18 results about "Heart motion" patented technology

The invention provides a four-dimensional cardiac function imaging method and device based on a biological magnetic signal. The method comprises the following steps: acquiring a magnetocardiogram signal of a to-be-detected object and dynamic structure information of a target trunk part in a full cardiac cycle; wherein the target trunk part at least comprises double lungs, a trunk, a heart cavity of the trunk and a pericardium structure, and the dynamic structure information comprises a contour structure and a multi-frame dynamic image; performing spatial registration on the dynamic structure information and the sensor for acquiring the magnetocardiogram signal to obtain coordinates of the sensor in a coordinate system of the dynamic structure information; according to the dynamic structure information, the coordinate of the sensor and the magnetocardiogram signal, calculating and reconstructing cardiac electrical excitation activity to obtain a calculation and reconstruction result; and converting the calculation reconstruction result into a four-dimensional visual image. According to the method, heart magnetic imaging and heart movement are combined, on the basis of obtained dynamic structure information and heart magnetic signals, the characteristics of biological magnetic signals are fully utilized, and the signal processing technology is integrated through space registration, so that accurate reconstruction of four-dimensional representation of the heart and visual display of functional activities are realized; therefore, the obtained four-dimensional visual image can completely represent the dynamic electrophysiological activities of the whole heart (including epicardium, endocardium and intrawall tissues) in a cardiac cycle, and a technical scheme with different concepts is provided for non-invasive imaging of cardiac electrical excitation activities.

Methods and systems are provided for authenticating an individual using a motion of a physiological structure. For example, an individual may be authenticated using their cardiac motion. A first radiofrequency (“RF”) signal is transmitted towards the physiological structure of the individual. A first RF return signal is received, where the first RF return signal corresponds to the transmitted first RF signal. The first RF signal and first RF return signal are processed to obtain a motion signal. One or more values are determined for each fiducial point of a set of pre-determined fiducial points in the motion signal. The set of pre-determined fiducial points corresponds to physical movements of the physiological structure. The individual is authenticated based on the values of one or more fiducial points.

This invention discloses an automatic cardiac ultrasound recording system and method with anti-distortion capabilities. The method includes: acquiring synchronized cardiac ultrasound image frames and probe motion state data; extracting explicit anatomical quality features and implicit spatiotemporal fusion features; generating predicted spatiotemporal features based on historical implicit features; obtaining a section quality deviation index by combining acoustic coupling quality indices; dynamically determining stable and distortion boundaries based on the deviation index within a sliding window; classifying the current frame as stable, transitional, or distorted; recording when the frame is stable and the phase meets the conditions; recording when the frame is transitional and the predicted and current features are adaptively fused and compensated based on observational uncertainty; and pausing recording and providing attribution prompts when the frame is distorted. This invention achieves precise decoupling of probe motion and cardiac motion through IMU and image fusion, and employs a dynamic threshold and residual calibration mechanism to effectively improve the continuity and diagnostic quality of automatic recording, making it suitable for intelligent control of cardiac ultrasound examinations.

An apparatus and method to mitigate cardiac motion and respiratory motion on geometric measurements of cardiac electrodes. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor configured to receive the at least a position signal comprising a first, second, and third spatial coordinate, and compensate, using a cardiac compensator and respiratory compensator, for respiratory motion, wherein compensating for the respiratory motion comprises calculating at least a velocity signal, using the at least a position signal, extracting, using the cardiac compensator, cardiac motion from the at least a position signal as a function of the at least a position signal and the at least a velocity signal, determining, using the respiratory compensator, a respiratory phase, and extracting the respiratory motion as a function of the respiratory phase.

This invention discloses a method and system for effectively reducing X-ray radiation dose during electrocardiogram (ECG) scanning, relating to the field of artificial intelligence technology. The method includes: acquiring real-time ECG signals of the subject, identifying the R-wave and its occurrence time, and calculating the RR interval between adjacent R-waves; dividing the subject's cardiac cycle based on the RR interval and the R-wave occurrence time, and determining the intense cardiac motion phase and diastolic phase of each cardiac cycle; during the intense cardiac motion phase, controlling the X-ray tube mA value to decrease to a dose reduction mA value below the source mA value; when the current time enters the diastolic phase, controlling the X-ray tube mA value to recover from the dose reduction mA value back to the source mA value; after determining that the X-ray tube mA value has recovered and stabilized at the source mA value, a trigger signal is activated, and the acquisition of projection data is initiated based on the trigger signal. This method helps solve the problem that existing technologies cannot effectively reduce X-ray radiation dose during ECG scanning.

