148 results about "Heart structure" patented technology

A heart ultrasound image segmentation method based on a state space model and feature interactive perception relates to the field of image segmentation, and a dual perception state space enhancement module DPSSEM uses two SSM modules and combines the two SSM modules with SAM and CAM respectively, so that more information can be obtained from two dimensions of space and channel, and the image segmentation efficiency is improved. The dual perception state space enhancement module DPSSEM carries out fine interaction and fusion among different feature maps, the feature identification capability and expression capability are significantly enhanced, the accuracy and robustness of a heart structure in ultrasound image segmentation are effectively improved, and the feature interaction perception decoding module FIPDM carries out multi-feature processing branch and interaction operation, so that the accuracy and robustness of the heart structure in ultrasound image segmentation are effectively improved. And performing multi-path refinement and enhancement on the extracted features in a decoding stage.

The invention provides a heart point cloud reconstruction method and system for echocardiography, and the method comprises the following steps: obtaining three-dimensional heart structure data, and carrying out the preprocessing of the data; reading voxel data of the three-dimensional heart structure data, and performing point clouding to form voxel reconstruction point cloud data; performing standardization processing on the voxel reconstruction point cloud data to obtain all-center point cloud data, and performing segmentation acquisition to obtain standard section point cloud data; establishing a scanning guide network based on the heart structure data to be processed and the standard section point cloud data; and obtaining a test sample, inputting the test section point cloud data in the test sample into the scanning guide network, and outputting the three-dimensional heart structure data of the test sample and the heart point cloud data of the section where the test section point cloud data is located. After the scheme is adopted, an echocardiogram scanning guide network based on the point cloud is provided, a complete three-dimensional heart model is constructed, and meanwhile, the tangent plane where the two-dimensional echocardiogram is located is determined in the three-dimensional heart model.

The invention discloses a hypertensive heart disease early screening system fusing ultrasonic multiple parameters, which comprises the following steps: acquiring ultrasonic image data and physiological index monitoring parameters of a patient at different time points, performing difference comparison on heart structure parameters to generate time sequence change data, and establishing a multi-dimensional data fusion scheme based on the time sequence change data and physiological indexes; according to the fusion scheme, acute risk trigger factors are identified, blood flow abnormal parameters are generated, and emergency threshold comparison is carried out to generate acute risk early warning signals; extracting a myocardial strain degradation rate to determine a chronic degradation acceleration time point, carrying out disease progress stage association to generate a chronic risk layering curve, and establishing a clinical index comparison table; carrying out double-layer correlation on the acute risk early warning signal and the chronic risk layering curve, and carrying out calibration judgment to generate a comprehensive risk judgment sequence; the sequence is decomposed into an acute judgment layer and a chronic judgment layer, a risk threshold value is configured to generate an instant early warning signal, a layered screening report is formed, and technical support is provided for early discovery of the hypertensive heart disease.

A system and a computer-implemented method are provided for generating at least one 4-dimensional medical image segmentation for at least one structure of a human heart. The method includes providing a first 4-dimensional medical image comprising the at least one structure of the human heart, the medical image being based on a computed tomography scan image; generating a segmentation of at least part of the provided first 4-dimensional medical image using at least one first trained artificial neural network, wherein the at least one first trained artificial neural network is configured as a convolutional processing network with U-net architecture; and generating at least one 4-dimensional medical image segmentation for the at least one structure of the human heart based at least on the segmentation generated by the at least one first trained artificial neural network.

The invention relates to the technical field of ultrasonic measurement, in particular to a heart parameter measuring method and system. According to the heart parameter measurement method, the position of the heart is acquired, and the probe is finely positioned according to the position of the heart, so that the probe can accurately and quickly find and acquire the ultrasonic image at the standard section of the heart; then capturing a target standard section in the ultrasonic image by adopting a pre-trained self-supervised deep learning model; and finally, carrying out key frame extraction on the 2D echocardiogram video so as to measure a heart structure in a key frame to obtain heart structure parameters, cutting out a region of interest in the Doppler echocardiogram, and carrying out segmentation processing on the region of interest to obtain functional parameters of the heart. By means of the mode, various parameters of the heart can be accurately and efficiently obtained, full automation is achieved in the whole process, and the accuracy and consistency of various parameter test results of the heart are guaranteed.

