58 results about "Cardiac zone" patented technology

A system and method for selecting, from a series of cardiac implants having different sizes, the cardiac implant having optimum size for implantation in a patient. The method includes obtaining data representative of a patient-specific cardiac region and predicting the optimum size of the cardiac implant best matching a predefined criterion when deployed in the cardiac region. The predicting includes querying a database; determining parameter values for a parametric model representation of the patient-specific cardiac region; and / or entering the data representative of the patient-specific cardiac region into an artificial intelligence device.

According to an embodiment of the present disclosure, disclosed is a method to read a chest image. The method includes: determining whether or not to identify presence of cardiomegaly for a chest image; detecting a lung region and a heart region respectively which are included in the chest image, by using a neural network model, when it is determined to identify presence of cardiomegaly of the chest image; and calculating a cardiothoracic ratio of the chest image using the detected lung region and the detected heart region.

The invention provides an atrial defect and ventricular defect image model reconstruction method, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring two-dimensional medical image data of a heart area; based on the two-dimensional medical image data, utilizing an image segmentation technology to identify atrial defect and ventricular defect areas and extracting feature points; constructing a preliminary three-dimensional model of the atrial defect and the ventricular defect according to the feature points by using a three-dimensional reconstruction algorithm; and the preliminary three-dimensional model is refined through a model optimization technology, so that the accuracy and definition of the preliminary three-dimensional model are improved. By means of the scheme, the accuracy and definition of the three-dimensional model can be improved in atrial defect and ventricular defect image model reconstruction.

The present disclosure provides a method for providing information on strain quantification implemented by a processor, the method includes receiving a cardiac ultrasound image including a target heart area of an subject, determining a motion vector field for the target heart area in the received cardiac ultrasound image using a prediction model trained to segment the target heart area using the cardiac ultrasound image as an input and determine a motion vector field based on the segmented target heart area, and determining a strain quantification parameter based on the motion vector field, and the present disclosure provides a device and system using the method for providing information on strain quantification.

The invention discloses a magnetic positioning type balloon and CT fusion navigation system for a pericardial interventional operation, and belongs to the technical field of medical surgical navigation and minimally invasive interventional instruments. The problems that in the prior art, the visual field of an endoscope is not clear due to pericardial fluid, and a preoperative CT three-dimensional model and an intraoperative magnetic positioning space are difficult to rapidly and accurately register are solved. According to the balloon type pericardium endoscope, the liquid is injected into the balloon to drain pericardium liquid, and a clear pericardium wall tissue image is collected; the position and the posture of an image sensor in the endoscope in a magnetic positioning space are determined; setting an in-vitro mark point on the body of the target person; constructing a three-dimensional model of the heart and pericardium region by collecting CT data of the target person containing the in-vitro mark points; calculating a registration transformation matrix between the two spaces; mapping the real-time position and posture of the balloon type pericardium endoscope into the three-dimensional model of the heart area; and generating and outputting a navigation image fusing the three-dimensional model of the heart area and the real-time position of the endoscope. The invention is suitable for the technical field of medical machinery.

The invention provides a heart function auxiliary analysis system based on a neural network, and belongs to the technical field of heart assistance, and the system comprises a preprocessing module which is used for preprocessing a heart ultrasound image; the waveform recognition module is used for performing waveform detection and multi-scale feature recognition on a waveform curve in the preprocessed ultrasonic image, positioning a peak value and a valley value of a waveform, and recognizing a diastolic frame and a systolic frame in a cardiac ultrasonic period; the three-dimensional reconstruction module is used for performing three-dimensional reconstruction on the heart region by using a deep learning technology; the calculation module is used for calculating the volume and ejection fraction of the left ventricle according to the reconstructed heart area; and the auxiliary reminding module is used for carrying out abnormal value verification and elimination on the ejection fractions obtained in the plurality of periods through an abnormal value elimination algorithm to obtain an accurate value, and outputting the cardiac function information of the patient for auxiliary reminding. The cardiac function condition of a patient can be accurately reflected, an accurate basis is provided for auxiliary reminding, and effective support is provided for clinical diagnosis.

