52 results about "Vascular architecture" patented technology

The invention provides an aorta morphological characteristic automatic measurement and clinical aid decision-making method and system based on artificial intelligence, and is suitable for three-dimensional medical image analysis of aorta and main branches thereof. The method comprises the steps of image data acquisition and preprocessing, vascular structure automatic segmentation, center line extraction and key point positioning, multi-parameter automatic measurement, risk assessment and clinical aid decision making, data standardization output and system feedback optimization. The corresponding system comprises an image acquisition and processing module, a segmentation module, a center line extraction module, a parameter measurement module, an auxiliary decision-making module, a data output module and a verification optimization module. Through a deep learning model and a morphological calculation means, efficient recognition and quantitative analysis of a vascular structure are realized, and individualized risk suggestions and surgical plans are output based on a statistics and machine learning model. According to the method, the automation, standardization and intelligence levels of blood vessel analysis are improved, and the clinical auxiliary decision-making capability under complex cases is enhanced.

The invention discloses an ultrasonic positioning microscopic imaging method and system based on joint resolution perception and vision Mangbar, and the method specifically comprises the steps: obtaining and preprocessing a plurality of modal angiography data, generating a microbubble motion sequence in combination with blood flow velocity field simulation data, and constructing a data set; inputting the data set into a mixed hierarchical feature pyramid fusion network, carrying out probability density estimation on a microbubble position based on a real-time multi-scale density field estimation algorithm, and introducing a resolution perception algorithm to enhance microbubble features to obtain a plurality of short-track images; performing dynamic feature decoupling and time-frequency analysis on the short-track image, extracting and encoding velocity component features and spatial-temporal context information of a vascular structure, and constructing a double-branch processing network based on visual Mangbar to obtain images respectively reflecting forward motion contribution and backward motion contribution, and finally generating a high-resolution blood vessel image through image fusion. The method has an important value for improving the efficiency and precision in microvascular imaging.

The invention discloses a two-stage segmentation method for a vascular structure in a medical CT image and a related device. The method comprises the following steps: acquiring the medical CT image and a coarse segmentation image thereof; performing blood vessel shape interpolation processing on the coarse segmentation image and performing HU value similarity judgment to generate first annotation data and HU value similarity mapping data; performing vessel connectivity judgment on the HU value similarity mapping data, extracting a condition label and generating second annotation data; and extracting a target region in the repair compensation data by using a blood vessel extraction method based on boundary corrosion, and finally obtaining a target segmentation result. By combining HU value judgment, vessel connectivity analysis and boundary corrosion technologies, the accuracy and connectivity of vessel segmentation are improved, and the method is suitable for fine extraction of complex vessel structures.

The invention relates to the technical field of neurosurgical intelligent operations, and discloses a neuroendoscopic surgery intelligent puncture positioning system based on image navigation, and the system comprises a preoperative modeling module which constructs a preoperative tissue model and a corresponding elastic tensor field based on a three-dimensional medical image of a patient; the image fusion module is used for receiving the data of the preoperative modeling module, collecting intraoperative endoscopic images, predicting the tensor disturbance of tissues through a deep learning model, and feeding back the updated tensor disturbance to the tensor updating module; and the tensor updating module is in two-way communication with the image fusion module and is used for superposing tensor disturbance obtained by processing the intraoperative image with the preoperative tensor. Through fusion of an image navigation technology and preoperative modeling, a three-dimensional positioning frame based on neuroendoscopy operation requirements is constructed, intelligent planning and real-time correction of a puncture path are realized, important brain regions and vascular structures are effectively avoided, intra-operative risks are reduced, and positioning precision and safety are improved.

The invention relates to the technical field of intelligent medical treatment, and discloses a portable blood vessel puncture device and method based on an AI identification B-ultrasonic image, and the method comprises the steps: determining a puncture body position and a to-be-scanned surface; constructing a coordinate system; based on the two-dimensional grid layout, driving the B-ultrasonic puncture integrated equipment to perform matrix scanning in the to-be-scanned surface to obtain and visualize a plurality of static blood vessel sub-images arranged in a matrix and corresponding coordinates of the static blood vessel sub-images; performing image processing by using the plurality of static blood vessel sub-images and the corresponding coordinates thereof to obtain and visualize continuous blood vessel structure image data; an AI recognition model is constructed, the blood vessel structure image data, the multiple static blood vessel sub-images, the corresponding coordinates and preset puncture data serve as input, a puncture strategy is output and visualized, and the puncture strategy comprises target puncture data and a puncture path which are determined based on a coordinate system; and driving the B-ultrasonic puncture integrated equipment to puncture along the puncture path. According to the invention, full automation of blood vessel puncture to establish deep and shallow artery and vein channels is realized, and the puncture success rate and safety are improved.

