186 results about "Respiratory motion" patented technology

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

The invention relates to the technical field of artificial intelligence, and discloses a teenager scoliosis intelligent quantitative evaluation system fused with multi-modal imaging. The system comprises a multi-modal data management module, a cross-modal registration fusion module, a dynamic quantitative analysis module, a risk prediction module, a three-dimensional reconstruction module, a report generation module and a system management module, and by setting a multi-modal registration verification mechanism, accurate matching of a bony structure and soft tissue is ensured. Key parameter calculation errors caused by registration failure are reduced, the three-dimensional reconstruction precision is greatly improved compared with traditional single-mode evaluation, a dynamic respiration compensation module is introduced, in the spine deformation parameter quantification stage, spine physiologic changes in a respiration movement period are automatically captured, measured values are corrected based on an individualized dynamic model, and the accuracy of three-dimensional reconstruction is improved. Pathological progression and physiological fluctuation are distinguished, the misjudgment risk caused by neglecting breathing phase change in traditional static measurement is avoided, and the reliability of scoliosis progression risk assessment is improved.

The invention discloses a tumor respiratory movement trajectory prediction method and system based on spatio-temporal feature separation LSTM, and the method comprises the following steps: collecting a four-dimensional CT respiratory cycle image, extracting a tumor centroid coordinate, and generating a standardized tumor centroid coordinate sequence through sliding window standardization; the method comprises the following steps: inputting bidirectional LSTM to calculate Euclidean distance screening features and weighting to generate a weighted spatial feature vector sequence, extracting time recursion feature weighted fusion through one-dimensional convolution and unidirectional LSTM, predicting a tumor motion trajectory through autoregression, and comparing real-time coordinate correction to generate an adaptive correction trajectory prediction result. A four-dimensional image sequence is extracted and standardized through registration, a two-way network is combined to calculate two-way distance features to recognize breathing phase changes, spatial-temporal features are fused to establish a nonlinear recursive relation to capture tumor motion differences, real-time correction optimization is achieved through deviation statistics and weight self-adjustment, and prediction stability and trajectory fitting precision are improved.

The invention discloses a vascular respiratory movement map construction method, device and equipment and a medium, and relates to the technical field of medical treatment, the method establishes a vascular digraph based on a topological connection relationship in a vascular structure, and quantifies the overall displacement of a blood vessel and the posture of each branch through the extraction of structure control points and the calculation of branch bending energy. The propagation direction of motion between branch points is defined, and consistency constraint is provided for vascular motion modeling. Vascular deformation in motion modeling is quantified by adopting adaptive deformation compensation, and motion deformation of each section of a branch is represented by using rotation measurement and stretching measurement under a spherical coordinate system of a manifold space. Meanwhile, the bending energy weight is adaptively distributed for each branch based on the vascular graph and the SRVF constraint, and the learning efficiency of the model for local attitude deformation is improved.

Various methods and systems are provided for x-ray imaging. In one embodiment, a method includes acquiring a plurality of fluoroscopic images depicting an interventional tool positioned relative to an anatomy 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 anatomy of interest in the corrected plurality of fluoroscopic images, and displaying images with the segmented interventional tool registered to the anatomy of interest. In this way, a practitioner may view the position and movement of an interventional tool located within a patient relative to static images of the anatomy without motion artifacts or errors induced by patient motion such as respiratory motion or cardiac motion.

An apparatus for generating motion control data for a robot is provided. The apparatus comprises input interface circuitry configured to receive first input data indicative of a target motion to be performed by a robot arm of the robot relative to a first user and receive second input data indicative of a breathing motion pattern of a second user. The apparatus further comprises processing circuitry configured to modify the target motion based on the breathing motion pattern and generate the motion control data, wherein the motion control data are indicative of the modified target motion. The apparatus further comprises output interface circuitry configured to output the motion control data.

