme is generated, and a new vascular three-dimensional geometric model is generated; under the new three-dimensional geometric model, fluid calculation is conducted again, according to a preset selecting standard, an optimal stent implantation scheme is output. According to the coronary artery virtual stent implantation method and system, blood vessel stenosis degree calculating, stent implantation strategy generating and quantitative evaluating of the virtual stent implantation effect are completed automatically, the operation scheme is planned accurately, the doctor decision efficiency is improved effectively, and the risk of dependence on the artificial judgment is reduced.

A method is proposed that identifies and quantifies stenoses in arteries based on an analysis of Doppler frequency shifts from several heartbeats. It is non-invasive and individual insensitive. Pulsatile flow through a blood vessel with wall roughness and / or variable lumen area generates flow disturbances, which lead to variations in the shape of the Doppler shift frequency spectrum. One or several frequency bands that are affected by these flow disturbances are selected from the overall Doppler shift frequency spectrum. Next, one or several parameters, which characterize the selected frequency bands and vary with the degree of stenosis, are used in a linear function to calculate the percentage of lumen area reduction. This method applies in the clinically important range of lumen area reduction of 10-70% with a standard error of 5% or less. For lumen area reductions greater than about 70%, the standard error is larger. A system for practical implementation is also proposed.

A system analyzes angiogram image data, including video data, in order to extract vasculature information. Advanced image processing and machine learning techniques are used in pre-processing, frame selection, and vasculature segmentation to remove classes of artifacts from angiogram videos, and specifically from frames selected as having a sufficient amount of image data. From segmentation, accurate vasculature diameters are calculated, and, in some examples, stenoses and / or the extent of stenosis is automatically determined and displayed.

The invention discloses a method for identifying the local vascular stenosis degree in a DSA coronary artery image, and the method comprises the following steps: 1), preprocessing a contrast image, and segmenting a coronary artery structure; 2) performing edge detection on the coronary artery structure, and extracting blood vessel boundary information of a region of interest; identifying the topological structure of the blood vessel to obtain a blood vessel center line; and 3) calculating the radius of the blood vessel point by point along the central line of the blood vessel to obtain the local vascular stenosis degree. According to the invention, interactive measurement of coronary vessel lesions is realized, diagnosis deviation and underestimation problems caused by artificial participation are reduced, doctors are assisted in rapid and accurate processing and analysis of DSA images, and application efficiency and objective requirements in actual diagnosis are ensured.

The invention discloses a method for establishing an intracranial vessel simulation three-dimensional stenosis model based on transfer learning. The method comprises the following steps: acquiring a bright blood image group and an enhanced black blood image group of an intracranial vessel; preprocessing each bright blood image and the corresponding enhanced black blood image to obtain a first bright blood image and a first black blood image; performing image registration by using mutual information based on Gaussian distribution sampling and a registration method of an image pyramid; groupingthe registered bright blood images to obtain MIP images in all directions; obtaining a two-dimensional blood vessel segmentation image based on the MIP image and the fundus blood vessel image; performing back projection synthesis on the two-dimensional blood vessel segmentation image to obtain first three-dimensional blood vessel body data, and obtaining an intracranial blood vessel simulation three-dimensional model by utilizing the second three-dimensional blood vessel body data; and for each section of blood vessel in the model, obtaining a numerical value of a target parameter representingthe stenosis degree of the section of blood vessel, and marking the intracranial blood vessel simulation three-dimensional model by utilizing the numerical value of the target parameter to obtain a simulated three-dimensional intracranial blood vessel stenosis analysis model.

