50 results about "Coronary vessel" patented technology

A method and apparatus for vascular assessment are disclosed. The apparatus, in some embodiments, receives, from a medical imaging device, a medical image of a coronary vessel tree of a subject and calculates a plurality of geometric measurements associated with individual portions of a vascular segment of the coronary vessel tree. The apparatus also determines a plurality of resistances associated with the plurality of geometric measurements associated with the individual portions of the vascular segment and determines a plurality of pressure drops across the individual portions of the vascular segment based on the determined resistances and a calculated or estimated blood flow. The apparatus further calculates based on the plurality of pressure drops, a functional index indicative of a presence or an absence of a stenosis within the vascular segment.

An apparatus for performing a vascular assessment is disclosed. The apparatus creates a three-dimensional model that is representative of a coronary vessel tree of a patient based on at least two angiographic images. The apparatus estimates first blood flow resistance values for points along at least some vascular segments of the coronary vessel tree using vascular geometrical dimensions of the three-dimensional model. The apparatus also estimates second blood flow resistance values for the points along the at least some vascular segments of the coronary vessel tree using a volume of a crown of the vascular segment downstream from the respective point. The apparatus determines fractional flow reserve (“FFR”) by calculating a ratio of the first blood flow resistance values and the second blood flow resistance values at each of the points along the at least some vascular segments of the coronary vessel tree.

A method and apparatus for vascular assessment are disclosed. The apparatus, in some embodiments, receives, from a medical imaging device, a medical image of a coronary vessel tree of a subject and calculates a plurality of geometric measurements associated with individual portions of a vascular segment of the coronary vessel tree. The apparatus also determines a plurality of resistances associated with the plurality of geometric measurements associated with the individual portions of the vascular segment and determines a plurality of pressure drops across the individual portions of the vascular segment based on the determined resistances and a calculated or estimated blood flow. The apparatus further calculates based on the plurality of pressure drops, a functional index indicative of a presence or an absence of a stenosis within the vascular segment.

The invention provides a coronary vessel deformation calculation method and device in a cardiac cycle, a medium and equipment, and the method comprises the steps: obtaining image data, selecting a frame of image as a key frame from the image data, and enabling the image data to comprise coronary vessel image data of the whole cardiac cycle; positioning a blood vessel section used for deformation analysis in the key frame as a reference analysis section; selecting a plurality of analysis frames from the image data, and positioning a blood vessel section corresponding to the reference analysis section in each analysis frame as a comparison analysis section; and carrying out contour segmentation and registration on the reference analysis section and each comparison analysis section so as to calculate deformation of each position in the blood vessel section to be analyzed. Deformation of each position in a to-be-analyzed blood vessel section is calculated, so that the problem that blood vessel deformation preprocessing is complex is solved, and the simplicity and accuracy of coronary artery diameter change detection are improved.

A method of describing fluid dynamics in a tissue or organ using a numerical model of blood flow is provided. In one aspect, the invention relates to a method for assessing myocardial perfusion in a human patient using non-invasive measurements and percolation mechanics including computational fluid dynamics. The method may be implemented as a computer-implemented invention. The method includes using patient-specific anatomical structure data obtained from the non-invasive measurements to generate an anatomical geometric model of the myocardium connected to the plurality of coronary vessels. The anatomical geometric model is a combination of fluid and porous domains and boundaries thereof. The invention eliminates the deficiencies of known in vivo methods, including all risks associated with invasive procedures. Furthermore, an actually infinite temporal-spatial resolution is achieved, the result of the invention is completely free of motion artifacts, and the exposure of the patient to radiation is limited.

A method of performing 3D vessel tree reconstruction includes providing segmented binary angiography images, applying a distance transform to the images, and generating distance transformed binary angiography images. The set of distance transformed binary angiography images are provided to a trained 3D vessel reconstruction machine learning model capable of reconstructing 3D vessels. The 3D vessel tree reconstruction machine learning model includes a multi-stage convolutional neural network comprising a multi-stage architecture with (i) a vessel centerline stage, and (ii) a radius reconstruction stage. Resultant 3D reconstructed vessel trees may be used in performing clinical assessment of coronary vessel health, and occlusion.