The invention relates to a multi-mode human body respiration prediction method and system. The method comprises the steps that a difference value time sequence T is obtained through a visible light video and a thermal infrared video, and then the respiration frequency f1 of a target human body is obtained; collecting an electrophysiological signal containing a surface diaphragm electromyogram from the thoracic skin surface by means of an electromyographic signal collection system, and processing the electrophysiological signal to obtain a separated diaphragm electromyographic signal E; the heart rate f2 of the target human body is obtained through an electrocardiosignal collecting system; acquiring an environmental parameter Y of the target human body by using an environmental parameter acquisition system; the parameters obtained in the step S1-4 are processed, and a breathing state evaluation model is built; and S4, optimizing and constructing a BP neural network, and predicting the respiratory movement state of the human body by matching the sample data obtained in the step S5 with the BP neural network.

The invention provides a self-navigation heart magnetic resonance imaging method and system based on self-adaptive fan-shaped sampling. The method comprises the steps that an ideal square kyz plane, a real rectangular kyz plane and the affine transformation relation between the ideal square kyz plane and the real rectangular kyz plane are established; dividing the real rectangle kyz plane into n concentric annular areas; a radial spoke ideal track rotating along a golden angle is formed in the ideal square kyz plane; mapping the data into a real rectangle kyz plane to generate a basic track; establishing a fan-shaped search window in an angle near each spoke of the basic trajectory, forming n fan-shaped ring crossing regions with the concentric ring region, and optimizing the position of a sampling point in each fan-shaped ring to obtain an optimized sampling trajectory; scanning is carried out according to the adjusted optimized sampling track, and a breathing motion field is estimated after box separation is carried out on collected k space data, and a three-dimensional image is reconstructed. According to the invention, the sampling repetition rate of the k space can be reduced, the sampling efficiency can be improved, and better imaging quality can be formed in shorter imaging time.

Disclosed herein is a medical device for tracking movement of a region of a patient's body. The region has a range of motion, for example a range of respiratory motion. The device comprises a controller configured to determine a motion of the region based on one or more initial images depicting at least part of the region. The controller is further configured to predict, based on the determined motion, a motion event time at which at least one property associated with the motion or position of the region will meet at least one criterion, wherein the at least one criterion comprises the region being located at a particular point in its range of motion. The controller is further configured to determine, based on the predicted motion event time, at least one subsequent image capture time at which at least one subsequent image should be captured.

The invention relates to the technical field of medical image processing and surgical navigation, in particular to a liver segmentation, modeling and surgical simulation system based on a cascade neural network and a multi-scale reconstruction technology. The multi-scale dynamic registration module is used for carrying out coarse segmentation and fine segmentation on liver and focus areas of the medical image and is used for non-rigid registration and respiratory motion compensation of the multi-modal medical image; the physical enhancement modeling engine is used for constructing a liver vessel tree three-dimensional model containing hemodynamic characteristics; the operation navigation and risk assessment module is used for performing real-time navigation and operation path planning in the operation; and the self-adaptive computing framework provides underlying computing resource support for the system. And high-precision segmentation: through a cascade recursive network and a boundary sensitive loss function, the liver tumor segmentation Dice coefficient reaches 94-96%, which is increased by 6-12% compared with the traditional method.

The invention relates to a PET / CT (positron emission tomography / computed tomography) image breathing motion artifact correction method based on a triple subgroup convolutional network, and belongs to the field of nuclear medicine imaging. The invention provides a lightweight triple-subgroup convolution kernel, and the convolution kernel focuses on analyzing the rotation and translation characteristics of coronal planes, sagittal planes and cross sections closely related to PET / CT images so as to realize efficient detail analysis under the condition of clinical small samples. And then, a Transform-subgroup convolution fusion encoder and a pyramid network are constructed in combination, a constructed triple subgroup convolution registration network can combine sub-deformation fields of multiple dimensions to obtain a smooth deformation field, and the deformation field is used for spatial distortion transformation to achieve the purpose of artifact correction. According to the method, artifact correction experiment comparison is carried out by using pixel body membrane simulation data and clinical data, the superiority of the method in the aspects of robustness and generalization ability is proved, and the quality of PET / CT images is effectively improved.

