17 results about "Left ventricular myocardium" patented technology

The disclosure deals with methodology and corresponding apparatus / system subject matter for image-based estimation of left ventricular myocardial stiffness. Increased left ventricular myocardial stiffness is a key factor in the development and progression of heart failure. Despite the potential impact on the clinical management of heart failure, there is currently a lack of available techniques to assess left ventricular myocardial stiffness. To address this limitation, a simple protocol is disclosed for processing routine echocardiographic imaging data to estimate left ventricular myocardial stiffness, with protocol specification for patients at risk for heart failure with preserved ejection fraction, for both sensitivity and translational feasibility of the obtained estimates of left ventricular myocardial stiffness.

Disclosed are a method for presenting cardiac myocardial strain and an ultrasound imaging apparatus, comprising: acquiring image data of myocardial segments of both left and right ventricles, and processing this data to obtain motion parameters of both left and right ventricular myocardial segments. A composite bull's-eye plot containing left and right ventricular bull's-eye subplots is displayed, wherein the left ventricular bull's-eye subplot comprises multiple left ventricular myocardial regions corresponding to the left ventricular myocardial segments and presenting motion parameters of the left ventricular myocardial segment; and the right ventricular bull's-eye subplot comprises a right ventricular myocardial region corresponding to the right ventricular myocardial segments that exclude some / all shared segments with the left ventricle and presenting motion parameters of the right ventricular myocardial segment. This configuration enables users to observe motion parameters of both ventricular myocardial segments, thereby providing comprehensive understanding of cardiac myocardial strain while enhancing clinical efficiency for doctors.

To provide suitable devices, systems, and methods for percutaneous pneumatic cardiac support. [Solution] The cardiac support system includes a pneumatic effector implanted beneath the pericardium and across the myocardial surface covering the patient's left ventricle. A port receives an implanted, percutaneously introduced cannula. The port is connected to supply the drive gas received from the cannula to the pneumatic effector. An external drive unit includes a pump assembly and a control circuit that operates the pump to activate the pneumatic effector in response to the patient's sensed cardiac rhythm. A connecting tube has a pump end connected to the pump and a percutaneous port connection end attached to the implantable port.

The application provides a cardiac phase judgment device, method, medium and electronic equipment. The device comprises: a medical image acquisition module, configured to acquire a plurality of three-dimensional medical images of a target object in at least one cardiac cycle; a myocardial wall thickness acquisition module, configured to acquire the thickness of a left ventricular myocardial wall in each frame of the three-dimensional medical images; and a cardiac phase judgment module, configured to acquire the cardiac phase of the target object according to the thickness of the left ventricular myocardial wall in each frame of the three-dimensional medical images. The cardiac phase judgment device only needs to acquire the cardiac phase of the target object according to the three-dimensional medical images of the target object, and is simpler to implement.

This invention discloses an automatic identification and classification method for scarred myocardium based on cine magnetic resonance imaging (CMRI) and deep learning. After preprocessing cardiac cMRI images, the method extracts the relative motion and imaging features of the left ventricular myocardium. A multimodal feature fusion network is then used for training and prediction to identify and classify five different types of scarred myocardium. The extraction of relative motion and imaging features enables an accurate and detailed description of the state of scarred myocardium. The multimodal feature fusion network model captures the compensatory effects of long-distance scarred myocardium movement, the non-rigid deformation motion patterns of the myocardium, and the spatiotemporal relationship between myocardial motion and morphology. This invention achieves high efficiency and accuracy in identifying and classifying scarred myocardium, saving doctors time and effort in diagnosing cardiovascular diseases leading to scarred myocardium, reducing patient suffering, and facilitating a more stable and simpler examination and diagnostic method.

The application discloses a left ventricular myocardium curved surface registration method based on two-dimensional parameter domain mapping. The method comprises the following steps: segmenting and reconstructing a dynamic three-dimensional curved surface sequence of left ventricular myocardium from a cardiac magnetic resonance short-axis image sequence, and then performing standardization processing on the dynamic three-dimensional curved surface sequence to obtain a target curved surface model; mapping a key frame curved surface in the target curved surface model to a pre-defined two-dimensional parameter domain to obtain global deformation parameters; and mapping the remaining frames of the target curved surface model in the whole cardiac cycle to the two-dimensional parameter domain according to the global deformation parameters. The method drives the mapping process of the whole sequence by using the global deformation parameters of a key frame curved surface, can establish stable space-time vertex corresponding relationships for the curved surfaces of all time frames on the two-dimensional parameter domain, realizes time sequence alignment of the curved surface sequence in the whole cardiac cycle and normalized registration of myocardium shapes among different cases, can effectively solve problems such as large shape difference among individuals and unclear characteristics, and realizes accurate registration of left ventricular myocardium curved surfaces.