The invention discloses a CCTA image-based full coronary artery tree complete automatic segmentation and blood vessel center line extraction system, and the system comprises a positioning module which is used for carrying out the positioning and extraction of a heart according to a CCTA image; the extraction module is used for performing primary extraction of coronary artery tree segmentation according to the CCTA image after heart positioning to obtain a coronary artery tree; the repairing module is used for extracting the center line of the blood vessel based on the extracted coronary artery tree, and extending the center line at the breakpoint to connect the fractures, or further extending the incomplete center line to the tail end of the blood vessel to obtain a repaired center line; the supplementing module is used for supplementing the coronary artery tree structure and repeating the steps of blood vessel repairing and coronary artery tree supplementing to obtain a coronary artery blood vessel area; and the reconstruction module is used for obtaining a coronary artery tree blood vessel result according to the multiple planes of the reconstructed blood vessel. According to the method, full-automatic accurate extraction of the complete coronary vessel tree can be realized, and technical innovation and clinical application of coronary imaging are promoted.

The invention relates to a cross-modal distillation coupling coronary plaque classification method, and aims to solve the problems that CTA is relatively low in spatial resolution and is easily interfered by artifacts, and a high-precision IVUS needs to introduce a probe into a blood vessel, so that discomfort and potential complication risks are possibly brought to a patient. In order to integrate the advantages of the CTA technology and the IVUS technology, the mapping relation between the CTA and the IVUS is modeled by means of the artificial intelligence technology, the limitation of the CTA is overcome through automatic sketching and recognition of coronary vessels and plaques and by means of the high-resolution characteristic of the IVUS, and the judgment capacity of the IVUS is given to a CTA image.

A method of performing 3D vessel tree reconstruction includes providing segmented binary angiography images, applying a distance transform to the images, and generating distance transformed binary angiography images. The set of distance transformed binary angiography images are provided to a trained 3D vessel reconstruction machine learning model capable of reconstructing 3D vessels. The 3D vessel tree reconstruction machine learning model includes a multi-stage convolutional neural network comprising a multi-stage architecture with (i) a vessel centerline stage, and (ii) a radius reconstruction stage. Resultant 3D reconstructed vessel trees may be used in performing clinical assessment of coronary vessel health, and occlusion.

The invention belongs to the technical field of medical image processing, and particularly relates to a blood vessel wall thickness measuring method based on coronary artery reconstruction. According to the technical scheme, based on multi-row single-heartbeat CT imaging, the optimal cardiac cycle is selected through Laplacian smooth mapping, image artifacts are eliminated through a U-net architecture with expansion convolution, the coronary artery is reconstructed, segmentation of the blood vessel wall and the lumen is conducted on the reconstructed coronary artery, and the thickness of the blood vessel wall is measured through a maximum internally tangent ball method. The problems that coronary artery imaging has artifacts caused by heart movement, adjacent imaging is discontinuous and accurate reconstruction cannot be achieved due to vasoconstriction are solved, and the problem of selecting the optimal period of coronary vessel wall thickness measurement is solved.

The present application relates to a kind of DSA coronary vessel segmentation deep learning method of edge and topological feature, comprising the following steps: obtaining DSA coronary angiography image dataset and corresponding coronary vessel label dataset;Data is input into deep learning network, the deep learning network is end-to-end U type deep learning network, include improved bidirectional Transformer module and multi-scale semantic fusion module, and encoder and decoder are connected by jump connection and multi-scale semantic fusion module;Coronary vessel segmentation model is trained in three stages using the composite loss function comprising Dice loss, Focal loss, edge loss and topological loss, the segmentation model of the training completion can be carried out complete segmentation to the coronary vessel in DSA coronary angiography picture;Using composite evaluation index, the overall segmentation accuracy of segmentation model, edge segmentation accuracy and the ability of keeping topological consistency are comprehensively evaluated.