The invention discloses an AI auxiliary radiotherapy dose intelligent optimization device, which comprises a multi-modal image fusion module, a dose distribution prediction module, a dynamic adaptive optimization module and a prognosis model integration module, and is characterized in that the multi-modal image fusion module is used for aligning anatomical features of different images, eliminating respiratory motion artifacts and predicting the dose distribution of the different images; the dose distribution prediction module is used for rapidly predicting radiotherapy dose distribution based on anatomical features and historical data, the dynamic adaptive optimization module is used for monitoring anatomical changes in real time and dynamically adjusting a dose plan, and the prognosis model integration module is used for quantifying correlation between dose distribution and radioactive injury risks. High-precision alignment and respiratory motion artifact elimination of an anatomical structure are achieved through the multi-modal image fusion module, three-dimensional dose calculation is rapidly and accurately conducted through the dose distribution prediction module, organ displacement and deformation are effectively coped with through the dynamic self-adaptive optimization module through a real-time image monitoring and reinforcement learning algorithm, and the accuracy of the three-dimensional dose calculation is improved. And the prognosis model integration module constructs an individualized risk prediction model.

The invention discloses an accurate X-ray radiation dose measurement system based on artificial intelligence, which comprises the following steps: acquiring CT image data of a patient and pulse irradiation parameters of radiotherapy equipment through terminal equipment, and calculating a patient specific anatomical structure dynamic influence factor set, a pulse waveform time domain characteristic factor and a tissue interface scattering correction factor; all the parameters are substituted into a dose measurement formula to obtain an accurate X-ray radiation dose value; through a deep fusion image recognition technology (such as a high-resolution anatomical structure, tissue density gradient and heterogeneous region distribution provided by CT / MRI), a secondary particle scattering effect of a junction region of a tumor and a normal tissue can be accurately represented; and dynamic anatomical information (such as respiratory movement and organ displacement) in the image is combined to correct the time characteristic (such as dynamic change under an instantaneous high dose rate) of the pulse radiation field in real time, so that the dose calculation accuracy of a fast algorithm in a complex scene is remarkably improved.

The invention discloses a small animal in-vivo multi-parameter dynamic monitoring method based on photoacoustic imaging, and relates to the technical field of biomedical imaging, and the method comprises the steps: obtaining a photoacoustic signal generated by a small animal under the excitation of pulse laser output by an optical excitation module through an acoustic detection module; reconstructing a photoacoustic image of the photoacoustic signal through a filtering back projection algorithm; calculating the similarity between adjacent images in the photoacoustic image, identifying the image of which the similarity is lower than a set frame threshold value as a cross section slice polluted by respiratory motion artifacts and removing the cross section slice, and replacing the removed image frame by using the interpolation of an adjacent artifact-free image so as to obtain an updated photoacoustic image; selecting a region of interest in the photoacoustic image, and carrying out oxygenation kinetics monitoring, pharmacokinetics monitoring, perfusion kinetics monitoring or nanoparticle in-vivo kinetics monitoring; according to the monitoring method, motion artifacts caused by breathing of the small animals are effectively inhibited, and in-vivo multi-parameter dynamics monitoring of the small animals is achieved.

The invention provides a regional anesthesia puncture path planning method and system under ultrasonic guidance, and relates to the technical field of ultrasonic image.According to the regional anesthesia puncture path planning method and system, dynamic motion information of a tissue structure is obtained by collecting ultrasonic image data of a target region and conducting motion analysis, and electrocardiosignals are synchronously collected to determine heart beat time phase information; determining a vascular movement mode based on the two, learning a time change rule by using a long-short-term memory network, and establishing an association relationship between a vascular spatial position and a heart beat phase; constructing a motion prediction model to predict the future motion trend of the blood vessel, and fusing the respiratory motion information to correct to obtain the blood vessel motion information; and finally, a safe puncture window is calculated according to the information, a regional anesthesia puncture path is planned, vascular movement prediction and safe puncture window calculation based on vascular movement rule learning and breathing correction in regional anesthesia under ultrasonic guidance can be achieved, and then the puncture path is precisely planned.