The invention discloses a vascular surgery image intelligent optimization method, and relates to the field of medical image intelligent processing. According to the technical scheme, the method comprises the following steps: collecting original image data according to a vascular surgical image, extracting image features through a deep convolutional neural network CNN, constructing an angiography lesion sensitivity map, generating a sensitivity initial weight map by using the sensitivity map, and constructing a vascular structure topological map based on the initial weight map; and carrying out sensitivity propagation and correction by adopting a graph neural network to obtain a corrected sensitivity weight graph, and carrying out local adaptive image enhancement processing according to the corrected sensitivity weight graph to obtain an intelligently optimized image. The problem that a lesion area is not prone to protruding in angiography is solved, and the diagnosis accuracy is improved.

A computer implemented method of simulating the deployment of an implantable medical device at a target deployment position within a patient's vascular structure, the method comprising: receiving a three-dimensional model of the patient's vascular structure; representing the implantable medical device as a centreline of the implantable medical device, the centreline comprising a plurality of nodes, where each node is associated with a two-dimensional shape representing the cross-section of the device at the position of the node; positioning the centreline at the target deployment position within the patient's vascular structure; iteratively (i) calculating a force on each node if the corresponding two-dimensional shape contacts or intersects a wall of the vascular structure within the three-dimensional model, (ii) determining a movement of the node based on the calculated force and (iii) updating the position of the node based on the determined movement; determining the deployed position of the implantable medical device based on the position of the nodes of the centreline when a convergence condition is met.

A platform device for material excision or removal from vascular structures for either handheld or stereotactic table or robotics platform use may comprise a work element or elements configured to selectively open and close at least one articulable beak or scoopula configured to penetrate and remove intra-vascular materials or obstructions, or follow a central lumen of another device or over a wire in a longitudinal direction. A telescoping set of inner and outer tube axially actuated together and to actuate a beak set, axially actuated differentially, whether controlled mechanically or electronically may be combined with an outer tubular sheath, which may be full circumference or partial circumference along its length, to form a system for rotational non-sharp dissection, coring, severing off, transporting or in the reverse, depositing various fluids and solids for a variety of clinical uses.

Apparatus, systems, and methods for achieving thermally-induced renal neuromodulation by intravascular access are disclosed herein. One aspect of the present application, for example, is directed to apparatuses, systems, and methods that incorporate a treatment device comprising an elongated shaft. The elongated shaft is sized and configured to deliver a thermal element to a renal artery via an intravascular path. Thermally-induced renal neuromodulation may be achieved via direct and / or via indirect application of thermal energy to heat or cool neural fibers that contribute to renal function, or of vascular structures that feed or perfuse the neural fibers.

The invention relates to the technical field of medical image early warning, and discloses a cerebral apoplexy early warning method based on a cerebrovascular image representation group. The method comprises the following steps: acquiring a cerebrovascular image sequence, and constructing multi-modal information containing a vascular structure and hemodynamics; based on this, a multi-level representation network is constructed, and a dynamic time-varying risk representation graph is generated through node interaction and space-time modulation. Then performing multi-scale grid segmentation on the risk map to obtain a plurality of local evaluation units, and synchronously screening out candidate units with abnormal evolution according to a preset risk dynamics model; and aggregating the candidate unit features to generate a global risk evolution path, backtracking and positioning to a high-risk frame set in the original image, and outputting an early warning signal after feature decoupling and recombination. According to the method, quantitative tracking and early-stage accurate positioning of the dynamic evolution process of the stroke risk in the space-time space are realized.

The invention provides a research device for arteriosclerosis detection, and the device consists of the following parts: a multi-modal data synchronous collection module which achieves the omnibearing data capture of arteriosclerosis through integrating a flexible ultrasonic array, a PPG module, a laser speckle imaging module and a multi-modal sensor, the blood vessel structure thickness, plaque form and microcirculation dynamic parameters are synchronously obtained in the wearable device, the limitation of single function of traditional equipment is broken through, comprehensive diagnosis from macroscopic to microscopic is achieved, continuous wearing is supported through the ultrathin flexible design, the self-adaptive ultrasonic array and high-precision time sequence synchronization are combined, blood vessel elastic changes can be captured in an all-weather mode, and the diagnosis accuracy is improved. And real-time data support is provided for disease course evolution.

The invention discloses a three-dimensional microscopic whole cerebrovascular image segmentation method and system, and the method comprises the steps: stacking microscopic cerebrovascular images which are shot layer by layer in a three-dimensional space according to a shooting sequence, segmenting the microscopic cerebrovascular images into a plurality of three-dimensional blocks which are suitable for training, and carrying out the necessary preprocessing; pre-training a teacher model through a reconstruction task on the three-dimensional microscopic cerebrovascular data which is not labeled, and meanwhile, obtaining a codebook containing a rich vascular structure distribution coding mode, and supervising and finely adjusting teacher model parameters through a label on the three-dimensional microscopic cerebrovascular data which is slightly labeled; training a student model by utilizing teacher model guidance and limited annotation data, and guiding the student model to perform high-quality extraction on the data by means of a vascular structure codebook; and the trained lightweight student model is used to complete segmentation of the whole brain microscopic image, and then the three-dimensional blocks are cut and reconstructed into a complete three-dimensional whole brain blood vessel network.