The application discloses a blood vessel image segmentation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring an initial blood vessel image, determining a coronary skeleton image of the initial blood vessel image; determining each skeleton point on the skeleton line in the coronary skeleton image, and determining a skeleton branch point in each skeleton point based on the degree of each skeleton point; acquiring a skeleton connected path between any adjacent skeleton branch points, and determining a venous branch line segment in the coronary skeleton image based on the skeleton connected path; and determining a coronary segmentation image of the initial blood vessel image based on the initial blood vessel image and the venous branch line segment. The technical scheme disclosed by the application removes the venous branch in the initial blood vessel image to obtain a coronary vessel image of the initial blood vessel image, solves the problem of low coronary segmentation accuracy of blood vessels in the prior art, and achieves the technical effect of improving the coronary segmentation accuracy.

The invention provides a comprehensive assessment method for coronary artery function, which comprises the following steps of: based on boundary conditions, performing hemodynamic numerical simulation calculation on each finite element model by adopting a traditional computational fluid mechanics solution mode or a reduced order solution mode to obtain simulated hemodynamic parameters; based on a comparison result between the simulated hemodynamic parameters and the reference hemodynamic parameters corresponding to each finite element model, performing iterative optimization on the outlet resistance of each blood vessel branch and the simulated hemodynamic parameters in the boundary conditions to obtain optimized outlet resistance of each target blood vessel branch and target simulated hemodynamic parameters; and determining the fractional flow reserve and the microcirculation resistance index by using the outlet resistance of each target blood vessel branch and the target simulated hemodynamic parameters. According to the method, coupling iterative calculation is carried out on the microcirculation resistance by establishing the coronary vessel tree, the microcirculation resistance index and the fractional flow reserve are determined at the same time based on the calculation result, and the reliability of overall coronary function calculation is improved.

This invention discloses a 3D-printed helical biodegradable anti-inflammatory vascular stent and its preparation method, belonging to the field of vascular stents. The invention uses multiple helical support wires arranged into a tubular shape, with the ends connected in a serpentine transitional manner. The helical support wires are connected by connecting ribs, and the surface is coated. This invention is biodegradable after use, avoiding the harm caused by permanent retention in the body, and has a lower restenosis rate compared to metal stents. The surface of the vascular stent is coated with a spermidine-containing coating to prevent immune rejection of the implanted vascular stent, thereby addressing the problems of inflammation and intimal hyperplasia. The helical structure facilitates contraction and expansion, has good bending flexibility, and the added connecting ribs increase the radial support performance of the vascular stent, better achieving the effect of vascular remodeling. This invention has significant implications in terms of biodegradability, anti-immune rejection, and mechanical properties, and can be used for treatment in different locations such as peripheral and coronary vessels.

A method, a computer-readable medium and a system for assessment of a hemodynamic response to an Adenosine receptor agonist stimulation for a human patient are disclosed. Starting point for the method are non-invasive pressure data in the resting state. Said data are used in pharmacokinetic / pharmacodynamic modeling to arrive at an individual patient-specific response for an adenosine receptor stimulation by an agonist. This response is used to a calibrate lumped-parameter Windkessel type model of a blood circulation. The calibrated model allows for calculating of hemodynamic parameters for the hyperemic state, in particular to calculate flow and pressure values in both coronary arteries inlets. The hemodynamic parameters allow for in silico coronary vessel diagnostic. The effectiveness of the developed method has been confirmed in multi-center clinical trials.