The invention discloses a method for identifying and processing an artifact region in a nuclear medicine imaging image, and relates to the technical field of medical image registration. Comprising the following steps: S1, acquiring a PET dynamic image sequence, a CT dual-energy spectrum image and a respiratory movement monitoring signal, and generating a registration quality map; s2, the PET dynamic image sequence and the CT image are combined, statistical dependence between the PET dynamic image sequence and the CT image is obtained, and a focus of an artifact area in the CT image is positioned or reconstructed and compensated; and S3, carrying out artifact reconstruction on the optimized CT image, and processing a reconstructed artifact region through a double-branch network model. According to the method, the instantaneous phase of the respiratory signal is extracted through Hilbert transform, the PET dynamic image sequence is grouped according to the respiratory phase, accurate matching of same-phase frames can be achieved, meanwhile, the registration quality map is generated through the B-spline Demons algorithm, and organ deformation artifacts caused by respiratory movement can be remarkably reduced.

A drug testing platform for simulating a lung environment includes a microfluidic chip and a stretching device. The microfluidic chip defines a microfluidic channel structure. The microfluidic channel structure includes a test unit, a cell shunt unit and a drug shunt unit. The stretching device is configured to stretch and contract the test unit in a manner of a reciprocal motion, thereby making degree of deformation of a plurality of test area of the test unit to be same during the reciprocal motion. The drug testing platform for simulating the lung environment according to the present disclosure may configure an environment that simulates a breathing motion of a human lung, and obtain multiple results within a single test.

The application provides a tumor target area pose real-time tracking and prediction method and system, equipment and a medium. The abdominal respiration monitoring device of the system is used for detecting abdominal respiration deformation. The multi-channel array flexible acquisition sensor has deformability and is used for detecting back muscle electric signals and pressure change signals. The body surface information acquisition device is used for acquiring human chest and abdomen point cloud data. The main controller is used for processing data collected by the abdominal respiration monitoring device, the multi-channel array flexible acquisition sensor and the body surface information acquisition device, and obtaining a predicted position of a moving tumor target area. The application adopts back pressure muscle electric information and abdominal respiration deformation information and binocular vision information respiration movement evaluation, constructs a mapping relationship from external movement to internal tumor movement through external multi-mode data and 4D-CT information, combines a big data model, and realizes internal tumor pose prediction and evaluation.

The application discloses a respiratory motion estimation method, device and equipment and a medium, relates to the technical field of motion estimation analysis, adopts an unsupervised sparse-dense motion estimation framework with sequence motion consistency constraints, and can quickly and accurately realize motion estimation of a liver ultrasound image sequence under the influence of respiratory motion. A sparse-dense coarse-to-fine registration strategy based on sparse point guidance is used to accurately predict the motion field between adjacent respiratory state images, a sparse key point automatic detection guided rigid registration network is designed to automatically detect sparse key points from the images in an unsupervised manner, and a multi-source structured feature guided deformation densification network is constructed to predict the dense elastic deformation component of the motion between the images. In combination with the idea of motion decomposition and coincidence, sequence motion consistency constraints are constructed based on a small motion sequence between a plurality of adjacent respiratory states, the continuity of motion in the time flow is strengthened, and the motion estimation accuracy is further improved.

The embodiment of the present application provides a method for determining a body surface marker placement area and related products, which comprises: obtaining sample data of a plurality of patients, extracting two body surface contours of each patient according to two medical image data of each patient in the sample data, calculating a displacement change amount of each pixel point of the body surface of each patient under two different breathing states according to the two body surface contours of each patient and performing clustering processing to obtain L-class clustering results of the body surface movement of each patient; dividing the body surface of the patient into N marker placement areas according to the distribution of the L-class clustering results of the body surface movement of the plurality of patients; mapping the L-class clustering results of the body surface movement of each patient to the N marker placement areas; and obtaining M marker placement areas with larger displacement amounts according to the displacement amounts corresponding to the N marker placement areas. The accuracy of a respiratory motion correlation model established based on the body surface marker movement can be improved.

