24 results about "End systole" patented technology

The invention relates to a simulation-based target removal area determination method and device. The method comprises the following steps: acquiring a heart simulation model and corresponding blood flow field information; according to the left heart lumen model and the blood flow field information, determining a first target area in the ventricular septum model; stress calculation is carried out according to the ventricular septum model, and a second target area in the ventricular septum model is determined; determining an initial target area according to the first target area and the second target area; according to the ventricular septum model, determining the thickness range of the initial target area at the end of contraction; determining a target cutting thickness according to a preset basic thickness and a thickness range; according to the heart simulation model, the initial target area, the target resection thickness and the blood flow field information, a first target resection area is determined. By adopting the method, the cardiac muscle tissue area needing to be cut off can be accurately determined.

The invention discloses an aortic valve function evaluation device which comprises a processor and a memory, and the memory stores programs or instructions capable of running on the processor. When the program or the instruction is executed by the processor, the following steps are realized: acquiring a first echocardiogram of a target object corresponding to a final systole and a second echocardiogram of a target object corresponding to a final diastole in a cardiac cycle, and acquiring medical record data of the target object; determining a first mask pattern corresponding to the first echocardiogram and a second mask pattern corresponding to the second echocardiogram according to the first echocardiogram, the second echocardiogram and an aortic valve segmentation model; determining an aortic valve morphological index of the target object according to the first mask pattern and the second mask pattern; determining aortic valve functional indexes of the target object according to the medical record data of the target object; and determining and outputting an aortic valve function evaluation result of the target object according to the first mask pattern, the second mask pattern, the aortic valve morphological index and the aortic valve functional index.

Existing techniques fail to propose a method for improving model parameters optimization and seamless integration of clinical data with computational model required for personalized cardiac hemodynamic model development. This disclosure relates to a system and method, which receives one or more model parameters comprising height, active pressure components and passive pressure components specific to left ventricle from cardiac hemodynamic model. The first set of values corresponding to one or more input parameters comprising end systolic diameter and end diastole diameter are received from subject specific Echo data. The second set of values corresponding to one or more model parameters are estimated using one or more input parameters. The estimated second set of values are optimized using a Particle swarm optimization to obtain one or more optimized values. Select at least a subset of optimized one or more values corresponding to one or more model parameters for personalization.

The invention belongs to the technical field of medicines, and particularly relates to application of indole-3-propionic acid in preparation of a medicine for treating myocardial ischemia-reperfusion injury. A stable and reliable mouse MIRI model is successfully constructed through a coronary artery ligation left anterior descending branch reperfusion method, and it is found through indole-3-propionic acid (IPA) intervention and multi-dimensional evaluation that IPA administration can significantly improve the left ventricular ejection fraction LVEF and the left ventricular short-axis shortening rate LVFS of a model mouse, reduce the left ventricular end diastolic inner diameter LVEDd and the end systolic inner diameter LVESd, and significantly improve the model mouse left ventricular ejection fraction LVEF, the left ventricular end diastolic inner diameter LVEDd and the left ventricular end diastolic inner diameter LVESd. The heart function is effectively improved; the myocardial fibrosis area can be reduced, the mRNA expression of fibrosis-promoting and pathological hypertrophy-promoting molecules in myocardial tissues can be obviously reduced, and the myocardial injury progress can be inhibited in a multi-dimensional manner. The invention provides a brand new strategy for treating myocardial ischemia reperfusion injury, has important application value and wide clinical transformation prospect, and is expected to provide a new direction for clinical treatment.

The invention discloses a cardiac magnetic resonance T1mapping image synthesis method, which comprises the following steps of: acquiring a plurality of groups of samples, each group comprising native T1mapping images and movie imaging sequence images, determining a heart region corresponding to each native T1mapping image, identifying initial associated image pairs of end diastole and end systole from the same group of movie images according to the heart region corresponding to each native T1mapping image, and synthesizing the initial associated image pairs of the end diastole and end systole according to the initial associated image pairs of the end diastole and end systole. Obtaining a target associated image pair corresponding to the heart preset region after segmentation, and training the constructed initial diffusion model to obtain a target model; respectively extracting myocardial tissue texture features and heart morphological dynamic features in a specific period from each film imaging sequence image; and inputting the tissue texture features and the heart morphological dynamic features into a target model, and performing inverse diffusion denoising on random noise randomly sampled from Gaussian distribution to obtain a synthesized T1mapping image. According to the method, rapid, efficient and high-quality T1mapping image synthesis can be realized.