The application relates to a coronary microcirculation vascular resistance acquisition method and system based on a layered myocardial model, which comprises the following steps: establishing a coronary vessel model from medical images; generating a side vessel for the coronary artery; regarding the coronary artery and the side vessel as main vessels, fitting and constructing an outer surface of a myocardial model according to the outlet position of the main vessels, establishing an inner surface of the myocardial model based on a similar principle to the outer surface, and ensuring that the volume of the model is the myocardial volume value; using an ellipsoid surface for layered interpolation; selecting a point P in the outermost layer and connecting the main vessel outlet to generate a microcirculation vessel; selecting a point T outside the current layer and cooperating with the nearest microcirculation vessel to generate a bifurcated microcirculation vessel; gradually advancing to the inner layer to form bifurcation and ensure the bifurcation number of each layer; and calculating the resistance value of the main vessel outlet. Compared with the prior art, the application considers the distribution of the coronary artery at various levels of the myocardium under real conditions, so that the calculated resistance value is more accurate and has the specificity of a patient.

The invention relates to the technical field of biological medicine, in particular to application of an Orai1 protein inhibitor in preparation of a medicine for treating coronary artery calcification. By using the Orai1 protein inhibitor, coronary artery calcification can be accurately treated, the occurrence proportion of serious coronary artery calcification is reduced, and economic and social burdens brought by surgical treatment are relieved. Besides, the Orai1 protein inhibitor can also improve and inhibit acute myocardial ischemia symptoms caused by factors such as coronary vessel and myocardial cell calcification, calcium overload and the like, the myocardial infarction area is reduced, and death caused by acute myocardial ischemia is reduced.

The invention provides a coronary blood vessel segmentation system and method fusing high and low frequency features. The method comprises the steps that a cascade multi-scale stripe convolution module is cascaded with stripe convolution operations of different scales, and features are extracted in a multi-level mode from the global mode to the local mode; the high and low frequency feature separation adaptive module separates an input feature map into high-frequency and low-frequency components, adaptive enhancement is carried out on the high-frequency and low-frequency components respectively, and finally the enhanced high-frequency and low-frequency components are fused; the multi-scale stripe high and low frequency enhancement module firstly inputs the input feature map into the cascade multi-scale stripe convolution module to extract features, then inputs the output feature map into the high and low frequency feature separation adaptive module to carry out high and low frequency separation and adaptive enhancement, and finally outputs the obtained feature map. Based on the technical scheme of the invention, the segmentation precision and the detail capture capability can be improved; local details and a global structure can be clearly distinguished, and the problem of excessive segmentation or insufficient segmentation is effectively avoided; the robustness of the model is enhanced, and the blood vessel can be stably and accurately segmented.

The application belongs to the technical field of medicine, and particularly relates to application of acacetin or 6-hydroxyflavone in preparation of anti-angina pectoris drugs, and aims to solve the problems of adverse reactions such as blood pressure drop, reflex tachycardia and the like caused by non-selective vasodilation of existing anti-angina pectoris drugs, wherein the drug improves myocardial blood supply by selectively dilating coronary vessels of the heart, wherein acacetin dilates coronary vessels of the heart by inhibiting cardiac coronary vessel myosin light chain kinase and activating myosin light chain phosphatase, and 6-hydroxyflavone dilates coronary vessels of the heart by inhibiting protein kinase C and enhancing myosin light chain phosphatase activity; and the acacetin or 6-hydroxyflavone significantly reduces the symptoms of myocardial ischemia of rats caused by isopropyl adrenaline, improves characteristic ECG abnormal changes of angina pectoris, does not affect heart rate and systemic blood pressure, and is safer, thereby providing a potential drug selection for anti-angina pectoris treatment.

This application relates to a method, apparatus, computer device, storage medium, and computer program product for measuring the microcirculatory resistance coefficient based on medical imaging. The method comprises: acquiring a medical image of a coronary region, identifying the myocardial lesion region within the medical image; identifying the lesion region corresponding to each coronary vessel within the myocardial lesion region based on the spatial geometric relationship between the coronary vessels in the medical image; and acquiring the microcirculatory resistance coefficient of each coronary vessel based on the lesion region corresponding to each coronary vessel. This method enables acquisition of the microcirculatory resistance coefficient from medical imaging, thereby avoiding damage to the patient's body during interventional surgery.