The invention discloses a respiratory movement tracking method and system, computer equipment and a medium thereof, which are applied to the technical field of medical treatment, and the method comprises the following steps: inputting a CT image sequence into a network model pre-constructed based on a conditional diffusion model, identifying the morphological change of a target area along with respiration, and generating a sequential deformation field changing along with respiration; selecting representative tracking points such as organ edge special position points and focus area center points from a target area, tracing position information at different moments according to a time sequence deformation field, calculating displacement and forming a displacement track; irrelevant frequency interference in displacement track data is filtered out through a band-pass filtering technology, breathing movement related signals are highlighted, phase information is extracted through Hilbert transform, a breathing movement curve is drawn, visual presentation and tracking of breathing movement are achieved, doctors can be helped to more accurately grasp the breathing law of a patient, and the accuracy of breathing movement tracking is improved. Errors caused by respiratory movement are avoided in advance in surgical planning.

The invention relates to the technical field of image segmentation, in particular to an automatic real-time outline drawing system for a liver tumor ablation area based on an ultrasonic image. The method comprises the following steps: firstly, registering and aligning preoperative and intraoperative ultrasonic images, and eliminating displacement deviation caused by factors such as respiratory movement and probe shaking; further based on the essential difference between the ablation region and the surrounding normal liver tissue in ultrasonic echo expression, dynamic changes of region features are analyzed to obtain ablation feature parameters of each sub-region in the intraoperative ultrasonic image so as to evaluate the ablation state, and then the image is enhanced based on the ablation state so as to enhance the boundary definition, and finally the ablation state of the intraoperative ultrasonic image is evaluated. And the outline drawing accuracy of the outline of the liver tumor ablation area is improved.

This invention provides a non-contact method, device, equipment, and storage medium for measuring trunk respiratory parameters. It synchronously acquires three-dimensional motion data of optical reflective markers during respiration, with these markers positioned at multiple pre-defined anatomical landmark locations on the subject's trunk. Based on the three-dimensional motion data, it determines the volume change of the spatial envelope formed by the optical reflective markers. According to a pre-established mapping relationship between the volume change and tidal volume, it converts the volume change into tidal volume. Based on the signal of the volume change over time, it extracts the respiratory rate. Finally, it calculates the total respiratory volume based on the tidal volume and the respiratory rate. By combining optical motion capture with spatial geometric modeling, it achieves non-contact, continuous, and high-precision measurement of trunk respiratory motion, effectively overcoming the shortcomings of traditional methods, such as reliance on patient cooperation, invasiveness, expensive equipment, and complex operation.

This invention relates to the field of ultrasound examination auxiliary control, specifically disclosing a self-orthogonal scanning control method for an abdominal ultrasound robot. The method includes the following steps: system initialization, defining the end-effector coordinate system and configuring the hardware; then, by sampling the point cloud through the camera, fitting the surface normal to key points and neighborhoods along the scanning path, completing coarse alignment, and switching to high-frequency force feedback fine-tuning. The force-position hybrid control main loop is initiated, and the S variable is set according to the contact force state: if the contact force meets the standard, scanning proceeds along the set direction; otherwise, compensation is performed in a specific direction. This method, with "force feedback self-orthogonal" as its core, can adapt to complex curved surfaces and respiratory motion scenarios, optimize imaging quality and acoustic coupling effects, suppress measurement errors, and has strong anti-interference capabilities and high hardware compatibility, reducing the difficulty of implementation.

The invention relates to an anesthesia puncture visual navigation system combining augmented reality and three-dimensional reconstruction, and belongs to the technical field of anesthesia puncture visual navigation. The anesthesia puncture visual navigation system comprises a physiological movement monitoring unit used for simultaneously collecting respiratory movement cycle data and soft tissue elastic deformation information; a comprehensive motion state data packet is generated by calculating the correlation coefficient of the breathing amplitude and the tissue elasticity modulus change rate, the space deviation caused by breathing motion and soft tissue deformation of a patient to puncture navigation can be effectively compensated, the puncture precision is remarkably improved compared with a traditional static navigation system under the normal breathing state of the patient, and the puncture accuracy is improved. The puncture failure rate and the complication risk are effectively reduced, the problem that precision is further deteriorated due to the fact that correction parameters are still used blindly when registration quality is deteriorated in a traditional system is solved, dependence on personal experience of doctors is reduced, and the problem that the system is completely invalid possibly caused by a single correction mode is solved.