The invention relates to a heart analysis method and device for a medical image, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring a heart three-dimensional mask image; the heart three-dimensional mask image represents a heart area in the medical scanning image; principal component analysis is carried out on the heart three-dimensional mask image, and the main axis direction of the heart area is determined; adjusting the medical scanning image according to the main axis direction to obtain an adjusted medical scanning image; the adjusted medical scanning image is used for analyzing the heart area. By adopting the method, the heart analysis can be more accurately carried out in the medical image.

A deep learning model for the detection of obstructive coronary artery disease (CAD) can take a set of polar maps and patient information as input, then output obstructive CAD scoring data, such as probabilities of obstructive CAD associated with various cardiac territories, as well as an attention map and a CAD scoring map. The model can operate agnostic of camera type used to capture the set of polar maps. The attention map indicates regions of the polar maps important to the deep learning process for that particular set of polar maps. The attention map and obstructive CAD scoring data can be used to generate a CAD scoring map showing CAD probability by segment on a standard 17-segment model of a left ventricle. The attention map and / or CAD scoring map can act as easily explainable tools for interpreting the results of a myocardial perfusion imaging study.

The invention relates to determining a model of a cardiac implant deployed in a cardiac region of a patient. A database including a plurality of different records is provided, each record including at least data representative of an image of a cardiac region and an associated model of the cardiac implant deployed in said cardiac region. A patient-specific image of the cardiac region for which the model of the cardiac implant deployed in said cardiac region is to be determined, is received. An approximation of the patient-specific image of the cardiac region is calculated based on a weighted combination of images of the cardiac region in a plurality of records. The model of the cardiac implant deployed in the cardiac region associated to the patient-specific image of said cardiac region is determined based on weighted combination of models of the cardiac implant associated to the images of the cardiac region in the plurality of records used for approximating said patient-specific image of the cardiac region.

A chest pad that significantly reduces the occurrence of commotio cordis among athletes is disclosed. The chest pad may be utilized as a stand-alone chest protector intended to provide coverage primarily for the upper chest wall and the cardiac area. The chest pad may also be implanted into or utilized in traditional chest protectors that provide full coverage of the user's torso. The chest pad provides additional protection to the heart of the wearer such that impact in the heart area with a projectile is absorbed by the chest pad.

This invention discloses a low-pass filtered non-breath-hold saline angiography combined with cardiac mirror contrast imaging for lung perfusion imaging, belonging to the field of medical imaging technology. The method includes the following steps: S1: determining whether the subject's tidal impedance variation meets the imaging criteria; S2: injecting contrast agent into the subject to perform angiography; S3: obtaining an impedance decrease signal; S4: combining cardiac mirror contrast imaging with lung perfusion images. This invention solves the problem of low success rate of saline angiography in existing technologies, achieves separation of thoracic impedance caused by tidal breathing and thoracic impedance caused by saline angiography, reduces the influence of the cardiac region on lung perfusion images, further improves the accuracy of lung perfusion imaging, greatly reduces the interference of breath-holding on respiratory circulation, improves the simplicity of clinical application, and expands clinical application scenarios.

The present invention discloses a cardiac region localization method based on electrical impedance tomography technology, belonging to the field of electrical impedance tomography technology, which comprises the following steps: collecting EIT data, and dividing into a localization group and a control group; reconstructing the EIT data to obtain an EIT time series signal map; processing each pixel in the EIT time series signal map to obtain a time series signal intensity map of each pixel; performing standard deviation normalization calculation on the time series signal intensity of each pixel to obtain a time series signal intensity distribution map, selecting the pixel point where the highest intensity value is located as the central position of the cardiac region of interest, and calculating the phase difference between each pixel position and the central position; obtaining the cardiac region localization results of the localization group and the control group according to the phase difference and the time series signal intensity map; comparing the results of the localization group and the control group, and determining the final cardiac localization region after verification, which no longer relies on simple signal intensity threshold division, can expand the clinical application scenarios of EIT, and further enhances the non-invasive superiority of EIT technology.

A device for processing cardiac signals, comprises: - a data storage (114) arranged to receive input data sets each comprising a plurality of P wave segments each associated with an electrocardiogram track and with an acquisition time window, and a plurality of coronary sinus signals associated with the same acquisition time window and having one or more activation sequence(s), - a random convolutional kernel-based extractor arranged, for a given input data set, to determine an electrocardiogram feature vector, and - a machine-learning based locator using decision trees trained on data comprising sets of electrocardiogram feature vectors labelled with a value indicating a cardiac region identifier, and arranged to receive an electrocardiogram feature vector from the random convolutional kernel-based extractor as input, and to return a cardiac region identifier as output.