The invention discloses a carotid artery stenosis degree calculation method and device and a storage medium, and the method comprises the steps: S1, receiving a neck MRA image, and carrying out the interlayer registration of a first sequence and a second sequence in the neck MRA image, so as to form an interlayer corresponding relation; S2, transverse blood vessels are extracted from all layers ofimages of the registered first sequence, and the blood vessel area of each transverse blood vessel is calculated; S3, extracting a transverse blood target from each layer of image of the registered second sequence, and calculating the blood area of each transverse blood target; S4, according to the interlayer corresponding relation, the blood vessel area of each transverse position blood vessel and the blood area of each corresponding transverse position blood target are substituted into a calculation formula, and a plurality of carotid artery stenosis degrees are obtained through calculation; and S5, outputting the maximum value in the plurality of carotid artery stenosis degrees as the carotid artery stenosis degree of the neck MRA image. According to the embodiment of the invention, the carotid artery stenosis degree can be automatically calculated, so that the diagnosis efficiency and accuracy of doctors are improved.

The invention provides a deep learning-based CBCT image cross-modal prediction CTA image stroke risk screening method and system. The method comprises the steps of 1, constructing a cyclic adversarialresistance generation network model; 2, training a cyclic adversarial resistance generation network model through the CBCT images and the contrast image data corresponding to the CBCT images; 3, inputting a test image into the trained cyclic antagonism generation network model to generate an angiography CT image; and 4, predicting the stroke risk according to the form, the carotid artery stenosisdegree and the curvature of the carotid artery in the angiography CT image. According to the method, based on the deep learning model, the non-enhanced CBCT image is converted into the enhanced CT angiography image, carotid artery blood vessel segmentation and extraction are carried out, the carotid artery stenosis degree and curvature are quantitatively calculated, then the stroke risk is predicted, and a convenient, economical and efficient new way is provided for clinically obtaining the CTA image and diagnosing.

The invention discloses a method for measuring the vascular diameter in a coronary angiography image based on depth learning. The method for measuring the vascular diameter in a coronary angiography image based on depth learning comprises the following steps: a DSA image processing module converts real-time acquired DSA image data into a data stream which can be processed by a subsequent module, and stores the data stream in a memory and sends the data stream to a depth network segmentation module. The depth network segmentation module segments the acquired DSA image data to distinguish the blood vessel pixel from the background pixel. The centerline extracting module extracts the blood vessel centerline from the blood vessel pixel image. The diameter calculation module calculates the measured diameter, the reference diameter and the stenosis rate of the blood vessel based on the segmented image and the blood vessel centerline. Compared with the PVA method, the invention has more objective results and avoids the differences between different doctors and different hospitals due to different interpretation methods. This method does not need manual intervention and is easy to operate.It can be used by doctors in coronary angiography and can provide objective reference for doctors to determine the degree of coronary stenosis.

A method of displaying an ultrasound image, the method including obtaining ultrasound data regarding a target object including a blood vessel; obtaining, from the ultrasound data, speeds of blood flows passing through a plurality of cross-sections of the blood vessel; determining stenosis degrees of the plurality of cross-sections based on the blood flow speeds; and generating and displaying an ultrasound image of the blood vessel showing the determined stenosis degrees.

The invention discloses a coronary artery stenosis estimation method, system and device. The method comprises the steps: carrying out the preprocessing of an obtained medical digital imaging and communication DICOM image, and obtaining a DICOM image meeting the requirements; inputting the preprocessed DICOM image into a pre-trained neural network, and outputting a calcified area and a coronary artery area through the neural network; determining a calcified area where the plaque with the maximum diameter is located and a corresponding coronary artery area; calculating the maximum diameter of the calcified area and the corresponding coronary artery diameter, calculating the coronary artery stenosis degree according to the maximum diameter of the calcified area and the corresponding coronaryartery diameter, obtaining the lesion level corresponding to the coronary artery stenosis degree, and outputting the lesion level and the coronary artery stenosis degree.