The application provides a kind of magnetic resonance image myocardial strain determination method and system based on group registration algorithm, comprising: step S1: left ventricular myocardium is segmented from diastolic end image of cardiac magnetic resonance movie image, and the image sequence is preprocessed according to the segmentation result, and the direction field is calculated;Step S2: using group registration algorithm based on global or local low rank difference measure to register image sequence, and estimate displacement field;Step S3: left ventricular myocardial strain related parameters are calculated using direction field and displacement field.The application uses group registration based method to eliminate cumulative error, so that myocardial strain can be more accurately determined, especially the strain in the later diastolic phase.The global or local low rank difference used in the method can more accurately measure the motion change between multiple movie images, and is more robust to artifacts.In addition, the joint use of spatial regularization term and cycle time regularization term further improves the accuracy of the method.

The application discloses an evaluation and diagnosis system for myocardial fibrosis based on photon CT energy scanning and belongs to the technical field of myocardial fibrosis evaluation. The evaluation and diagnosis system for myocardial fibrosis based on photon CT energy scanning comprises a scanning data acquisition module, a full-automatic heart segmentation module, a biological feature recognition module, a fibrosis region recognition module and a multi-parameter quantitative analysis module. The application solves the problem that the prior art has weak boundary distinguishing ability for myocardium, blood vessels and valves, and is prone to cause segment division deviation. The application realizes automatic and accurate segmentation of left ventricular myocardium, ensures the accuracy of standard 17-segment model division, greatly improves the speed and efficiency of fibrosis region recognition, effectively reduces manual operation intervention, reduces human error, improves the efficiency and accuracy of diagnosis, can comprehensively quantitatively analyze each myocardial segment and output rich key indicators.

This application discloses a method and apparatus for determining myocardial strain parameters based on 4DCTA images. The method includes: using a Mamba-based neural network to separate a first myocardial region image sequence from an original image sequence; performing motion correction on the myocardial region images in the first myocardial region image sequence to eliminate motion artifacts, resulting in a second myocardial region image sequence; obtaining the deformation field of the left ventricular myocardium region between consecutive myocardial region image frames in the second myocardial region image sequence through motion tracking; performing layering and block segmentation on the myocardial region images in the second myocardial region image sequence; and determining myocardial strain parameters using the deformation field of the left ventricular myocardium region between consecutive myocardial region image frames in the second myocardial region image sequence and the layering and block segmentation results of the myocardial region images in the second myocardial region image sequence. This application solves the technical problem of low efficiency in myocardial strain processing in related technologies.

According to the cardiac myocardial strain presentation method and the ultrasonic imaging equipment, the image data of at least one myocardial segment of the left ventricle and the right ventricle of the heart are obtained, and then the image data are processed to obtain the motion parameters of the myocardial segments of the left ventricle and the right ventricle. Displaying a composite bullseye diagram comprising a left ventricle bullseye sub-diagram and a right ventricle bullseye sub-diagram; wherein the left ventricle bull eye sub-graph comprises a plurality of left ventricle myocardial areas corresponding to a plurality of myocardial segments of the left ventricle, and motion parameters of the left ventricle myocardial segments are presented in the corresponding left ventricle myocardial areas; the right ventricle bull eye sub-graph comprises a right ventricle myocardial region corresponding to the myocardial segment of the right ventricle except for the myocardial segment which is partially or completely shared with the left ventricle, and the motion parameters of the right ventricle myocardial segment are presented in the right ventricle myocardial region corresponding to the right ventricle myocardial segment. A user can see motion parameters of left and right ventricular myocardium, so that the myocardial strain condition of the heart can be more comprehensively known, and the working efficiency of a doctor is improved.

The present invention relates to an implantable lead assembly comprising an implantable first lead, an implantable second lead, and a control unit, to which the first and the second leads are electrically connected through connector lines, wherein the control unit is configured to establish a potential difference between the first and second leads so that an electric current can flow between the two leads, the first lead is configured to be positioned in the right ventricle of the heart, and the second lead comprises a coil comprising an uninsulated portion at the distal end thereof, wherein the uninsulated portion is configured to be positioned in the coronary sinus and in a left lateral vein in the left ventricular myocardium, and optionally a central lumen passing longitudinally through said coil.