The invention discloses an intelligent electrocardio garment based on the flexible sensing technology and an electrocardio monitoring method.The intelligent electrocardio garment comprises a garment body, a flexible sensing unit, an edge calculation processing unit, a wireless communication unit and a power management unit, and the flexible sensing unit is arranged at the position, corresponding to a heart area, of the inner layer of the garment body; electrocardiosignals are collected by tightly attaching the flexible electrode to the human skin, the edge calculation processing unit conducts preprocessing and feature analysis on the electrocardiosignals, and the wireless communication unit transmits data to an external terminal. It can be seen that through the integrated design of the flexible sensing unit and the clothes, high-precision collection of electrocardiosignals is achieved, and the problem that traditional equipment is poor in fitting degree is solved; the edge calculation processing unit locally completes data processing and anomaly recognition, real-time early warning is achieved, and the problem of data processing lag is solved; the clothes body is made of breathable elastic fabric, and long-time wearing comfort is guaranteed.

The present invention provides a method, apparatus and electronic device for assisting in predicting latent atrial fibrillation, which are applied to the technical field of electromagnetic induction. The method includes: acquiring an initial electromagnetic echo signal; performing signal separation processing on the initial electromagnetic echo signal to obtain a target electromagnetic echo signal corresponding to the motion state of the heart region of the object to be measured within a predetermined time period; using the spatio-temporal encoder of the information detection model to perform first feature extraction processing on the target electromagnetic echo signal to generate a plurality of mechanical motion spatio-temporal features related to normal heartbeats and abnormal heartbeats in the heart region; using the feature extraction encoder of the information detection model to perform second feature extraction processing on the plurality of mechanical motion spatio-temporal features to generate a target mechanical motion time feature related to abnormal heartbeats in the heart region; and using the detection and evaluation sub-model of the information detection model to perform feature conversion mapping processing on the target mechanical motion time feature to obtain the target predicted heartbeat information of the object to be measured.

A three-dimensional (3D) image of a cardiac region is input to a convolutional neural network (CNN) to semantically segment the 3D image. An output of the CNN includes a 3D geometry of a heart valve structure within the cardiac region. A chain code is applied to the 3D geometry to obtain two-dimensional (2D) geometric parameters of the heart valve structure. The 2D geometric parameters are used to recommend or select an implantable device that fits with the heart valve structure.

A method of pacing a heart includes implanting a subcutaneously implantable device on a xiphoid process and / or a sternum of a patient. The subcutaneously implantable device includes a housing, a prong extending away from the housing, and a first electrode at a distal end of the prong. The method further includes contacting a Manicka Zone of Pacing on an anterior surface of the heart with the first electrode on the distal end of the prong. The Manicka Zone of Pacing is defined as an area of the heart extending 3 centimeters (1.1811 inches) laterally outward from either side of a left anterior descending artery of the heart. A pacing signal is delivered to the Manicka Zone of Pacing of the heart through the first electrode. A right ventricle and a left ventricle of the heart are synchronously paced with the pacing signal received in the Manicka Zone of Pacing.

The present invention provides a method, device and electronic device for generating a cardiac video based on electromagnetic signals, which can be applied to the field of electromagnetic induction technology. The method for generating a cardiac video based on electromagnetic signals includes: determining a target electromagnetic echo signal corresponding to the cardiac region of a to-be-detected object, wherein the target electromagnetic echo signal includes the heartbeat motion characteristics after eliminating the respiratory motion characteristics; performing feature mapping processing on the target electromagnetic echo signal by using an encoder of an electromechanical modal conversion model to obtain the target mechanical motion state characteristics of the cardiac region, wherein the target mechanical motion state characteristics correspond to the characteristics of the electrocardiogram motion state of the to-be-detected object; and using an ultrasonic cardiac video sub-model of the electromechanical modal conversion model to process the target mechanical motion state characteristics and a conditional image to generate a target ultrasonic cardiac video of the to-be-detected object, wherein the conditional image represents an ultrasonic cardiac template image.