The invention discloses a method for establishing an intracranial vessel simulation three-dimensional stenosis model based on transfer learning. The method comprises the following steps: acquiring a bright blood image group and an enhanced black blood image group of an intracranial vessel; performing image registration by using a registration method based on mutual information and an image pyramidto obtain a registered bright blood image group; utilizing the registered bright blood image group to obtain an MIP image in each direction; obtaining a two-dimensional blood vessel segmentation image based on the MIP image as a target domain and a fundus blood vessel image; synthesizing the two-dimensional blood vessel segmentation images in the three directions by using a back projection methodto obtain first three-dimensional blood vessel body data; obtaining an intracranial blood vessel simulation three-dimensional model by utilizing the second three-dimensional blood vessel body data corresponding to the registered bright blood image group; and for each section of blood vessel in the model, obtaining a numerical value of a target parameter representing the stenosis degree of the section of blood vessel, and marking the intracranial blood vessel simulation three-dimensional model by utilizing the numerical value of the target parameter to obtain a simulated three-dimensional intracranial vessel stenosis analysis model.

The invention provides a functional evaluation method and system for intracranial vascular stenosis, electronic equipment and a medium, and the method comprises the steps: carrying out the three-dimensional reconstruction of intracranial image data, and obtaining a three-dimensional model of a lesion blood vessel; acquiring specific blood flow parameters of the patient according to the three-dimensional model; calculating hemodynamic parameters of lesion blood vessels based on the patient-specific blood flow parameters; and according to the hemodynamic parameters of the lesion blood vessel, evaluating intracranial vascular stenosis. According to the functional evaluation method for intracranial vascular stenosis, the problem that the intracranial stenosis is inaccurately judged only through the intracranial liability artery stenosis degree in the prior art is solved.

The invention discloses an AVA stenosis positioning and stenosis degree multistage classification system based on MPSF extraction. An acoustic signal collector collects and transmits acoustic signalsof a tester AVA relative to upstream and downstream positions; after the processor receives the acoustic signals of the tester AVA relative to upstream and downstream positions, the following steps are executed: the received acoustic signals are segmented according to a heart rate period to obtain acoustics sub-signals; performing singular spectrum analysis and welch power spectrum estimation on the acoustics sub-signals to obtain frequency spectrum energy of a high-frequency part, and forming a group of MPSF by the frequency spectrum energy of the acoustics signals from the same AVA relativeupstream and downstream positions; and inputting each group of MPSF into an AVA stenosis positioning and stenosis degree multistage classification model constructed based on a long-term and short-termmemory network, and outputting a classification result of the AVA stenosis position and the stenosis degree of the tester through calculation. According to the system, the accuracy of AVA stenosis area positioning and stenosis degree classification is improved.

The invention discloses a coronary artery stenosis visualization quantification method and device based on multi-channel heart sound, and is suitable for the field of analysis of cardiophonogram electrocardiograms. The method comprises the steps of synchronously collecting and recording electrocardiosignals and multi-channel heart sound signals, and storing the electrocardiosignals and the multi-channel heart sound signals as audio files; segmenting heartbeat cycle signal data into S1, a systolic period, S2 and a diastolic period by using R waves and heart sound characteristics; respectively calculating characteristic values of each section of the heartbeat cycle and the total heartbeat cycle; performing stenosis degree risk prediction by adopting multi-model prediction and decision rules;and representing the coronary artery stenosis risk degree by adopting columnar graphs with different colors and heights. According to the method, various characteristics are extracted, heartbeat cycle characteristics are reflected in all directions, and the model prediction accuracy is improved through the multi-machine learning model and the use of decision rules. The health condition of the coronary artery is visually reflected in the forms of the energy spectrogram and the histogram.

The invention relates to an intracranial vascular focus marking and three-dimensional display system based on smart medical treatment. The system comprises an image acquisition module, an image preprocessing module, an image registration module, a flow void artifact elimination module, a blood three-dimensional module establishment module, a blood vessel three-dimensional model establishment module, an angiography enhancement three-dimensional model establishment module, an intracranial vascular angiography enhancement three-dimensional model establishment module, an intracranial vascular angiography enhanced three-dimensional stenosis analysis model building module and an intracranial vascular three-dimensional display module. According to the scheme, the real information of the intracranial blood vessel and the analysis data about the intracranial blood vessel stenosis degree can be simply, conveniently, rapidly and visually obtained in clinical application, and a doctor is assistedto analyze and judge a focus more accurately and visually.

