25 results about "Cardiac motion" patented technology

The invention relates to the technical field of medical image processing, in particular to an engineered heart tissue pulsation trajectory automatic analysis method based on an image recognition algorithm, which comprises the following steps: S1, multi-modal image fusion: performing space-time registration and feature fusion on acquired multi-modal heart images to generate a fused image sequence; s2, cardiac muscle tissue segmentation: outputting a cardiac muscle tissue segmentation result with a timestamp; s3, motion track modeling: generating three-dimensional track point cloud data in a pulsation period; s4, feature parameter extraction: performing spatial-temporal feature analysis on the track point cloud data, and extracting multi-dimensional motion parameters; and S5, heterogeneity atlas generation: generating a cardiac pulse heterogeneity atlas according to the multi-dimensional motion parameters. According to the method, automatic analysis of the cardiac pulse track and generation of the heterogeneity atlas based on the multi-modal image and space-time modeling are realized, and the precision and the intelligent level of cardiac motion anomaly recognition are remarkably improved.

The present invention discloses a sequential scanning DECT cardiac imaging method based on AIGC conditional generation, including sequential CT scanning of projection data of different phases and high and low energy levels of the heart, using the projection data to perform FDK reconstruction to obtain image domain data of high and low energy levels, selecting high and low energy level organ images in a relatively static state in the image domain to train a conditional generation network model Energy-GAN, using low-energy image layers of moving organs as input conditions of Energy-GAN to generate high-energy image layers in the same motion state, and finally performing image domain material decomposition on the generated high-energy image and the corresponding low-energy image, and using a pre-trained diffusion model to denoise the material decomposition image. The present invention realizes sequential scanning DECT cardiac imaging, effectively reducing the material decomposition artifacts caused by cardiac motion and the noise generated by image domain material decomposition without performing flexible 3D image registration.

The invention relates to the technical field of medical images, and discloses a coronary artery phase analysis method and device and a storage medium. The heart comprises a plurality of coronary arteries, for at least one coronary artery, first a plurality of phase reconstructed image sequences of the heart are acquired. Then, coronary artery motion data of the coronary artery and heart motion data corresponding to the position of the coronary artery are acquired based on the plurality of phase reconstruction image sequences. Then, based on the heart motion data, motion differences of different coronary artery partitions are analyzed, and a target interval corresponding to a target period of the coronary artery is obtained; and finally, based on the target interval, obtaining a target phase corresponding to the coronary artery on the coronary artery motion data, improving the accuracy of the optimal phase in the heart reconstruction image, reducing motion artifact interference, and generating a clearer coronary artery image.

The invention belongs to the technical field of medical image processing, and discloses a heart motion estimation method based on motion decomposition, and the method comprises the steps: constructing a training set based on a moving image and a fixed image; constructing a heart motion estimation network, wherein the heart motion estimation network comprises a double-branch feature encoder network and a feature hybrid decoder network; constructing a total loss function; training the heart motion estimation network based on the training set; and inputting a pair of a moving image and a fixed image to be predicted into the trained heart motion estimation network to obtain an estimated deformation field. A double-branch coding structure designed by the invention is helpful for a network to learn an anatomical structure in a medical image from a plurality of different scales, a block convolution unit covers a complete image block area through a sufficiently large convolution kernel and optimizes an initial deformation field by using a weight coefficient, and meanwhile, a multi-head mechanism is introduced, so that a multi-head structure is optimized. Decomposition and adaptive learning of deformation are realized, and accuracy and universality of heart motion estimation are improved.

This invention relates to a system for identifying and assessing valvular motion abnormalities in fetal echocardiography, specifically in the field of echocardiography imaging. The system first robustly extracts the valvular central skeleton from a single frame of ultrasound image, overcoming image noise interference. Then, it constructs a physically constrained curve model to simulate the valvular tissue's mechanical properties and precisely tracks its motion trajectory in the image sequence through spatiotemporal optimization. Finally, the system isolates the overall cardiac motion, quantifies the valve's own opening and closing deformation, interleaflet motion synchronicity, and local tissue strain rate, thereby achieving early, objective, and quantitative accurate assessment of fetal valvular motion dysfunction and assisting in clinical diagnosis.