The invention provides a left heart data analysis method and system based on image segmentation, and relates to the technical field of medical image processing. Comprising the following steps: acquiring cardiac ultrasound image data and preprocessing; performing image segmentation on the preprocessed image data to obtain a segmentation result including a left ventricle, a left ventricular wall and a left atrium; searching a boundary line of the left ventricle and the left atrium, selecting two boundary points corresponding to the minimum and maximum abscissas in the boundary line as left and right mitral valve ring points respectively, and establishing a long axis based on the mitral valve ring midpoint and the cardiac apex point; modeling the ventricle into a structure formed by superposing a plurality of discs along the long axis direction, and obtaining the diameter of the corresponding disc; based on the obtained diameters of the corresponding discs, the left ventricular volumes in the diastolic period and the systolic period are calculated respectively, and the left ventricular ejection fraction is calculated. According to the method, image segmentation and geometric modeling are fused to achieve accurate calculation of the cardiac ejection fraction, meanwhile, the accuracy of cardiac structure image segmentation is improved, and an interpretable ejection fraction estimation process is achieved.

The invention discloses an echocardiogram myocardial motion estimation method and system based on a structure perception diffusion model, and relates to the technical field of ultrasonic video data processing. The invention aims to solve the problem of myocardial motion estimation in an ultrasonic video. According to the technical key points, the method comprises the following steps: (1) preprocessing collected echocardiogram video data; (2) establishing an echocardiogram myocardial motion estimation model; (3) constructing a diffusion model motion estimation module of structure perception; (4) constructing a spatial adaptive up-sampling module; (5) training the network by using the existing data to obtain a network model; and (6) estimating the myocardial movement of the echocardiogram video by using the trained model. According to the method, the cardiac structure information and the multi-scale correlation contained in the echocardiogram can be fully utilized to automatically estimate the cardiac muscle movement of the echocardiogram, and the accuracy and the robustness of the algorithm are effectively improved.

The invention relates to a myocardial resection area determination method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a heart scanning image, and determining a heart three-dimensional model according to the heart scanning image; determining a heart short-axis section and a heart long-axis section according to the heart three-dimensional model; the clock direction angle of the scalpel is determined on the short axis section of the heart; based on the clock direction angle, the excision depth of the scalpel is determined on the long-axis section of the heart; and performing myocardial resection on the heart three-dimensional model according to the clock direction angle and the resection depth through a scalpel model, determining an updated three-dimensional model after myocardial resection, and marking a resection area on the updated three-dimensional model. According to the scheme, accurate preoperative planning guidance and comprehensive heart structure information presentation are provided for the myocardial rotary cutting operation, and therefore the accuracy and safety of the myocardial rotary cutting operation are improved.

The embodiment of the invention relates to a processing method and device for predicting ejection fraction retention type heart failure. The method comprises the steps that a deep learning model used for conducting dichotomy prediction on the ejection fraction retention type heart failure is constructed and recorded as a first prediction model; constructing a model data set and recording the model data set as a first data set; training the first prediction model based on the first data set; after model training is finished, heart structure feature analysis, heart hemodynamic feature analysis and left ventricle vortex time sequence feature analysis are conducted on four-dimensional blood flow heart magnetic resonance imaging data input by a user to obtain a corresponding structure feature set, and the hemodynamic feature set and the vortex time sequence feature form a corresponding first feature data set; and inputting the first feature data set into a first prediction model for prediction to obtain a corresponding first classification vector which is fed back to the current user. According to the invention, the prediction accuracy and the prediction efficiency can be improved.

Apparatus, systems, and methods are provided for optimizing radiation therapy to a patient to treat cardiac arrhythmias. The system generally includes an ultrasound device placed within the esophagus to image and map cardiac structures in real-time. The system may also control the ventilation of the patient to optimize ultrasound monitoring and radiation delivery. The device in the esophagus is designed to position and monitor the esophagus and / or nearby structures to optimize radiation delivery to targets while minimizing radiation to other key structures. In addition, different ablation technologies may be delivered from within the esophagus to ablate certain tissues that cannot be safely ablated with radiation therapy. Finally, the esophageal device may have electrical or magnetic properties that can be used to guide the radiation therapy.