The invention relates to a coronary artery stenosis assessment method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring an angiography image ofa coronary artery; performing three-dimensional reconstruction of blood vessels according to the angiography image to obtain a three-dimensional blood vessel model of the coronary artery; aiming at atarget branch of the coronary artery, carrying out a pressure drop test on the three-dimensional blood vessel model to obtain a quadratic function relationship between the blood flow rate and the blood flow pressure of the target branch, the target branch being any branch of the coronary artery; and evaluating the narrow condition of the coronary artery according to the constant coefficient of the quadratic function relationship. The embodiment of the invention is used for evaluating the stenosis degree of the blood vessel.

The present invention discloses the correlation between circular endothelial progenitor cell (EPC) and coronary artery pathological change. It is test proved that the number and migration and adhesion power of circular EPC is high correlated with the stenosis degree of coronary artery, so that circular EPC may be used as the reference index for diagnosing the stenosis degree of coronary artery. The present invention also provides method and kit for detecting the stenosis degree of coronary artery based on the said correlation.

The invention discloses a coronary artery stenosis lesion degree identification method based on multi-classifier fusion. The method comprises the following steps: firstly, constructing an image sample library; preprocessing a CT original sequence diagram extracted by a heart CTA, and then performing feature extraction to extract three types of features including interested texture features, gray features and geometric features; dividing the samples into a training group and a test group, calculating the correlation between each feature and a prediction result, and removing the features with small correlation; establishing a multi-classifier fusion prediction model, fusing results of the single classifiers to predict the coronary artery stenosis lesion degree, meanwhile, determining the weights of the three single classifiers in the fusion classifier through a weighting method, and when the stenosis degree is lower than 50%, judging that the sample a normal sample, and when the stenosis degree is larger than 50%, judging that the sample is a lesion sample. According to the method, automatic classification and pre-judgment on the aspect of judging the stenosis degree are realized, and the injury to a patient caused by an invasive operation is avoided.

The invention discloses a method for establishing a simulated three-dimensional intracranial vascular stenosis analysis model. The method comprises the following steps: acquiring a bright blood imagegroup and an enhanced black blood image group of an intracranial vascular part; taking each bright blood image and the corresponding enhanced black blood image as an image pair, and preprocessing eachimage pair to obtain a first bright blood image and a first black blood image of the image pair; for each first bright blood image, taking the corresponding first black blood image as a reference, and performing image registration by using mutual information based on Gaussian distribution sampling and a registration method of an image pyramid to obtain a registered bright blood image group; establishing an intracranial blood vessel simulation three-dimensional model by using the registered bright blood image group; for each section of blood vessel in the intracranial blood vessel simulation three-dimensional model, obtaining a numerical value of a target parameter representing the stenosis degree of the section of blood vessel; and marking the intracranial vessel simulation three-dimensional model by utilizing the values of the target parameters of each section of vessel to obtain a simulated three-dimensional intracranial vessel stenosis analysis model.

The invention discloses a method for establishing a simulated three-dimensional blood vessel stenosis analysis model. The method comprises the following steps: acquiring a bright blood image group andan enhanced black blood image group of a blood vessel part; performing image registration on each bright blood image by using a registration method based on mutual information and an image pyramid bytaking the corresponding enhanced black blood image as a reference to obtain a registered bright blood image group; establishing a blood vessel simulation three-dimensional model by using the registered bright blood image group; segmenting each section of blood vessel in the blood vessel simulation three-dimensional model from three preset directions to obtain a two-dimensional sectional drawingof each direction; performing corrosion operation on the blood vessel in the two-dimensional sectional drawing in each direction, and recording the target corrosion times when the blood vessel is corroded to a single pixel; obtaining a numerical value of a target parameter representing the stenosis degree of the section of blood vessel according to the target corrosion times respectively corresponding to the section of blood vessel in the three directions; and marking the blood vessel simulation three-dimensional model by utilizing the values of the target parameters of each section of blood vessel to obtain a simulated three-dimensional blood vessel stenosis analysis model.