The invention discloses an ultrasonic myocardial movement tracking method based on a data-enhanced end-to-end multi-frame optical flow estimation network. The method comprises seven steps to obtain a forward optical flow of a to-be-estimated multi-echocardiogram. According to the ultrasonic myocardial movement tracking method based on the data-enhanced end-to-end multi-frame optical flow estimation network, segmentation dependence is abandoned, and a data enhancement strategy aiming at ultrasonic image characteristics is introduced, so that the network is automatically focused on a myocardial region in a training process, interference of non-myocardial tissues is effectively inhibited, and end-to-end tracking without segmentation is realized. Meanwhile, on the basis of a network structure of multi-frame optical flow estimation, a motion feature transmission mechanism is introduced, multi-frame motion information can be fused, and cross-period iteration correction can be performed by utilizing the periodicity characteristic of heart motion, so that the limitation of short-time inter-frame tracking is broken through, and the accuracy of heart motion is improved in a complex acoustic environment. For example, stable and accurate myocardial motion estimation can still be obtained under the conditions of low signal-to-noise ratio and fuzzy boundary.

Various methods and systems for x-ray imaging are provided. In one embodiment, a method includes: acquiring a plurality of fluoroscopic images depicting an interventional tool positioned relative to an anatomical structure of interest of a patient; segmenting the interventional tool in the plurality of fluoroscopic images; measuring motion of the patient in the plurality of fluoroscopic images; correcting the plurality of fluoroscopic images to remove the motion of the patient; registering the segmented interventional tool to the anatomical structure of interest in the corrected plurality of fluoroscopic images; and displaying an image with the segmented interventional tool registered to the anatomical structure of interest. In this way, a physician can view the position and movement of an interventional tool located within a patient's body relative to a static image of the anatomical structure without motion artifacts or errors caused by patient motion such as respiratory motion or cardiac motion.

The invention discloses a heart CT imaging method and device, electronic equipment and a storage medium. The heart CT imaging method comprises the steps of obtaining a plurality of reconstructed images corresponding to a plurality of periods based on original heart scanning data; determining an initial motion field of scanning angles corresponding to the plurality of reconstructed images based on a motion field estimation model; fitting the initial motion field to obtain a target motion field of all scanning angles; and performing motion compensation on the heart images corresponding to the plurality of scanning angles based on the target motion field to obtain a plurality of compensation images, and fusing the plurality of compensation images to obtain a target image. According to the heart CT imaging method, the deep learning network is combined with the multi-phase image to estimate the operation field, the heart motion field can be estimated more accurately, and the imaging effect is better.

Described are computer-implemented methods for determining a cardiac abnormality of a subject. The computer-implemented methods may include obtaining a signal related to a cardiac motion using a non-invasive contact sensor and wherein the signal includes one or more peak measurements of cardiac motion and one or more non-peak measurements of cardiac motion. The computer-implemented methods may include determining one or more peak values relating to the one or more peak measurements and one or more non-peak values relating to the one or more non-peak measurements. The computer-implemented methods may include generating an indication of the cardiac abnormality based at least in part on the one or more peak values and the one or more non-peak values.

The invention discloses an echocardiography parameter curve reconstruction smoothing method based on ECG data. The echocardiography parameter curve reconstruction smoothing method comprises the following steps that S1, the ECG data and ultrasonic data which are synchronous in clock are collected; s2, constructing physiological priori; s3, performing time phase segmentation on the ECG data, and performing time phase constraint reconstruction on the ultrasonic curve; and S4, reconstructing a smooth curve, extracting features, and establishing an output interface. The method has the beneficial effects that the feature points of the ECG curve are extracted, and the ultrasonic curve is positioned in an auxiliary manner, so that the ultrasonic continuous parameters are smoothly reconstructed, and the parameter curve of the real continuous motion trend of the heart can be accurately reflected.

Described are computer-implemented methods for determining a cardiac abnormality of an individual. The computer-implemented methods may include obtaining a signal indicative of cardiac motion and generated by a sensor. The computer-implemented methods may include identifying one or more features of the signal over a temporal length of the signal. The computer-implemented methods may include extracting one or more sample signal portions of the signal based at least in part on the one or more features. The computer-implemented methods may include applying an integral transform to the one or more sample signal portions. The computer-implemented methods may include determining a classification of a sample signal portion of the one or more sample signal portions based at least in part on a coefficient of the integral transform applied to the sample signal portion of the one or more sample signal portions.

A system (100) for generating a three-dimensional image of a coronary tree (449) includes a memory (151) and a processor (152). The processor (152) is configured to to: obtain a sequence of two-dimensional angiogram images (410) corresponding to a moving heart from a single viewpoint of an imaging device; and generate, using a trained machine learning model (430), a three-dimensional representation (211A) of the coronary tree (449) based on the sequence of the two-dimensional angiogram images (410) and cardiac motion of the moving heart.