Planning cardiac surgery is a complex task. Current heart models are mostly static and provide no functional information or personalization apart from structural information from imaging modalities like MRI, CT etc. The embodiments of present disclosure addresses unresolved problem of generating a personalized four dimensional cardiac model that integrates blood flow velocity, pressure profile and volume on three-dimensional cardiac structure, providing a comprehensive tool for pre or post-surgical planning and analysis. Unlike existing techniques, which often rely on separate assessments of cardiac anatomy and hemodynamics, using invasive methods, the present disclosure provides a 4D cardiac model that captures the person specific dynamic interaction between cardiac structures and blood flow throughout the cardiac cycle. Further, the 4D cardiac model enables clinicians to visualize and quantify complex flow patterns and pressure distributions within the region of interest in the heart, facilitating precise surgical planning and outcome prediction especially in pediatric heart surgery.

The invention provides a method for three-dimensional visualization of heart scars, a control device and a storage medium, and belongs to the technical field of three-dimensional heart modeling and visual display. The method comprises the following steps: matching a heart structure obtained by segmenting a pre-trained nnU-Net and a binary tag graph corresponding to a scar tissue with a myocardial shape shown by the binary tag graph of the heart structure by using a standard myocardial grid model as a template; and fitting deformation parameters of a standard myocardial grid model by using a deep learning regression model, and applying the deformation parameters to a standard grid to complete grid deformation so as to fit individual myocardial morphology. The heart shape prior is effectively utilized based on the template deformation method, so that the reconstructed three-dimensional model has higher anatomical consistency and fineness. And superposing the fitted three-dimensional myocardial grid model and the reconstructed scar tissue grid under the same coordinate for registration and superposition to realize image three-dimensional transparent myocardial scar visualization so as to accurately restore a continuous three-dimensional scar surface.

The invention relates to the technical field of heart structure parameter measurement, and provides a heart structure parameter acquisition method, and a heart video processing method, device and equipment, which can measure relatively accurate heart structure parameters. The method comprises the following steps: obtaining a left ventricle surrounding key point set, a left ventricle cardiac cavity structure boundary and a left ventricle surrounding tissue structure boundary according to a cardiac image map of a left ventricle long-axis section; according to the left chamber long axis direction determined by the key point set around the left chamber, an inner diameter measuring line of the left chamber is obtained; and determining the inner diameter of the left ventricle and the thickness of the tissue around the left ventricle according to the intersection information of the inner diameter measuring line and the heart cavity structure boundary and the tissue structure boundary.

The invention provides a heart medical image automatic segmentation method and system based on lossless processing. The method comprises the following steps: receiving a cardiac motion dynamic sequence image through imaging equipment, and generating dynamic cardiac image data; on the basis of the dynamic heart image data, a time-frequency analysis algorithm is applied, unique vibration modes of all chambers and valves of the heart are recognized, a self-adaptive harmonic reconstruction technology is adopted, the definition and accuracy of important physiological signals are enhanced, and a heart motion time-space consistency probability map is generated; based on the heart motion time-space consistency probability map, intelligently adjusting energy change by using a variational level set evolution algorithm, and performing anatomical coherence processing by using a topological optimization method to generate a heart structure fine segmentation result; and based on the heart structure fine segmentation result, comprehensive evaluation is carried out in combination with a clinical diagnosis standard, and a heart medical image automatic segmentation report is generated. According to the technical scheme provided by the invention, the accuracy and efficiency of heart medical image automatic segmentation are remarkably improved.

A compressible and expandable stent assembly for implantation in a body lumen such as a mitral valve, the stent assembly including at least one stent barrel that is shaped and sized so that it allows for normal operation of adjacent heart structures. One or more stent barrels can be included in the stent assembly, where one or more of the stent barrels can include a cylinder with a tapered edge.

The invention relates to the technical field of medical image processing, in particular to a fetal echocardiogram heart structure segmentation model training and segmentation method, and the method comprises the steps: obtaining four-cavity view images of a plurality of fetal echocardiograms, and marking the four-cavity view images; generating a training data set according to the labeled four-cavity view image and the correspondingly segmented mask image; the training data set is utilized to train a heart structure segmentation model, the heart structure segmentation model outputs a heart structure segmentation result, the heart structure segmentation model comprises an image encoder, a prompt encoder and a mask decoder, a global attention module of the image encoder comprises an adapter, and a global attention module of the image encoder comprises a mask decoder; the image encoder and the mask decoder are connected through a cross attention module. Therefore, the problems of poor heart structure segmentation accuracy and the like in related technologies are solved.