A method of assessing stenosis severity for a patient includes generating a three dimensional model of a lesion specific vessel tree of the patient. A plurality of inlet and outlet positions are identified in the lesion tree. A total flow rate from the vessel tree model is estimated. A processor and task specific software are utilized to perform computational fluid dynamic simulation on the vessel tree. A flow rate and apparent flow resistance for each of the outlets is then determined. At least one ideal model is generated. A computational fluid dynamic simulation is performed on the at least one ideal model. A level of stenosis severity is determined for each of the outlets.

The invention discloses a biomarker for detecting occlusion or stenosis of coronary artery and a preparation method thereof, and a reagent kit containing the biomarker. The name of the biomarker is circ-Sirtl, and the biomarker comprises a nucleotide sequence gene shown in SEQ ID NO: 1. The preparation method comprises the following steps of (A) extracting total RNA (ribonucleic acid) in human blood plasma; (B) removing remained DNA (deoxyribonucleic acid) in the total RNA; (C) reversely transcribing into cDNA by a random primer; (D) designing a primer, and performing PCR (polymerase chain reaction) amplifying, cloning and sequencing. The reagent kit comprises a blood plasma total RNA extraction reagent set, a RNA reverse transcription reagent set, a real-time fluorescent quantitative PCR reagent set, a probe set, and a real-time fluorescent quantitative PCR primer set. The circ-Sirtl biomarker and the detection regent kit have the outstanding effects of high sensitivity and strong specificity in detecting and evaluating the stenosis degree of the coronary artery of a patient.

The present invention discloses the correlation between circular endothelial progenitor cell (EPC) and coronary artery pathological change. It is test proved that the number and migration and adhesion power of circular EPC is high correlated with the stenosis degree of coronary artery, so that circular EPC may be used as the reference index for diagnosing the stenosis degree of coronary artery. The present invention also provides method and kit for detecting the stenosis degree of coronary artery based on the said correlation.

The present disclosure provides a method for predicting a healthy radius of a blood vessel path, a method for predicting a candidate stenosis of a blood vessel path, and a device for predicting a degree of vascular stenosis. The method for predicting the healthy radius includes: obtaining the vascular radius of the vascular path; using a processor, detecting the peak value of the vascular radius of the vascular path; and using the processor, predicting the healthy radius by performing regression on the radius peak. The device for predicting the degree of vascular stenosis includes a processor, which is configured to realize by executing executable instructions: acquiring image units along the centerline within the candidate stenosis range of each vascular path; and based on the acquired image units, using a learning network to determine The stenosis of each vascular path, wherein the learning network is based on a convolutional neural network and a recurrent neural network. The device can automatically determine the candidate stenosis and detect the stenosis according to the candidate stenosis range, which can significantly reduce the calculation load, improve the detection efficiency and effectively avoid missed detection.

The present invention proposes an arteriovenous fistula external hemodynamics physical simulation model device, the simulated blood circuit includes a simulated heart container, a radial artery blood pressure simulation container, a cephalic vein blood pressure simulation container and a fistula simulator equipped with a measuring device; The installation height of the arterial blood pressure simulation container and the cephalic vein blood pressure simulation container is higher than that of the simulated heart container; the fistula simulator is equipped with a fistula simulation cavity surrounded by elastic simulated skin; the input end of the fistula simulation cavity is respectively connected to the radial artery blood pressure simulation container , the cephalic vein blood pressure simulation container, and the output port is connected to the simulated heart container; when the simulation is performed, the fistula simulation cavity reaches the required deformation, and the simulated blood in the radial artery blood pressure simulation container and the cephalic vein blood pressure simulation container flows back through the fistula simulation cavity To simulate the heart container, the measuring device of the fistula simulator measures the pressure and flow velocity of the blood flowing through the input end and the output end; the invention can establish a set of stable blood vessel models with controllable stenosis.