The invention provides a method and a device for identifying a diastolic period and a systolic period of a CBCT image, and the method comprises the steps: scanning a heart region through CBCT equipment, and collecting a plurality of two-dimensional projection images of the heart region; preprocessing the plurality of two-dimensional projection images so as to suppress scanning errors of CBCT equipment and enhance the distinction degree of heart motion signals and non-motion signals in the CBCT images; based on a PCA algorithm, extracting a feature value of each preprocessed two-dimensional projection image, the feature values being used for representing a heart motion state; fitting the characteristic values of all the two-dimensional projection images into a characteristic curve according to an acquisition time sequence; and identifying a peak value and a valley value of the characteristic curve, and determining that the two-dimensional projection image corresponding to the peak value is a diastolic period projection image and the two-dimensional projection image corresponding to the valley value is a systolic period projection image. According to the application, accurate and efficient identification of the key cardiac stage of the heart can be realized, and stable and reliable image support is provided for subsequent clinical operation.

The invention provides a non-contact heart mechanical activity event real-time monitoring method and device. The method comprises the steps that a thoracic cavity micro-motion signal and a periodic rhythm signal are extracted from millimeter wave echo signals, collected in real time in a non-contact mode, of a target subject; and respectively inputting the chest micro-motion signals and the periodic rhythm signals into a heart event identification model, so that the model respectively extracts feature data corresponding to the chest micro-motion signals and the periodic rhythm signals and splices the feature data to obtain heart joint feature data, and decoding and multi-label classification processing are sequentially carried out on the heart joint feature data to output heart mechanical activity event identification result data. The result data comprises heart mechanical activity event type labels and time sequence positioning data. According to the invention, while the heart mechanical activity event identification efficiency is ensured, the identification accuracy of the transient event can be effectively improved and event type identification can be realized, so that the accuracy and robustness of real-time monitoring of the non-contact heart mechanical activity event can be effectively improved.

This invention belongs to the field of identity recognition technology and discloses a non-contact identity recognition method, system, medium, device, and terminal. It extracts, demodulates, and differentiates the phase of vital movement signals of a target individual collected by an AWR1642 sensor. Sine fitting is used to denoise the differentiated phase signal to obtain cardiac motion information. Adaptive weight adjustment and local perturbation are used to improve the Wild Dog algorithm's support vector machine optimization, achieving identity recognition. A Butterworth low-pass filter separates respiratory and heartbeat signals, and the obtained heartbeat frequency is compared with that of a smart bracelet to verify the feasibility of using FMCW millimeter-wave radar for vital signal detection. Experimental results show that the heartbeat frequency obtained by this invention has a Pearson correlation coefficient of 0.96 with that obtained by contact methods, indicating the feasibility of FMCW radar collecting vital signals. Furthermore, the accuracy of using the improved Wild Dog optimization algorithm to optimize the SVM for real individual identity recognition can reach 88%, demonstrating that the identity recognition method of this invention can effectively improve the accuracy of recognition.

The application provides a method and device for identifying diastolic and systolic phases of a CBCT image. The method comprises: scanning a heart region by a CBCT device to collect a plurality of two-dimensional projection images of the heart region; preprocessing the plurality of two-dimensional projection images to suppress scanning errors of the CBCT device and enhance the distinguishability of the heart motion signals and non-motion signals in the CBCT images; extracting eigenvalues of each of the preprocessed two-dimensional projection images based on a PCA algorithm, wherein the eigenvalues are used to represent the heart motion state; fitting the eigenvalues of all the two-dimensional projection images into a characteristic curve in the order of acquisition time; identifying the peak and valley of the characteristic curve, determining the two-dimensional projection image corresponding to the peak as a diastolic phase projection image, and determining the two-dimensional projection image corresponding to the valley as a systolic phase projection image. The application can accurately and efficiently identify the key heart phases, and provide stable and reliable image support for subsequent clinical operations.

A system for generating information indicative of a heart abnormality (e.g., aortic valve stenosis) includes a signal interface (101) for receiving a signal indicative of heart motion and generated by a motion sensor system (103) having mechanical contact with a chest of an individual. The system includes a processing system (102) configured to extract samples from the motion signal each having a time length of one or more heartbeat cycles such that a time phase of a heartbeat-related feature is the same within each sample relative to a start of the sample, an integral transform for each sample is calculated to map each sample to a basis function of the integral transform, and the samples are classified to correspond to a health case or a cardiac anomaly based on coefficients resulting in the integral transform for each sample.

The invention provides an individualized cardiac function prediction system, and relates to the technical field of data prediction, a marker matching module is used for analyzing in-vitro index data of a prediction target to obtain a plurality of marker parameters matched with the cardiac function of the prediction target in the in-vitro index data; the feature processing module is used for performing weighted fusion on the marker feature vectors to obtain organ-heart influence vectors; the feature fusion module is used for fusing the organ-heart influence vector and the heart monitoring data of the prediction target to obtain a fusion data feature; the coupling prediction model construction module is used for constructing a cardiac mechanology sub-model and a cardiac electrophysiology sub-model of a prediction target, and generating a cardiac motion model according to the cardiac mechanology sub-model and the cardiac electrophysiology sub-model; and the dynamic prediction module is used for simulating the heart motion of the prediction target through the heart motion model to obtain the heart function of the prediction target in the preset future time period. According to the invention, the prediction precision of the heart function is improved.