An implantable medical device for stimulating a heart, comprising a control unit, a memory unit, a stimulation unit for stimulating a cardiac region of a heart, and a detection unit for detecting an electrical signal of the heart. The memory unit comprises a computer-readable program that causes the control unit to perform the following steps: a) detecting capture thresholds during an observation period, each capture threshold detected in response to a sequence of pacing pulses delivered by the stimulation unit; b) storing the detected capture thresholds in the memory unit; c) determining threshold-to-threshold differences between two consecutive capture thresholds; and d) if a maximum determined threshold-to-threshold difference within the observation period is equal to or greater than a predetermined limit, adjusting a pacing output of the stimulation unit based on the maximum capture threshold determined within a first time period which is equal to or shorter than the observation period.

The present invention provides a neural network-based cardiac function auxiliary analysis system, belonging to the field of cardiac auxiliary technology, comprising: a preprocessing module for preprocessing cardiac ultrasound images; a waveform recognition module for performing waveform detection and multi-scale feature recognition on the waveform curve in the preprocessed ultrasound image, locating the peaks and valleys of the waveform, and identifying the diastolic and systolic frames in the cardiac ultrasound cycle; a three-dimensional reconstruction module for three-dimensionally reconstructing the cardiac region using deep learning technology; a calculation module for calculating the volume and ejection fraction of the left ventricle based on the reconstructed cardiac region; and an auxiliary reminder module for verifying and eliminating outliers in the ejection fraction obtained over multiple cycles using an outlier rejection algorithm to obtain an accurate value, and outputting the patient's cardiac function information for auxiliary reminders. This system can accurately reflect the patient's cardiac function status, provide a precise basis for auxiliary reminders, and provide effective support for clinical diagnosis.

The invention discloses a wearable monitoring system for real-time acquisition of respiration, electrocardio and myoelectricity, and relates to the technical field of medical equipment, the wearable monitoring system comprises a monitoring terminal, a data processing center and an equipment management platform, the monitoring terminal comprises a respiration monitoring module, an electrocardio monitoring module, a myoelectricity monitoring module and a blood oxygen and heart rate monitoring module; the monitoring terminal is composed of a flexible sensing jumpsuit and adopts an ergonomic wearable design; the flexible sensing jumpsuit is made of flexible conductive fiber fabric and has the characteristic of variable tensile resistance, conductive fibers deform along with expansion and contraction of the thorax in the breathing process, the resistance value changes accordingly, and the breathing volume and the myoelectricity change are accurately measured through a breathing monitoring module; the electrocardio monitoring module integrates a textile electrode at the position, corresponding to the heart area, of the inner side of the jumpsuit. Synchronous high-quality collection of three important physiological signals of respiration, electrocardio and myoelectricity can be achieved, and electrocardio monitoring meets the national detection standard.

The present invention discloses an automatic segmentation method for cardiac medical images based on lossless processing, belonging to the field of medical image analysis. The key points of the technical solution of the present invention include: enlarging the resolution of the cardiac medical image to be segmented by filling pixels around the image, inputting it into a rough segmentation network to obtain a rough segmentation result and removing the filling, finding the coordinates of the center point of the heart in the rough segmentation result, making a fixed-size crop on the cardiac medical image to be segmented with the coordinates of the center point of the heart as the center and recording the crop position, inputting the cropped cardiac region medical image into a network with consistent resolution to obtain a cardiac region segmentation result, and filling the result into the same crop position of the initialized segmentation result to obtain the final segmentation result. The present invention can avoid the problems of pixel information loss in the pooling operation in the convolutional neural network and damage to medical image data in the resizing operation, improve the accuracy of the cardiac medical image segmentation task, and facilitate the diagnosis and treatment process of cardiac diseases by doctors.

Systems (104) and methods (400) for predicting a location of a target cardiac region for cardiac ablation may include one or more processors that generate (402) a 3D model of a patient's heart based on a medical image of the patient, and estimating (404) one or more mechanical properties that drive movement of the heart of the patient using the 3D model of the heart and the electrophysiological data of the patient. The one or more processors may generate (406) a simulated motion pattern of the patient's heart over at least a portion of the cardiac cycle using the 3D model and the one or more mechanical properties, and identify (408) a region of interest (ROI) of the patient's heart to be radiated. The one or more processors may determine (410) a location of the ROI at a predefined instance of time within the cardiac cycle using a simulated motion pattern of the patient's heart.