The invention discloses a vascular stenosis detection method and device and a computer readable medium, and the method comprises the steps: firstly carrying out vascular stenosis site detection on a blood vessel straightening reconstruction image, and generating a vascular stenosis list; obtaining an adding instruction of the blood vessel stenosis site; then responding to the adding instruction, and adding the vascular stenosis site to the vascular stenosis list; and finally, for any blood vessel stenosis site in the blood vessel stenosis list, determining a stenosis lumen diameter and a reference lumen diameter corresponding to the blood vessel stenosis site, and determining the stenosis degree of the blood vessel lumen based on the stenosis lumen diameter and the reference lumen diameter. Therefore, the blood vessel stenosis site of the blood vessel lumen can be effectively detected, the problem of omission of blood vessel stenosis site detection is avoided, and the accuracy of blood vessel stenosis site detection is improved.

The invention discloses a method for establishing a simulated three-dimensional blood vessel stenosis analysis model. The method comprises the following steps: acquiring a bright blood image group and an enhanced black blood image group of a blood vessel part; performing image registration on each bright blood image by using a registration method based on mutual information and an image pyramid by taking the corresponding enhanced black blood image as a reference to obtain a registered bright blood image group; establishing a blood vessel simulation three-dimensional model by using the registered bright blood image group; aiming at each section of blood vessel in the blood vessel simulation three-dimensional model, segmenting from three preset orientations to obtain a two-dimensional section diagram of each orientation; performing corrosion operation on the blood vessel in the two-dimensional section diagram of each orientation, and recording target corrosion times when the blood vessel is corroded to a single pixel; according to the target corrosion times corresponding to the section of blood vessel in the three directions, obtaining a numerical value of a target parameter representing the stenosis degree of the section of blood vessel; and marking the blood vessel simulation three-dimensional model by using the numerical value of the target parameter of each section of blood vessel to obtain a simulated three-dimensional blood vessel stenosis analysis model.

The invention discloses a coronary artery virtual stent implantation method and system based on haemodynamics analysis. The coronary artery virtual stent implantation method comprises the steps that CTA image data of a patient are read, plaque is identified, a vascular three-dimensional image model is constructed, the plaque is removed, and the position of the plaque is recorded; the physiological parameters of the patient is received, a CFD boundary condition is set, and CFD calculating is conducted; according to a three-dimensional vascular form, a plaque characteristic and FFR values throughout blood vessels, a lesion is identified; according to the identified lesion, a stent specification database and a virtual stent implantation strategy, an implantation scheme is generated, and a new vascular three-dimensional geometric model is generated; under the new three-dimensional geometric model, fluid calculation is conducted again, according to a preset selecting standard, an optimal stent implantation scheme is output. According to the coronary artery virtual stent implantation method and system, blood vessel stenosis degree calculating, stent implantation strategy generating and quantitative evaluating of the virtual stent implantation effect are completed automatically, the operation scheme is planned accurately, the doctor decision efficiency is improved effectively, and the risk of dependence on the artificial judgment is reduced.

The invention provides a complete detection kit for conveniently and rapidly detecting miRNA. According to the detection kit, a special primer sequence does not need to be customized. On the other hand, the detecting kit is used for detecting the expression level of circulating miR-155 in serum of an AS patient and the expression level of miR-155 in a plaque tissue and simultaneously detecting theexpression levels of miR-155 in the serum of the patients with different degrees of coronary stenosis, the correlation between miR-155 and AS common risk factors is analyzed by virtue of a statistical method, the diagnosibility of miR-155 is analyzed by virtue of ROC when the degree of coronary stenosis is more than 75%, the expression of miR-155 in AS and the clinical significance are discussed,and a new through is provided for the auxiliary diagnosis of AS and the grading of the degree of coronary stenosis.