The invention provides a method for measuring the left ventricular ejection fraction of a dynamic echocardiography. The method comprises the following steps: inputting a to-be-identified echocardiogram sequence into a neural network segmentation model based on multi-frame feature aggregation, then performing spatial feature coding on each frame of image of the to-be-identified echocardiogram sequence through a spatial feature encoder structure to obtain a spatial feature map corresponding to each frame of image, and finally, identifying the to-be-identified echocardiogram sequence according to the spatial feature map. Performing cross-frame and cross-section spatio-temporal feature aggregation on the spatial feature pattern through a spatio-temporal feature aggregation structure to obtain global spatio-temporal features including time sequence information of a heart apex two-cavity section and a heart apex four-cavity section; the left ventricular ejection fraction determining method comprises the steps of generating a left ventricular membrane segmentation mask of a terminal diastole frame and a terminal systole frame on the basis of global spatial-temporal characteristics and spatial shallow layer characteristics corresponding to the terminal diastole frame and the terminal systole frame, and finally determining the left ventricular ejection fraction on the basis of the left ventricular membrane segmentation mask of the terminal diastole frame and the terminal systole frame.

The invention discloses a method and a system for removing breathing artifacts of an animal micro-CT image. The method comprises the following steps: preprocessing projection data of an animal to obtain label information; training label information by using a depth target detection network to obtain thoracic cavity diaphragm positions, classifying the thoracic cavity diaphragm positions, extracting target limiting projections at the end of diastole and the end of systole, and performing unequal interval filtering back projection reconstruction on the target limiting projections to obtain a breathing artifact-removed image; correcting the breathing artifact-removed image by using an improved deep noise reduction network to obtain a breathing and stripe artifact-removed image; in-phase projection is extracted in a projection domain and is reconstructed, so that respiratory artifacts are effectively removed from the source; and the improved deep noise reduction network is used to enhance the quality of the sparse reconstruction image, so that the image quality is remarkably improved, and a clearer and more accurate image basis is provided.

The present application relates to an ultrasonic diagnostic apparatus and an electrocardiogram waveform processing method. A setter (42) sets a plurality of heart rate intervals for an electrocardiogram waveform. A primary determiner (44) determines an interval satisfying a primary determination condition as a temporary stable interval within the plurality of heart rate intervals. A secondary determiner (46) determines the temporary stable interval as a stable interval in a case where the temporary stable interval satisfies a secondary determination condition. An end-diastole detector (48) and an end-systole detector (50) detect an end-diastole and an end-systole within a focus interval. A selection control section (33) controls selection of the focus interval at a time of change of the focus interval so that the changed focus interval becomes the stable interval.

This application provides an ultrasound cardiac dynamic monitoring system and method, relating to the field of ultrasound cardiac function dynamic monitoring technology. The system includes: a patch-type ultrasound probe, a hydrogel patch, a wearable hardware module, and a cardiac function analysis module. The cardiac function analysis module is configured to: receive the ultrasound image; determine a target sampling line in the ultrasound image based on a reinforcement learning model; control the wearable hardware module to acquire M-mode images along the target sampling line; input the M-mode images into a deep learning model to output the key point positions of the left ventricular septum and the left ventricular posterior wall at end-diastole and end-systole based on the deep learning model; calculate the left ventricular ejection fraction (LVEF) based on the LVEF and the key point positions; the LVEF represents the dynamic monitoring and detection results of cardiac function. This system can improve the effectiveness and accuracy of dynamic cardiac monitoring.

An ultrasonic measurement method and an ultrasonic imaging system for cardiac parameters, the method comprising: under the condition that an ultrasonic probe is guided into a cavity of a patient, controlling the ultrasonic probe to emit ultrasonic waves to the heart of the patient to obtain a first cardiac section; controlling the ultrasonic probe to obtain a first imaging angle of the first heart section to emit ultrasonic waves so as to obtain a first end diastole ultrasonic image and a first end systole ultrasonic image; controlling the driving mechanism to enable the ultrasonic probe to rotate from the first imaging angle to a second imaging angle for acquiring a second heart section, wherein the angle difference between the second imaging angle and the first imaging angle is a preset angle; controlling the ultrasonic probe to emit ultrasonic waves at a second imaging angle to obtain a second end diastole ultrasonic image and a second end systole ultrasonic image; and calculating the cardiac ejection fraction according to the first end-diastolic ultrasound image, the first end-systolic ultrasound image, the second end-diastolic ultrasound image and the second end-systolic ultrasound image.