A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels includes receiving a CT scan volume representing a 3D volume of a region of anatomy that includes a pericardium; preprocessing the CT scan volume to output a preprocessed scan volume; dividing the CT scan volume into a first set of subvolumes; extracting a region of interest by autonomous segmentation of the heart region as outlined by the pericardium, by means of a neural network trained on 3D subvolumes and combining the results of the individual subvolume predictions for the first set to output a mask denoting a heart region as delineated by the pericardium; combining the preprocessed scan volume with the mask to obtain a masked volume; converting the masked volume to a second set of 3D subvolumes; and performing autonomous coronary vessel segmentation to output a mask denoting the coronary vessels.

The application provides a non-invasive coronary blood flow evaluation method based on multi-phase CTA and Bayesian inference, comprising the following steps: acquiring multi-phase coronary CTA images of a target to be evaluated, and extracting coronary vessel deformation waveform data from the multi-phase coronary CTA images; generating posterior probability distribution estimation results of coronary blood flow rate through a pre-established Bayesian inference framework and the coronary vessel deformation waveform data; and generating coronary blood flow rate evaluation results corresponding to the target to be evaluated according to the posterior probability distribution estimation results. The application uses a Bayesian framework to simultaneously estimate multiple physiological parameters, and identifies parameter interactions through posterior correlation analysis, thereby improving inversion accuracy.

The application belongs to the technical field of medical images, and discloses a coronary vessel center line extraction method, device, equipment and storage medium. The method comprises the following steps: acquiring a coronary segmentation image and an aorta segmentation image, and detecting the coronary segmentation image to obtain a first left coronary starting point and a first right coronary starting point; performing inflation processing on the aorta segmentation image to obtain a target aorta segmentation image; performing intersection segmentation on the coronary segmentation image based on the target aorta segmentation image to obtain a final coronary segmentation image; detecting the final coronary segmentation image, and determining a left coronary end point and a right coronary end point after detecting a second left coronary starting point and a second right coronary starting point; and determining a left coronary vessel center line and a right coronary vessel center line based on the first left coronary starting point, the first right coronary starting point, the left coronary end point and the right coronary end point. In this way, the coronary vessel center line can be accurately extracted when the coronary vessel origin is abnormal.

The invention provides a method for describing flow dynamics in a tissue or organ using a numerical model of blood flow. In one aspect, the invention pertains to a method for assessment of myocardial perfusion using a non-invasive measurement and poromechanics, involving computational fluid dynamics, for a human patient. The method can be realized as a computer-implemented invention. The method comprises generating an anatomical geometry model of a myocardium connected with a plurality of coronary vessels using patient-specific anatomical structure data obtained from said non-invasive measurement. The anatomical geometry model is a composition of fluid and porous domains with their boundaries. The invention cures insufficiencies of the known in vivo methods, including all the risks related to invasive procedures. Moreover, a practically unlimited spatial-temporal resolution is achieved, the result of the invention is completely free of motion artifacts and the patient exposure to radiation is limited.

This application provides a method, apparatus, and storage medium for calculating coronary artery calcium scores, comprising: segmenting an initial three-dimensional medical image of the cardiac region from a plain coronary CT image using an image segmentation model; identifying calcified plaque regions in the initial three-dimensional medical image to determine at least one target region image and the location information of each image; identifying the coronary vessel category in each image by recognizing the input consisting of the target region image and the image location information; performing overall recognition of the initial three-dimensional medical image using a second coronary classification model to determine the calcified plaque regions included in the initial three-dimensional medical image, the location information of each region, and the category of coronary vessels; and calculating the calcium score for the calcified regions identified by both models to obtain the total coronary artery calcium score. Thus, the method provided in this application can quickly and accurately calculate the coronary artery calcium score based on plain CT scans.