The invention relates to the technical field of surgical navigation, in particular to a children urinary system endoscopic surgery visual auxiliary operation system which comprises a respiratory displacement field generation module, a tissue stiffness inversion module, a visual scale correction module, a tangential gradient calculation module and an anisotropic registration correction module. According to the method, a respiration displacement field is analyzed to invert tissue hardness parameters, a preoperative physical model with biomechanical characteristics is constructed, non-rigid deformation behaviors of kidneys and ureters of children in a stress state are simulated, the physical depth of pixels is calculated in combination with endoscopic image characteristics, sparse feature point cloud is converted into real-scale spatial data, and the spatial depth of the kidneys and ureters of the children is calculated. And according to the tangential gradient change of the path, constructing an anisotropic weight matrix to dynamically adjust the registration constraint intensity, and driving the virtual model to follow the movement track of the endoscope to carry out adaptive deformation correction, so that real-time three-dimensional visual guidance for soft tissue respiratory movement and contact deformation is realized.

Disclosed in the present invention are a motion management method and system, and a related device, which relate to the technical field of radiotherapy. The system comprises: a real-time imaging module used for acquiring a body surface contour of a patient in real time and acquiring a respiratory curve of the patient according to the body surface contour, the body surface contour being used for auxiliary positioning of the patient; and a 4D acquisition module used for sending a time signal to a sliding gantry CT according to the respiratory curve of the patient to establish a respiratory phase synchronization timing sequence, wherein when a treatment couch is at a scanning position of the sliding gantry CT, according to a real-time respiratory signal of the patient, 4DCT images or breath-hold images of the patient in different respiratory phases are acquired using the sliding gantry CT. The 4DCT images or the breath-hold images are used for registration with a positioned 4DCT image, thereby overcoming the drawbacks that previously only tumor images at a certain respiratory phase are obtainable and the images are prone to motion artifacts, resulting in errors in registration. As a result, the registration accuracy of a tumor that is greatly affected by respiratory motion is improved, and the clinical efficiency is enhanced.

The invention discloses an X-ray machine intelligent examination scheme creation method based on pet identity recognition, and relates to the technical field of veterinary diagnostic images.The method has the advantages that variety-specific artifacts are accurately inhibited, modeling is performed through breathing movement driven by skeleton points, high-frequency vibration noise is separated, dynamic blurring of a trachea area of a short-nose dog is eliminated, and the accuracy of the examination scheme is improved. The tiny lesion detection capability is improved; the limitation of a single angle threshold value is avoided by adopting a composite geometric criterion, so that pets with different body types can be clearly imaged under abnormal body positions; full-process identity data binding, biological characteristic penetration scanning protocol formulation, radiation protection and data storage links are provided, real-time association of images and individual medical records is ensured, and manual checking errors are avoided; a block chain evidence storage mechanism is introduced to meet the electronic medical record compliance requirement of a diagnosis and treatment institution, and cross-institution data security sharing is realized; historical parameters are automatically loaded, repeated debugging is reduced, new pets are rapidly initialized through a variety parameter library, and the equipment preparation time is remarkably shortened.

The present invention discloses a dual-arm puncture robot system integrating ultrasonic and tactile information, which includes an ultrasonic robotic arm, an ultrasonic probe, a flexible tactile sensor matching the probe, a puncture robotic arm, a puncture mechanism, a flexible tactile sensor matching the puncture needle, an ultrasonic imager, and an industrial control computer. The ultrasonic robotic arm is installed with the ultrasonic probe and the flexible tactile sensor matching the probe. By fusing ultrasonic information and tactile information, artificial intelligence is used to identify and locate the positions of the puncture needle and the tumor. At the puncture end, by adjusting the puncture robotic arm to keep the contact force of the flexible tactile sensor constant, real-time tracking compensation of respiratory movement can be achieved, and the relative fixation between the puncture needle and the abdomen can be maintained. The present invention achieves the effects of improving the accuracy of ultrasonic positioning and compensating for the dynamic respiration of the human body by introducing a tactile sensor. The overall system structure is simple and convenient to use, without the need for preoperative device deployment or calibration, and has great application value in clinical medical puncture operations.