The present invention provides a method, device, and ultrasonic imaging device for determining left ventricular parameters. The method comprises: collecting two-dimensional ultrasonic cardiac data of a target subject over a continuous period of time, the continuous period being greater than one cardiac cycle, the two-dimensional ultrasonic cardiac data comprising multiple frames of two-dimensional ultrasonic cardiac images; determining multiple target frame images from the multiple frames of two-dimensional ultrasonic cardiac images, the target frame images comprising multiple frames of end-diastolic images corresponding to the end-diastolic phase and multiple frames of end-systolic images corresponding to the end-systolic phase; performing image segmentation on the target frame images to determine the left ventricular contour of the target frame images; determining the apex point, the junction point between the left ventricular septum and the mitral valve, and the junction point between the left ventricular wall and the mitral valve in the left ventricular contour; and determining left ventricular parameters based on the left ventricular contour, the apex point, the junction point between the left ventricular septum and the mitral valve, and the junction point between the left ventricular wall and the mitral valve. The present invention balances the efficiency and accuracy of left ventricular measurement.

The invention discloses a heart ultrasound video left ventricle segmentation and ejection fraction calculation method based on a DeepLabv3 + and R (2 + 1) D network, and belongs to the field of medical artificial intelligence. The method aims at solving the problems that in the prior art, left ventricle function evaluation time sequence information is insufficient in utilization, the key frame recognition automation degree is low, and the segmentation precision and the overall process automation degree are not high. According to the technical scheme, the method comprises the following steps: preprocessing an original cardiac ultrasound video, including frame extraction, intelligent cutting of a region of interest and data enhancement; performing time sequence modeling on a video sequence by using an R (2 + 1) D network, and automatically identifying key frames of the end of diastole (ED) and the end of systole (ES); performing high-precision pixel-level left ventricle region segmentation on the identified key frame by adopting a DeepLabv3 + network; estimating a left ventricular end diastolic volume (LVEDV) and a left ventricular end systolic volume (LVESV) by combining an improved Simpson method on the basis of a segmentation result and image physical size calibration information; and finally, calculating a left ventricular ejection fraction (LVEF) according to a standard formula, and outputting a structured report and a visual image. Through multi-network collaboration and full-process automation, the accuracy, efficiency, objectivity and consistency of LVEF evaluation are remarkably improved, and the method has a good clinical application prospect.

An apparatus for monitoring a patient post operation having electrically conducting leads which are adapted to extend from inside the patient. The leads having electrodes adapted to communicate with a heart of the patient and apply electrical signals to the heart. The electrodes providing cardiac signals to the computer in response to the electrical signals so the computer can determine in real time at least one of heart volume, end diastolic heart volume, end systolic heart volume, stroke volume, change in heart volume, change in stroke volume, contractility, respiration rate or tidal volume regarding the patient.

The application discloses a kind of based on registration point set cardiac deformation field estimation method, system, device and medium, solve the precision of image registration in prior art needs to be improved, method includes: receiving diastolic end and systolic end cardiac image and its segmentation map;The segmentation map of diastolic end and systolic end cardiac image is used to extract the contour of heart, and the registration point set corresponding to the contour of heart image center is obtained using farthest point sampling algorithm;Diastolic end and systolic end cardiac image and the registration point set are input into the variational neural network trained in advance, and the distribution of latent variable is obtained;The distribution of latent variable is input into the radial basis function constructed in advance, and the deformation field of diastolic end image is decoded to obtain, and the deformed diastolic end image is obtained according to the deformation field.This application improves the image registration precision of cardiac deformation field.