This invention relates to the field of medical control technology, specifically to a glioma ECoG electrode localization system based on the fusion of multimodal MRI and intraoperative photographs. It includes a brain imaging data acquisition module for preoperative acquisition of multimodal brain imaging data and an acquisition module for acquiring intraoperative craniotomy photographs for ECoG electrode placement. The system further aligns the multimodal brain imaging data to the same three-dimensional spatial coordinate system; then segments and corrects the multimodal brain imaging data into different regions; and establishes an individualized brain region atlas based on a healthy brain tissue texture model and segmented brain regions. Vascular structures are extracted, and a three-dimensional spatial coordinate system for vascular feature points and electrode contacts is established and transformed. The minimum Euclidean distance is calculated based on the electrode contacts, and the individualized brain region atlas is labeled. This invention addresses the problems of insufficient spatial positioning accuracy of electrode contacts and difficulty in quantifying tumor-electrode distance during surgery for glioma patients, facilitating intraoperative determination of the anatomical location of the epileptogenic focus by surgeons.

The present invention belongs to the field of surgical navigation technology, and specifically refers to a surgical robot navigation system based on a neuroendoscope, wherein the system includes a surgical robot, a neuroendoscope, a high-resolution feature extraction module, a fluoroscopic registration module, and an augmented reality display device. This solution proposes a fluoroscopic registration method to optimize the traditional fluoroscopic method, compare the patient's vascular structure characteristics, and achieve accurate registration of preoperative angiographic image sequences and intraoperative fluoroscopic angiographic images, thereby improving the accuracy and stability of the image-guided navigation system; it also proposes a high-resolution feature extraction method to effectively capture detailed information in the image through a visual vocabulary, fuse the features of the left and right views, improve the accuracy of the view of the target surgical area captured by the neuroendoscope, reconstruct high-resolution images, and help doctors accurately navigate to the target surgical area.

A method (1200) includes instructing an image capture device (150) to move across an anatomy portion of a subject and capture a sequence of ultrasound image frames (154). For each corresponding ultrasound image frame, the method includes processing the corresponding ultrasound image frame to generate a respective vessel mask (312) that identifies one or more vessel portions (314) of the corresponding ultrasound image frame. The method also includes processing the vessel masks generated for the sequence of ultrasound image frames and corresponding three-dimensional position data (153) to generate a three-dimensional vessel structure map (700) representing vessels (156) within the anatomy portion of the subject. The method also includes processing the three-dimensional vessel structure map to select a candidate vessel (156C) to target for venipuncture from the vessels represented in the three-dimensional vessel structure map.

The application discloses an acute ischemic stroke prognosis prediction method based on CTA images, relates to the technical field of medical image processing, and fuses a three-dimensional visual transformer and a meta-learning strategy to train a target prognosis prediction model based on the three-dimensional visual transformer and matched with a target data acquisition condition.The target prognosis prediction model can effectively model a spatial dependence relationship in a three-dimensional CTA image across regions and layers, significantly improves the recognition ability of complex cerebral vascular structures and ischemic regions compared with a traditional convolution network which only depends on a local receptive field, and further more, the introduction of the meta-learning strategy can improve the model generalization ability under small samples and multiple data acquisition conditions, so that the target prognosis prediction model directly takes the CTA image to be predicted as input to realize automatic prognosis prediction, does not need manual segmentation or feature extraction, avoids the dependence on manual experience in the traditional method, and improves the standardization degree and repeatability of the model application.

A method for operating an X-ray facility includes supplying a vascular model of a vascular structure. A target position of an instrument that may move in the vascular structure, a starting position of the instrument in the vascular structure of the vascular model, and a path in the vascular structure of the vascular model are marked in the vascular model. Support points are defined along the path. An optimized course of projection geometries is ascertained for the support points in an optimization process of a target function using the vascular model. Apart from at least one first term, the target function also includes at least one second term that minimizes the number of changes in the projection geometry along the path due to movement of the support. Positioning parameters for actuating the positioning facility are ascertained for each projection geometry.

The invention relates to a computer-implemented method for evaluating a set of projection images when an X-ray device (1) performs maskless angiography in an examination region having a vascular structure, comprising the following steps: providing the recorded set of projection images, the set of projection images shows, in a number of projection images of the plurality of projection images, a flow of the contrast agent through the vascular structure over an elapsed flow time period; selecting a reference image from the plurality of projection images; the weight of each projection image is determined, the weight of a projection image different from the reference image is determined according to the similarity between the projection image and the reference image, and the weight of the reference image is not lower than the maximum weight of other projection images; at least one evaluation image is ascertained from the plurality of projection images, the projection images being used in accordance with the ascertained weights during the ascertainment.