The invention provides a method and a device for comprehensively evaluating coronary artery plaque dangerousness based on a medical image. The method for comprehensively evaluating the risk of the coronary plaque based on the medical image comprises the following steps: acquiring coronary strain parameters according to a coronary tree segmentation image; acquiring plaque form parameter information according to the plaque segmentation image; acquiring hemodynamic parameter information according to the resting state time sequence image; and acquiring a plaque risk index according to the coronary strain parameter, the plaque morphological parameter information and the hemodynamic parameter information. The method comprises the following steps: firstly, segmenting a heart structure, and calculating coronary artery strain parameters according to the shape of the coronary artery and the strain difference between different time phases; and calculating morphological parameters of the plaques according to the positions, forms and texture structures of the plaques. And then calculating hemodynamic parameters of the coronary artery by adopting a computational fluid mechanics method. And finally, designing a comprehensive index to jointly judge the risk of the coronary plaque.

The embodiment of the invention relates to the field of multivariable data processing, in particular to a processing method and a processing system for multi-dimensional cardiac structure function data. According to the method, the data of the dynamic electrocardiogram, the photoplethysmography and the seismocardiogram are subjected to noise reduction processing through the feedforward neural network, the data of the dynamic electrocardiogram, the photoplethysmography and the seismocardiogram after noise reduction are obtained, and the credibility of the data is higher; fusing the dynamic electrocardiogram, the photoplethysmography and the seismocardiogram of different time sequence segments, and determining characteristic parameters; combining and splicing the respectively determined characteristic parameters according to a time sequence to obtain a multi-parameter matrix; and processing the multi-parameter matrix by using a multi-modal fusion network to obtain a processed multi-dimensional vector, mutually correcting a plurality of parameters through processing, and splicing the plurality of parameters to obtain a multi-dimensional vector to obtain a high-credibility parameter which better conforms to a real physiological law.

The invention provides a fetal heart region image segmentation method and system, electronic equipment and a storage medium, and the method comprises the steps: segmenting a current frame image of fetal heart body data through employing an inference network and a Canny edge detection algorithm; the fetal heart segmentation area of the current frame image is determined according to the obtained first fetal heart segmentation area and the second fetal heart segmentation area, and the inference network carries out inference based on the two-dimensional features and the three-dimensional features of the current frame image to obtain the first fetal heart segmentation area. The invention aims to combine two-dimensional information and three-dimensional information of an image so as to effectively solve the problems of non-fitting edges and poor reasoning effect of some frames in 3D segmentation, and a segmentation result of a Canny edge detection algorithm is used as a reference to help to judge and correct segmentation output generated by a reasoning network so as to improve the efficiency of 3D segmentation. By combining the advantages of the multi-source features and the Canny edge detection algorithm, the precision of complex heart structure segmentation is improved, and a more reliable segmentation result can be obtained.

The invention provides a mitral valve leaflet length automatic measurement method based on 3D-to-2D morphological transformation. The method comprises the following steps: firstly, acquiring three-dimensional medical image data of the heart of a patient, and extracting six partitions of a mitral valve and related heart structures; an orthogonal plane is calculated by fitting a mitral valve structure point set, and a cKDTree algorithm is used to determine a connected area of each partition, and a corresponding projection plane is established. And then extracting the contour of the projection point set through an Alpha shape algorithm, correcting the contour into a linear three-dimensional point set, and finally calculating the length of the valve leaflet of each partition. According to the method, the accuracy, consistency and efficiency of measurement are improved through an automation technology, personal errors are reduced, the method is suitable for various three-dimensional medical image data, and a more reliable basis is provided for clinical diagnosis and treatment of mitral valve diseases.

The invention discloses an ultrasonic image automatic quantitative evaluation method and system based on artificial intelligence, and relates to the technical field of medical image processing. And respectively acquiring a modal category of the ultrasonic image, a detection frame of each target structure, a structure type and / or a sampling line position and a detection frame of each cardiac cycle through the depth image classification model and the target detection model. And the section type of the ultrasonic image is accurately judged according to the data. According to the method, in combination with the modal category and section type of the ultrasonic image and the image type contained in the ultrasonic image, an appropriate quantitative analysis mode can be accurately provided for different ultrasonic sections, and a quantitative analysis result is visualized, so that a doctor can observe the result conveniently. According to the invention, comprehensive analysis of the multi-modal section can be realized, and more comprehensive and deep analysis and evaluation of the heart structure or function can be realized.