The application discloses a method, system, device, medium and product for evaluating right ventricular function, and relates to the technical field of medical image processing and ultrasonic diagnosis. The method comprises the following steps: acquiring a sequence of multiple two-dimensional ultrasonic images of a right ventricle of a heart and spatial pose information of the sequence, and generating point cloud data; identifying an end-diastolic frame, an end-systolic frame and an anatomical structure point, and acquiring sparse point cloud data of the anatomical structure point; constructing a three-dimensional grid model of the end-diastolic frame and the end-systolic frame; determining multiple tracking points of a tricuspid annulus and acquiring a three-dimensional coordinate trajectory; calculating longitudinal displacement, area change rate and peak time dispersion based on the trajectory, and completing function evaluation. The application overcomes the limitations of angle dependence and point substitution of traditional TAPSE technology, realizes comprehensive evaluation of right ventricular function from three dimensions of longitudinal displacement, area change rate and systolic synchrony, and improves the accuracy and clinical value of evaluation.

The invention discloses a method, a system, equipment, a medium and a product for evaluating a right ventricular function, and relates to the technical field of medical image processing and ultrasonic diagnosis. The method comprises the following steps: acquiring a multi-frame two-dimensional ultrasonic image sequence of a right ventricle of a heart and spatial pose information of the multi-frame two-dimensional ultrasonic image sequence, and generating point cloud data; identifying the last diastole frame, the last systole frame and the anatomical structure point, and obtaining sparse point cloud data of the anatomical structure point; constructing a three-dimensional grid model of the last diastole frame and the last systole frame; determining a plurality of tracking points of the tricuspid valve ring and acquiring a three-dimensional coordinate trajectory; and calculating the longitudinal displacement, the area change rate and the peak arrival time dispersion based on the trajectory to complete the function evaluation. According to the method, the limitation that a traditional TAPSE technology depends on a point instead of a surface is overcome, comprehensive evaluation of the right ventricular function from the three dimensions of longitudinal displacement, the area change rate and contraction synchronism is achieved, and the accuracy and clinical value of evaluation are improved.

The invention relates to an aortic valve pressure gradient determination method and device and a storage medium, and the method comprises the steps: obtaining a first pressure gradient of a target object, a first cardiac image corresponding to the end systole in a cardiac cycle, a second cardiac image corresponding to the end diastole in the cardiac cycle, and a blood flow velocity time sequence in the cardiac cycle; determining cardiovascular characteristics of the target object according to the first cardiac image, the second cardiac image and the blood flow velocity time sequence; inputting the first pressure gradient and the cardiovascular characteristics into a pressure gradient correction model to obtain a second pressure gradient after the first pressure gradient is corrected; wherein the pressure gradient correction model is used for determining a pressure gradient correction amount according to the cardiovascular characteristics, and correcting the first pressure gradient according to the pressure gradient correction amount.

The application relates to a simulation-based target resection region determination method and device. The method comprises the following steps: obtaining a heart simulation model and corresponding blood flow field information; determining a first target region in an interventricular septum model according to a left heart lumen model and the blood flow field information; performing stress calculation according to the interventricular septum model to determine a second target region in the interventricular septum model; determining an initial target region according to the first target region and the second target region; determining a thickness range of the initial target region at a time point of the end-systole according to the interventricular septum model; determining a target resection thickness according to a preset basic thickness and the thickness range; and determining a first target resection region according to the heart simulation model, the initial target region, the target resection thickness and the blood flow field information. The method can accurately determine a myocardial tissue region required to be resected.

Embodiments of the present invention provide a system for automatically deriving parameters of a human heart from ultrasound results. The first neural network is arranged to receive a plurality of echocardiography images and to classify the images as one of at least a two-lumen tangent plane and a four-lumen tangent plane. The second neural network is arranged to receive echocardiography images comprising two or four-lumen tangent planes and to identify endocardial boundaries of a left ventricle (LV) for each tangent plane. End systolic and end diastolic images are then identified and parameters such as LV volume, ejection fraction, overall longitudinal strain, and regional longitudinal strain are calculated.