The invention relates to a heart motion estimation method and device based on sparse key point driving and a medium, and the method comprises the steps: respectively inputting a moving image and a fixed image into a key point detection network with time-space consistency, and carrying out the extraction to obtain a key point pair; wherein in the key point detection process, a local correlation graph is generated according to normalized cross-correlation of key points in a fixed image and key points in a moving image, and after the local correlation graph is converted into a probability distribution graph, the positions of the key points in the moving image are corrected; and constructing a sparse basic motion vector according to the relative motion between each pair of key points to obtain a sparse motion vector expressed by heart motion, and performing dense motion reconstruction on the moving image by using a deep neural network according to the sparse motion vector to generate a dense motion field. Compared with the prior art, the method has the advantages of being high in structure retentivity, high in individual generalization and high in heart motion detail modeling capacity.

The present invention discloses a component for in vitro near-physiological simulation of an isolated heart, belonging to the field of medical device technology. Its design purpose is to solve the many problems existing in existing in vitro heart simulation devices, simulate the physiological movement of the heart more accurately and simplify the valve movement observation process. The component includes a transparent shell, a top cover and a reciprocating pump. The shell has a accommodating chamber for placing the isolated heart, and the top cover has inflow and outflow channels respectively connected to the left atrioventricular orifice and the aortic orifice of the heart. An observation window is provided on the top cover to observe the valve movement. The reciprocating pump changes the hydraulic pressure of the accommodating chamber through an isolation membrane to cause the heart to contract and relax. The component is also provided with multiple pressure measuring interfaces and pressure measuring components, and the shell is polyhedron for easy observation. The component can be used to accurately simulate cardiac movement and is suitable for the evaluation of cardiac implantable medical devices, such as the evaluation of parameters such as the function, biomechanical properties and damage mechanism of interventional artificial valves, etc., providing a reliable in vitro simulation environment for the development and verification of medical devices.

A system and method of catheter angiography utilizing a deep neural network-based digital subtraction angiogram (DSA) algorithm for accurately recovering vessel structures and intensity information from cardiac angiography acquisitions including the steps of receiving X-ray coronary angiography (XCA) images from an individual, generating a mask of vessel layers from the XCA images, extracting vessel layers from a background image and a foreground image in which the background image and the foreground image comprise voluntary motion, respiratory motion, cardiac motion, or other artifact, and generating a final angiographic image comprising the extracted vessel layers.

The invention provides a radiotherapy control method and a related device. The radiotherapy control method comprises the following steps: acquiring a heart motion model corresponding to an object to be subjected to radiotherapy; determining a to-be-sketched image from the plurality of medical images, and determining a moment corresponding to the to-be-sketched image as a target treatment moment, so that an operator performs target region sketching on the to-be-sketched image to obtain a corresponding sketched image; based on a heart motion model, determining a target electrocardiogram wave band and a target optical marker ball position corresponding to the sketching image; and performing radiotherapy on the to-be-radiotherapy object based on the target treatment moment, and if the deviation between the electrocardiogram wave band acquired by the electrocardiograph in real time and the target electrocardiogram wave band is smaller than a first preset threshold value and the deviation between the optical marking ball position acquired by the optical positioning system in real time and the target optical marking ball position is smaller than a second preset threshold value, performing radiotherapy on the to-be-radiotherapy object according to the sketched image. Through the method, the accuracy of radiotherapy is ensured.

The present application relates to a kind of cardiac ultrasound image key point constraint calibration method, device / equipment / system, readable storage medium and program product, the method can not only accurately predict key point position, reduce the tracking error caused by occlusion and noise interference, also ensure the continuity and smoothness of key point tracking by automatic calibration mechanism.At the same time, considering the physiological characteristics of cardiac movement, so that the corrected key point position is more in line with the normal systolic and diastolic process of heart, improve the physiological rationality of diagnostic result, reduce the dependence on artificial operation, enhance the objectivity and consistency of diagnosis, provide strong support for the diagnosis and treatment of cardiovascular disease.

Devices, systems and methods for performing an interventional vascular procedure with visual guidance using one or more stereoscopic optical head mounted displays are disclosed. Devices, systems and methods for compensating the display of a stereoscopic optical head mounted display for cardiac motion, vascular movement or pulsation and / or respiratory motion are disclosed.

A visualization system for a medical procedure. The system includes a medical device, a localization system, a signal processing system, and a display. The medical device includes one or more position sensors. The localization system is configured to generate an magnetic field and / or an impedance field to track the one or more position sensors. The signal processing system is configured to receive input location signals from the one or more position sensors. The signal processing system includes low pass filter to attenuate oscillation in the input location signals from cardiac motion and a low pass filter controller to generate a reset output communicated to the low pass filter to reset the low pass filter. The display provides real-time visualization of the medical device.