A heart image segmentation method based on a state space model and dual-path multi-scale enhancement relates to the technical field of image segmentation, and a state space attention module integrates an SSM state space model branch, a convolution branch and an attention branch, so that the recognition precision of the model on each structure boundary in a heart image is improved, and the recognition accuracy of the model is improved. And the unique advantage of processing the long-distance dependence while keeping the calculation complexity of linear correlation on the input size of the SSM is utilized, so that the heart image can be segmented more accurately and efficiently. The dual-path multi-scale enhancement module can enhance the recognition capability of the model for different scale structures through a channel aggregation branch and a multi-scale fusion branch, improves the adaptability and robustness of the model, and facilitates the processing of heart structures of various sizes. Therefore, the method can better adapt to the complexity and diversity of heart image segmentation, a segmentation result with higher precision and efficiency is provided, and the accuracy of heart disease diagnosis and treatment planning is remarkably improved.

The invention relates to the technical field of image processing, in particular to a cardiac ultrasound examination auxiliary system based on artificial intelligence, comprising: an image processing module comprising an acquisition unit for acquiring cardiac ultrasound dynamic images by using an ultrasound probe; the model training module is connected with the image processing module and comprises a model generation unit which is used for training an initial model according to the optimized image so as to output a convolutional neural network model; the feature extraction module is connected with the model training module and comprises a feature extraction unit which is used for extracting key features of the heart structure through a convolutional neural network model so as to output heart ultrasonic dynamic image features; and the control module is respectively connected with the image processing module, the model training module and the feature extraction module and is used for determining the pulse repetition frequency of the ultrasonic probe according to the artifact occurrence rate of the cardiac ultrasonic dynamic image. According to the invention, the processing stability of the cardiac ultrasonic dynamic image is improved.

The invention provides an ultrasonic evaluation system and method for heart segmental motion abnormality, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a continuous multi-frame echocardiogram of a patient; carrying out heart structure segmentation on each echocardiogram to obtain a plurality of heart segments, and respectively tracking and extracting motion features of each heart segment in a heart cycle to form a motion feature time sequence of each heart stage; and calculating a difference value between the motion feature time sequence and a baseline feature of a pre-constructed normal heart motion mode, and when the difference value exceeds a preset threshold value, outputting an evaluation result that the heart segment is in the abnormal motion mode. The method has the advantages that diagnosis efficiency is improved, workload of doctors is relieved, personal errors are reduced, accuracy, consistency and objectivity of diagnosis are improved, final evaluation results can provide effective reference for the doctors, diagnosis ability of primary doctors is improved, and dependence on experience of the doctors is reduced.

The invention relates to the technical field of medical image processing and deep learning crossing, and discloses a congenital heart disease whole heart segmentation method based on text guidance. According to the method, based on a U-Net architecture, a text-image alignment module is added between different levels of jump connections, and semantic information in report texts and CT image features are subjected to feature semantic alignment and fusion layer by layer; a multi-scale image feature fusion module is added between adjacent levels of the encoder, and the feature extraction capability of the encoder on each substructure is improved through mask adaptive fusion weight; a text-driven multi-level segmentation supervision module is added to a decoder part, and a segmentation result is guided and optimized by using semantic features, so that a constructed deep learning network effectively understands heterogeneity features of a congenital heart disease structure, and the structure distinguishing capability of a fuzzy boundary is enhanced; automatic segmentation of the congenital heart disease whole heart structure can be achieved, and effectiveness and accuracy of congenital heart disease whole heart segmentation are improved.