The application relates to a method and system for obtaining coronary end microcirculation vascular resistance based on vascular growth, and the method comprises the following steps: acquiring medical images, and extracting a myocardial model and a coronary vascular model respectively; generating collateral vessels in the coronary vascular model; regarding all coronary vessels and collateral vessels as main vessels, and obtaining the position coordinates and the vascular radius; randomly selecting a point P in the myocardial model, connecting the point P with the outlet of the main vessel, and generating microcirculation vessels for each outlet of the main vessel; randomly selecting a point T in the point cloud file of the myocardial model, connecting the point T with the nearby microcirculation vessels, and generating multiple bifurcated microcirculation vessels, until the radius of all vessel outlets in the generated vascular tree is smaller than a preset vascular threshold; and calculating the resistance value of each outlet of the main vessel. Compared with the prior art, the coronary end microcirculation vascular resistance value calculated based on the reestablished microcirculation vascular model is more accurate and has the specificity of patients.

The invention relates to a dual catheter arrangement for reperfusion of an ischemic tissue region via a coronary vessel, comprising a primary balloon catheter having a lumen for inflation and deflation of a primary balloon disposed at a distal region of the primary balloon catheter, and at least two separate lumens both extending throughout the primary balloon catheter and open at a distal end of the primary balloon catheter, and a secondary balloon catheter having a lumen for inflation and deflation of a secondary balloon disposed at a distal region of the secondary balloon catheter and an irrigation lumen opening at a distal end of the secondary balloon catheter, which provides at least in an axial region along which the secondary balloon (13) is disposed a means of radial reinforcement and which is arranged within one of the lumens of the primary balloon catheter so that the secondary balloon catheter continues completely through the lumen of the primary balloon catheter and extends distally beyond the primary balloon catheter. Further a system for reperfusion of an ischemic tissue region via a coronary vessel is described comprising the dual catheter arrangement.

The application relates to a coronary vessel segmentation method and device and a storage medium. The segmentation method comprises the following steps: acquiring a three-dimensional CTA image containing a coronary vessel; inputting the three-dimensional CTA image into a trained bifurcation point detection network to obtain bifurcation point information of the coronary vessel, wherein the bifurcation point detection network is constructed based on a first deep full convolution network; and inputting the three-dimensional CTA image and the bifurcation point information into a trained blood vessel segmentation network to determine a segmentation result of the coronary vessel, wherein the blood vessel segmentation network is constructed based on a second deep full convolution network. The coronary vessel segmentation method can quickly and accurately segment the coronary vessel from the three-dimensional CTA image in an end-to-end manner, and the reliability and accuracy of the blood vessel segmentation are further improved due to the bifurcation points in the coronary vessel as auxiliary information.

The invention relates to a coronary vessel registration method and system of a heart two-dimensional image set and a heart three-dimensional image set and a medium. The method comprises the steps that the heart two-dimensional image set and the heart three-dimensional image set are acquired; matching the two-dimensional image with different view angle images of the three-dimensional image set to obtain a target view angle image matched with the two-dimensional image; identifying coronary blood vessels in the two-dimensional image and the target view angle image; and aligning the coronary blood vessels in the two-dimensional image and the target view angle image, and fusing to obtain a coronary blood vessel registration result. The registration method provided by the invention is a multi-stage coronary blood vessel registration method, and local coronary blood vessel registration can be well carried out through view angle matching, recognition, alignment and fusion.

Embodiments of the present application provide an image processing method and device and a storage medium. In the embodiments of the present application, a coronary vessel region and a lesion sub-region containing a plaque in the coronary vessel region in a medical image can be automatically identified, feature extraction and quantification are respectively performed on the coronary vessel region and the lesion sub-region, first medical features and second medical features are obtained, and the first medical features and the second medical features are sent into a pre-trained image classification model to obtain a classification result of the medical image. In the whole process, potential information in the medical image can be deeply mined, the accuracy of identifying the classification result of the medical image is improved, and the demand of actual application is met.