The invention belongs to the field of digital medical treatment, and particularly relates to a method for automatically selecting myocardial stimulation sites based on an AHA standard. According to the method for automatically selecting myocardial stimulation sites based on the standard of 17 sections of myocardial areas proposed by AHA, left ventricular myocardium is divided into 17 sections, right ventricular myocardium is additionally divided into 10 sections, and then surface stimulation sites and internal stimulation sites of all the sections are automatically selected; a simulation stimulation site closer to a clinical result can be provided for simulation of a three-dimensional personalized heart model. According to the method, various parameters of a model are obtained through interpolation images of a three-dimensional personalized heart model and structure judgment of the interpolation images, then classification of left and right myocardial areas is carried out according to the myocardial interpolation images with classified left and right myocardium and an AHA standard, and then selection of myocardial stimulation sites is carried out by using area classification results; the method is mainly suitable for a selection process of stimulation sites in simulation of a three-dimensional personalized heart model.

The invention discloses a left ventricular myocardium deformation quantification method and system. The method comprises the following steps: obtaining a dynamic three-dimensional curved surface sequence of left ventricular myocardium in a complete heartbeat cycle; obtaining a Beltrami coefficient corresponding to each fixed anatomical point in each time frame according to a mapping relation between each frame in the three-dimensional curved surface sequence and a unified parameter domain, and obtaining a local deformation scalar of each frame in the three-dimensional curved surface sequence based on the coefficient; and constructing an intrinsic deformation trajectory curve of each fixed anatomical point on the unified parameter domain in the cardiac cycle, and determining a deformation feature vector according to the curve. According to the method, homeomorphic mapping from a myocardial curved surface at each time point to a unified parameter domain is established, three-dimensional motion is coded into sequential evolution of a Beltrami coefficient on a two-dimensional parameter point, the certainty of mapping is utilized to replace the estimability of frame-by-frame registration in a traditional three-dimensional space, drift and accumulative errors of corresponding points are fundamentally and effectively avoided, and the accuracy of the method is improved. And accurate quantification with high time-space consistency on myocardial deformation can be realized.

The present invention discloses a cardiac magnetic resonance cardiac function assessment method based on multi-task learning, which belongs to the technical field of medical image processing. The present invention labels, enhances and preprocesses cardiac magnetic resonance image data; then uses the pre-processed data to train a multi-task learning model, introduces regional geometric consistency loss to supervise the multi-task training process, and improves the accuracy and generalization performance of the model; then inputs the cardiac magnetic resonance image to be evaluated into the trained multi-task learning model, obtains the left and right ventricular endocardial segmentation results in the cardiac anatomical structure, the left ventricular myocardial segmentation results, and the right ventricular insertion point and left ventricular center point in the cardiac key point position, further calculates the ventricular volume to obtain the left and right ventricular ejection fraction; calculates the thickness of each segment of the left ventricular myocardium; and then outputs the cardiac function assessment result. The present invention improves the accuracy and generalization performance of the model, and solves the problems of low accuracy and long analysis time of traditional methods.

The present invention relates to an implantable lead assembly comprising an implantable first lead, an implantable second lead, and a control unit, to which the first and the second leads are electrically connected through connector lines, wherein the control unit is configured to establish a potential difference between the first and second leads so that an electric current can flow between the two leads, the first lead is configured to be positioned in the right ventricle of the heart, and the second lead comprises a coil comprising a central lumen passing longitudinally therethrough and an uninsulated portion at the distal end thereof, wherein the uninsulated portion is configured to be positioned in the coronary sinus and in a left lateral vein in the left ventricular myocardium.

A method of delivering a pacing lead may include locating a potential implantation site adjacent to or within the triangle of Koch region of a patient's heart. The method may include advancing a pacing lead to the potential implantation site. The pacing lead has an elongate body and a fixation element coupled to a distal portion and attachable to the right-atrial endocardium adjacent to or within the triangle of Koch region. The method may include implanting the pacing lead at the potential implantation site to or sense electrical activity of the left ventricle in the basal and / or septal region of the left ventricular myocardium of the patient's heart. The pacing lead may include a lumen configured to receive a guide wire. A sheath of a delivery system used to deliver the pacing lead may include two or more curves to facilitate implanting the pacing lead at the implantation site.

The application provides a target heart map generation model training and generation method, system, device and storage medium, and belongs to the technical field of medical image recognition. The target heart map generation model training method comprises the following steps: acquiring a three-dimensional heart function image and a three-dimensional heart anatomical image; constructing a heart target heart map generation model, wherein the heart target heart map generation model comprises a heart short-axis position positioning network and a left ventricular myocardium segmentation network; comparing the three-dimensional heart anatomical image, and jointly training the heart short-axis position positioning network and the left ventricular myocardium segmentation network by using the three-dimensional heart function image to obtain a trained heart target heart map generation model. In the case that the left ventricular myocardium contour in the three-dimensional heart function image is incomplete, the three-dimensional heart function image and the three-dimensional heart anatomical image are compared, the training of the three-dimensional heart function image by the target heart map generation model is completed, and the automatic generation of the heart target heart map is realized.