The invention discloses a heart CT image segmentation method and system based on ultrasonic guidance, and relates to the field of image processing, and the method comprises an uploading module which is used for uploading a heart CT original image and an ultrasonic image of a corresponding part, and synchronously building the spatial position correlation mapping of the heart CT original image and the ultrasonic image; the preparation module is used for performing denoising and gray normalization processing on the heart CT original image and the ultrasonic image respectively, and completing spatial alignment of the two types of images based on spatial position correlation mapping; according to the method, spatial position correlation mapping of the heart CT and the ultrasonic image is established, denoising and gray level normalization processing of the two types of images are combined, the image quality and the spatial alignment precision are effectively improved, CT segmentation interested areas are positioned by means of heart structure features extracted by the ultrasonic image, interference of irrelevant areas is avoided, and the accuracy of CT segmentation is improved. And during segmentation, image texture, gray gradient and structure edge features are fused, and accurate pixel-level segmentation of the heart target anatomical structure is realized.

The invention discloses a double-nested hollow-core anti-resonance optical fiber with an off-center structure, and relates to the technical field of optical communication. The double-nested hollow-core anti-resonance optical fiber sequentially comprises an air core, a first-stage cladding tube, a second-stage cladding tube, a third-stage cladding tube and a peripheral cladding tube from inside to outside, the first-stage cladding tube and the peripheral cladding tube are fixed in an internally tangent manner, the second-stage cladding tube and the first-stage cladding tube are fixed in an internally tangent manner, and the third-stage cladding tube and the second-stage cladding tube are fixed in an internally tangent manner; the optical fiber is of an off-center structure which is vertically symmetrical and bilaterally asymmetrical. Compared with the conventional hollow-core anti-resonance optical fiber, the hollow-core anti-resonance optical fiber with specific structure parameters provided by the invention solves the problem that the conventional hollow-core anti-resonance optical fiber cannot well solve the problem of high bending loss under the condition of extremely short bending radius; the hollow-core optical fiber has relatively low confinement loss and bending loss, and has relatively great application potential in particle sensing and micro lasers.

The embodiment of the application discloses an echocardiogram auxiliary report generation method, system, device and storage medium, and relates to the technical field of medical treatment. The echocardiogram auxiliary report generation method first aligns the input echocardiogram video through automatic registration, then identifies the starting time of each cardiac cycle in the echocardiogram video, generates the echocardiogram video frame of each cardiac cycle based on a preset frame number, divides the heart structure in the echocardiogram video frame into N target images of target regions by using a medical image segmentation model, obtains a structure parameter, inputs the structure parameter into an echocardiogram knowledge graph to automatically generate an auxiliary report, including an auxiliary diagnosis report and / or an auxiliary interpretation report. Therefore, doctors do not need to manually measure and fill in related clinical parameters or diagnosis opinions, and the health status of patients can be understood, and the working pressure of doctors is effectively reduced.

The utility model relates to an expandable artificial valve prosthesis which is characterized in that channels are formed in an elastic valve prosthesis, and a Tesla valve structure is arranged in each channel; an instrument guide hole is formed in the elastic valve prosthesis and can be communicated with the inflow end and the outflow end; the end face of the inflow end and the end face of the outflow end are each provided with an elastic sealing body used for closing the instrument guide hole, each elastic sealing body is provided with a disconnection part, and the disconnection parts are in a normally-closed state under the elastic force action of the elastic sealing bodies so as to close the instrument guide holes. A transition layer is circumferentially arranged on the outer wall of the elastic valve prosthesis in a surrounding manner, and the transition layer has plasticity; the valve frames are arranged at the inflow end and the outflow end respectively and connected with the transition layer, or the valve frames are directly and fixedly connected with the elastic valve prosthesis. The expandable artificial valve prosthesis disclosed by the utility model can be expanded, so that the overall diameter size of the expandable artificial valve prosthesis disclosed by the utility model is changed, and the expandable artificial valve prosthesis can be adapted to heart structure tissues with larger sizes.

Devices, systems, and methods for releasing a catheter from an implant may include a catheter comprising first and second elongate elements, first and second elongate element lumens, a tissue anchor lumen, apertures corresponding to each of the lumens, and retaining portions between adjacent apertures. The first elongate element may extend out of its lumen through the first elongate element aperture, across the retaining portion transversely with respect to a longitudinal axis, and towards the second elongate element aperture. The first elongate element may then extend into the second elongate element aperture, loop over the second elongate element, and extend back across the retaining portion and into the first elongate element lumen. In some instances, the second elongate element is retracted to uncouple the first and second elongate elements from each other and to open a channel for the release of tethered tissue anchors.