The invention belongs to the field of biological medicines, and particularly relates to application of astragaloside in preparation of a medicine for treating myocardial remodeling after myocardial infarction. In-vivo and in-vitro experiments prove that the astragaloside can up-regulate the expression of Acox1, Cd36, Pgc1a and Cpt1a lipid metabolism related factors and improve lipid metabolism disorder after myocardial infarction; meanwhile, Acta2, Col1a1, Col3a1, Postn fibrosis factors and alpha-SMA expression are inhibited, and myocardial fibrosis after myocardial infarction is relieved; the left ventricular ejection fraction and the left ventricular short axis shortening rate can be remarkably increased, the left ventricular end diastolic inner diameter and the left ventricular end systolic inner diameter are reduced, and cardiac dysfunction caused by myocardial infarction is improved. The composition can block pathological circulation of lipid metabolism disorder, myocardial fibrosis and cardiac function decline in a targeted manner, the curative effect is dose-dependent, the safety is good, and a brand new effective drug choice is provided for clinical treatment of myocardial remodeling after myocardial infarction.

The invention discloses an ultrasonic measurement method and system for the three-dimensional volume and ejection fraction of a cardiac cavity, and relates to the field of ultrasonication.The measurement method comprises the following steps that S1, a low-frequency ultrasonic cardiac cavity image and a high-frequency ultrasonic cardiac cavity image are obtained respectively; s2, suppressing high-frequency image noise, adjusting high-frequency feature weight, screening continuous edge segments, and reserving continuous endocardium boundaries; s3, superposing the low-frequency contour and the high-frequency detail features to generate a fused image; s4, segmenting the endocardium boundary of the fused image; and S5, based on the segmented boundary data, calculating the volume and ejection fraction of the end diastole / end systole. According to the method, the low-frequency and high-frequency ultrasonic images are synchronously collected, the fusion weight is dynamically adjusted by monitoring the signal-to-noise ratio in real time, the low-frequency image provides the overall contour of the cardiac cavity, the high-frequency image focuses endocardium details, excessive superposition of high-frequency features in the noise environment is effectively avoided, the stability of endocardium boundary recognition is further guaranteed, and the recognition accuracy is improved. Therefore, the accuracy of measuring the three-dimensional volume and ejection fraction of the heart cavity is improved.

The present application generally relates to systems and methods for synchronizing deployment of various devices into cardiac tissue with ECG waveforms. Systems and methods may include using pressurized gas to actuate deployment and anchoring of a device (e.g., an implant configured to reshape a heart ventricle) into heart tissue. Deployment of the device may occur during a portion of the cardiac cycle where cardiac muscle tissue may be at its thickest (e.g., at the end of systole).

The invention relates to a mitral valve function evaluation device which comprises a processor and a memory, and the memory stores programs or instructions capable of being executed on the processor. When the program or the instruction is executed by the processor, the following steps are realized: acquiring a first echocardiogram of a target object corresponding to a final systole, a second echocardiogram of a target object corresponding to a final diastole and a blood flow velocity time sequence; determining a first mask pattern corresponding to the first echocardiogram and a second mask pattern corresponding to the second echocardiogram according to the first echocardiogram, the second echocardiogram and a mitral valve segmentation model; according to the first mask pattern and the second mask pattern, mitral valve structural features are determined; determining mitral valve calcification features according to a calcification region of interest in the first echocardiogram; according to the blood flow velocity time sequence, mitral valve blood flow characteristics are determined; and determining a mitral valve function evaluation result of the target object according to the first mask pattern, the second mask pattern, the mitral valve structural features, the mitral valve blood flow features and the mitral valve calcification features.

The invention discloses an aortic valve function evaluation device which comprises a processor and a memory, and the memory stores programs or instructions capable of running on the processor. When the program or the instruction is executed by the processor, the following steps are realized: acquiring a first cardiovascular three-dimensional diagram corresponding to the end systole and a second cardiovascular three-dimensional diagram corresponding to the end diastole of a target object in a cardiac cycle, and medical record data of the target object; according to the first cardiovascular three-dimensional diagram, the second cardiovascular three-dimensional diagram and an aortic valve segmentation model, determining a first three-dimensional mask diagram corresponding to the first cardiovascular three-dimensional diagram and a second three-dimensional mask diagram corresponding to the second cardiovascular three-dimensional diagram; according to the medical record data, determining aortic valve hemodynamic indexes of the target object; and inputting the first three-dimensional mask map, the second three-dimensional mask map and the aortic valve hemodynamic indexes into an aortic valve function evaluation model to obtain and output an aortic valve function evaluation result of the target object.