The application discloses a multi-order time series difference method for heart motion feature learning, and aims at the problem that current heart motion tracking methods mainly require the displacement field of myocardial motion, lack the representation of the velocity field of myocardial motion, the acceleration field after force and other inherent physiological characteristics of myocardial, and proposes a multi-order time series difference method for heart motion feature learning, compared with the existing heart motion feature learning method, the application uses the CineMorph framework combining the UNet network architecture of frame perception and the time continuous Transformer block, captures the periodic motion of the heart, not only effectively extracts the motion field of each time point, but also obtains the high-precision time-continuous Lagrangian motion field of the heart from the original cine magnetic resonance imaging. The advantage of this way is that the dynamic change of the heart in the periodic motion can be accurately captured, so that the motion state of the heart at different time points can be better reflected.

The invention relates to a computer implemented method (10) for storing incoming data (16) provided at an input interface (14) comprising: allocating at least three memory areas (M1,M2,M3,M4,M5) of specified sizes in a storage (12); associating each of the subsequent memory areas with a data aggregation scheme (D2,D3,D4,D5) having an increasing data compression rate; feeding the incoming data (16) to the sequence of memory areas by losslessly storing the incoming data (16) in the first memory area, wherein in case the occupancy of a memory area exceeds a maximum capacity, a portion of the data stored in this memory area is processed using the data aggregation scheme associated with the subsequent memory area and moved to the subsequent memory area or is discarded (22) from the last one. The invention also relates to a wearable device, in particular, a cardiac motion sensor.

A system for producing information indicative of cardiac abnormality, for example aortic stenosis, comprises a signal interface (101) for receiving a signal indicative of cardiac motion and generated by a motion sensor system (103) having a mechanical contact with a chest of an individual The system comprises a processing system (102) configured to extract, from the motion signal, samples each having a temporal length of one or more heart-beat periods so that a temporal phase of heart-beat related features with respect to a beginning of the sample is same within each of the samples, compute an integral transform of each of the samples to map each of the samples to basis-functions of the integral transform, and classify the samples to correspond either a healthy case or a cardiac abnormality based on coefficients resulting in the integral transform of each of the samples.

A monitoring system (MS) comprising: a housing (20); a heart sound sensor (40); an acceleration sensor (30); and a control device (60). The heart sound sensor (40) is attached to the housing (20). The heart sound sensor (40) detects heart sounds, which are sounds generated by cardiac motion of a subject. The acceleration sensor (30) is attached to the housing (20). The acceleration sensor (30) detects acceleration corresponding to body-surface motion caused by cardiac motion. The control device (60) monitors cardiac motion on the basis of information acquired from the heart sound sensor (40) and the acceleration sensor (30).

The application relates to a scan triggering method, device, system and storage medium, wherein the scan triggering method comprises the following steps: acquiring a heart motion signal of a scanning object collected in real time; judging whether the latest signal data in the heart motion signal meets a preset triggering condition; generating a triggering instruction in the case that the latest signal data meets the preset triggering condition; and the triggering instruction is used for triggering a medical imaging device to perform medical scanning on the scanning object. Through the application, the problem that accurate scan triggering cannot be realized is solved, and the heart motion signal collected is used for accurately triggering scanning.

An apparatus is provided with processing circuitry that receives a phase image acquired at a corresponding cardiac phase, determines, from the received phase image, a mask image of a particular cardiac region, applies both the determined mask image and the phase image to inputs of a trained neural network model to obtain, from outputs of the neural network model, a location probability map. The neural network model is trained with a set of input data and a corresponding set of output data. The input data includes a training mask image and a training phase image, and the output data includes a training location probability map. The processing circuitry calculates, for the cardiac phase, from the determined location probability map output from the trained neural network model, a value of a cardiac motion metric. The determined location probability map specifies a probable location of a cardiac vessel.