A computer-implemented method for autonomous segmentation of contrast-filled coronary artery vessels includes receiving a CT scan volume representing a 3D volume of a region of anatomy that includes a pericardium; preprocessing the CT scan volume to output a preprocessed scan volume; dividing the CT scan volume into a first set of subvolumes; extracting a region of interest by autonomous segmentation of the heart region as outlined by the pericardium, by means of a neural network trained on 3D subvolumes and combining the results of the individual subvolume predictions for the first set to output a mask denoting a heart region as delineated by the pericardium; combining the preprocessed scan volume with the mask to obtain a masked volume; converting the masked volume to a second set of 3D subvolumes; and performing autonomous coronary vessel segmentation to output a mask denoting the coronary vessels.

The application relates to an inhaled cold air treatment instrument and a temperature control method, and belongs to the medical instrument field. The application comprises a case, a flow regulating valve, an air inlet pipe, a heat exchange unit, a temperature sensor, a control unit, a heat preservation pipeline, a patient interface, a display control panel and a gas bottle. The heat exchange unit and the control unit are arranged in the case. The front panel of the case is provided with the display control panel. The heat exchange unit is provided with an air inlet and an air outlet. One end of the air inlet pipe is connected with the gas bottle, and the other end is connected with the air inlet through the flow regulating valve. The patient interface is connected with the air outlet through the heat preservation pipeline. The temperature sensor is arranged on the heat preservation pipeline. The control unit is connected with the flow regulating valve, the heat exchange unit, the temperature sensor and the display control panel. The application solves the problems of slow effect, large side effect and poor patient compliance of the existing systemic or invasive hypothermia treatment method, and lacks an effective means for targeted hypothermia protection of the heart region through the respiratory tract.

A cardiac lead system is provided. The lead is placed epicardially through the transverse pericardial sinus with integrated curvatures to prevent the lead from slipping out of the transverse pericardial sinus. Interaction with multiple chambers of the heart is facilitated in a single lead, without anchors that embed into the heart wall. Multiple electrodes can be grouped over each targeted heart area to ensure adequate electrical contact.

This application provides a method, apparatus, and storage medium for calculating coronary artery calcium scores, comprising: segmenting an initial three-dimensional medical image of the cardiac region from a plain coronary CT image using an image segmentation model; identifying calcified plaque regions in the initial three-dimensional medical image to determine at least one target region image and the location information of each image; identifying the coronary vessel category in each image by recognizing the input consisting of the target region image and the image location information; performing overall recognition of the initial three-dimensional medical image using a second coronary classification model to determine the calcified plaque regions included in the initial three-dimensional medical image, the location information of each region, and the category of coronary vessels; and calculating the calcium score for the calcified regions identified by both models to obtain the total coronary artery calcium score. Thus, the method provided in this application can quickly and accurately calculate the coronary artery calcium score based on plain CT scans.

An implantable medical device for stimulating a heart, includes a stimulation electrode configured to stimulate a first cardiac region of the heart, and a detection unit configured to detect an intracardiac electrogram at a second cardiac region (ventricle) of the heart. In operation, the device: delivers a stimulation pulse to the heart; evaluates a time and at least one morphologic parameter of a responsive signal of an intracardiac electrogram, wherein the at least one morphologic parameter is chosen from: an absolute value of the signal amplitude, a width of the signal, a positive, negative and / or total area under at least a part of the signal, and a number of occurrences and / or time of occurrence of zero crossings of the signal; and identifies a dislocation of the stimulation electrode if the time of the signal is below a first threshold value and the morphologic parameter exceeds a further threshold value.

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.

In a described example, a method includes constructing a data structure including a representation of locations, within a given cardiac region, visited by a catheter during a build phase for a model of at least the given cardiac region. The method can also include receiving an electrogram associated with one or more first electrodes of the catheter at locations in a patient's heart and receiving an electrophysiological signal associated with second electrodes at respective substantially fixed locations relative to the patient's heart. The method can also include identifying, based on the electrogram and the electrophysiological signal, a cardiac region corresponding to a respective one or more of the locations of the one or more first electrodes in the patient's heart. On a graphical user interface, visual indicia can be displayed based on the identified cardiac region and the given cardiac region.