Disclosed herein is a method for extracting cardiac signal data (428) from a set of sensor data (412) acquired using a plurality of pressure sensors (102) of a two-dimensional sensor array (100). The method comprises receiving (200) the set of sensor data (412), determining (202) a principal respiratory time signal (342; 416) of a respiratory motion pattern for the subject (118), determining (204) sensor-wise individual phase-shifts (418) of individual respiratory time signals, eliminating (208) the determined principal respiratory time signal (342; 416) sensor-wise using the determined individual phase-shifts (418) for the individual sensors (102), determining (10) a principal cardiac time signal (424) of a cardiac motion pattern for the subject (118), and outputting (216) cardiac signal data (428) comprising the principal cardiac time signal (424).

Embodiments of the present application disclose a continuous human body identity recognition method, device and system based on UWB radar detection of heart micro-movement and a medium. The method comprises: generating a heart movement signal of a measured target according to an echo signal of an ultra-wideband (UWB) radar; analyzing the heart movement signal through double wavelet joint positioning to obtain a heartbeat signal of the measured target; extracting feature data from the heartbeat signal according to a two-dimensional principal component analysis (2D-PCA); and identifying the identity of the measured target through a classification strategy.

The application provides a non-contact arrhythmia detection method, system and storage medium, and the method comprises the following steps: acquiring a patient heart micro-motion signal detected by a radar system, and based on the time domain phase feature, symbolic dynamics feature and motion vector feature of the heart micro-motion signal; obtaining the input of a pre-trained disease diagnosis model based on the time domain phase feature, symbolic dynamics feature and motion vector feature, inputting the input into the disease diagnosis model, and outputting the arrhythmia detection result of the patient. The application can not only capture high-frequency heart motion features, but also has high detection accuracy and user comfort by using a non-contact detection method.

The inertial sensor of the sensor unit is placed at an appropriate position on the subject. [Solution] The detection device 10 of the sensor unit includes a housing 20, a heart sound sensor 40, and an acceleration sensor 30. The heart sound sensor 40 is attached to the housing 20. The heart sound sensor 40 detects heart sounds, which are sounds produced by the movement of the subject's heart. The acceleration sensor 30 is attached to the housing 20. The acceleration sensor 30 detects acceleration, which is movement of the body surface caused by the movement of the heart.

The invention relates to a heart motion estimation method and device based on motion smoothness retention loss and a medium. The method comprises the following steps: inputting a moving image and a fixed image into a key point detection network to extract key point pairs; constructing a sparse basic motion vector according to the relative motion between each pair of key points to obtain a sparse motion vector expressed by heart motion, carrying out three-dimensional repeated broadcasting to generate a dense basic motion field, carrying out spatial distortion transformation on a moving image, generating a coarse registration image set, and carrying out coarse registration; inputting into a dense motion estimation network to predict to obtain a weight mask corresponding to each dense basic motion field, and weighting and combining to obtain a dense motion field; wherein the motion smoothness retention loss based on a key point sparse motion mask is introduced to carry out network training, and the smoothness of the dense motion field is adaptively adjusted according to a weight mask representing the motion intensity difference of different regions. Compared with the prior art, the method has the advantages of being high in structure retentivity, high in individual generalization and high in heart motion detail modeling capacity.

The invention belongs to the technical field of intelligent medical treatment, and particularly relates to an electrocardiosignal positioning method, a diagnosis device and a heart sound and electrocardio detection system. The electrocardiosignal positioning method comprises the following steps: acquiring a plurality of original lead electrocardiosignals; screening out N first lead electrocardiosignals from the plurality of original lead electrocardiosignals, wherein N is a positive integer; determining an R wave peak point position of the N first lead electrocardiosignals in combination with the N first lead electrocardiosignals; n electrocardiosignal data segments with a first preset time length are intercepted from the N first lead electrocardiosignals on the basis of the determined R wave peak value point position, the first preset time length is larger than T and smaller than zeta T, and T represents the duration of a heart movement cycle; and inputting the N electrocardiosignal data segments to a pre-trained positioning model to obtain a plurality of waveform positioning point positions of the N electrocardiosignal data segments. According to the invention, high-precision waveform positioning can be rapidly carried out, and an accurate waveform positioning point position is obtained.

The invention relates to a computer implemented method for operating a wearable cardiac motion monitoring device (10) comprising at least one motion sensor, the method comprising: acquiring motion data (12) from the at least one motion sensor in a standard mode (14); screening (32) the acquired motion data (12) for one or more predefined motion patterns (22, 24, 26) associated with a high accuracy mode (16, data (12), temporarily switching (28) to the high accuracy mode. The invention also relates to a corresponding wearable cardiac motion monitoring device.