Systems, methods, and computer program products for generating and displaying angiography images from multiple machine learning models that provide different sensitivity and specificity performance in the segmentation of vascular structures in an angiogram. A graphical user interface with control elements provides user control over the mixture of the multiple machine learning models and other settings, including an a pseudo brightness control and a zoom control. The displayed angiogram image is based on the settings of the control elements, and adjusts in response to changes thereto. The pseudo brightness control controls the mixture of the high specificity model versus the high sensitivity model in the displayed image. The zoom control modifies the magnification of the displayed image and increases the proportional mixture of the high sensitivity model in the displayed image. A widget or other type of control element may also be provided for controlling the mixture of the empirical data versus machine learning model output in the displayed image.

The present disclosure solves many problems. The main problem solved by the present disclosure is the problem of showing veins for patients whose veins may be hard to find. The present disclosure will greatly improve the venipuncture experiences for millions of people every day. The present invention discloses a system and method imaging objects in or behind a turbid medium comprising a light source adapted to illuminate an imaged area, an imaging device arranged to optically capture and relay an image, an electronic display to receive and display the image, and a control unit to control at least one spectral and polarization properties of the light source. Also disclosed is a camera module within an embodiment of the imaging device that provides for an alternative operation of the imaging device, without a control unit, and relaying of the image to the electronic display, which may be a tablet.

This invention discloses an ultrasound-guided microscopic imaging method and system based on joint resolution perception and visual Mamba, specifically including: acquiring and preprocessing several modal angiography data; generating microbubble motion sequences by combining blood flow velocity field simulation data to construct a dataset; inputting the dataset into a hybrid hierarchical feature pyramid fusion network; estimating the probability density of microbubble positions based on a real-time multi-scale density field estimation algorithm; introducing a resolution perception algorithm to enhance microbubble features to obtain several short trajectory images; performing dynamic feature decoupling and time-frequency analysis on the short trajectory images; extracting and encoding velocity component features and spatiotemporal context information of vascular structures; constructing a dual-branch processing network based on visual Mamba to obtain images reflecting forward and backward motion contributions respectively; and finally generating high-resolution vascular images through image fusion. This invention has significant value in improving the efficiency and accuracy of microvascular imaging.

The present invention relates to the technical field of medical images, and specifically relates to a segmentation method and apparatus for magnetic resonance vascular architectural imaging (VAI). The method and apparatus involve: performing data preprocessing on a VAI image set; constructing a deep learning model, and using the VAI image set, which has been subjected to data preprocessing, to train the deep learning model, so as to optimize a model parameter; and using the trained and optimized deep learning model to perform automatic tumor region segmentation on a new VAI image, so as to generate a segmentation result. In the present invention, by using a deep learning model such as a convolutional neural network (CNN), the features of vascular architecture can be automatically learned, and accurate segmentation is performed with regard to microvascular changes in a tumor region.

This invention discloses an endoscopic electronic staining method based on spectral unmixing. It separates the intrinsic tissue spectrum from background noise under LED white light using an improved spectral unmixing algorithm; constructs a multi-scale feature enhancement module to simultaneously enhance mucosal texture and vascular structure; designs a dynamic staining mapping function to achieve adaptive matching between staining parameters and tissue type; and finally, ensures the reliability of the staining effect through dual-index verification. This invention solves core problems such as tissue spectral overlap, low staining contrast, and poor adaptability under ordinary LED white light and CMOS imaging conditions. The method of this invention leverages algorithm optimization to unlock the potential of the imaging system, achieving software-defined staining effects.

A method of providing vascular effects includes emitting (402) infrared light toward a body part of a guest, receiving the infrared light from the body part of the guest, generating a signal indicative of the received infrared light, generating (406) a set of coordinates corresponding to a vascular structure of the body part of the guest based on the signal indicative of the received infrared light, mapping (408) the vascular structure to the body part of the guest, retrieving (410) a portion of code representing a sprite (316), generating a series of images of the vascular structure of the body part of the guest and the sprite (316), and projecting (414) the series of images of the vascular structure and the sprite (316) onto the body part of the guest.

Disclosed herein is an all-in-one methodology for the fabrication and the programming of soft machines. Instead of relying on the assembly of individual parts, the disclosed approach harnesses interfacial flows in elastomers that progressively cure to robustly produce monolithic pneumatic actuators whose shape can easily be tailored to suit applications ranging from artificial muscles to grippers. Rationalized herein are the fluid mechanics at play in the assembly of the disclosed actuators and modeled herein are their subsequent morphing. This quantitative knowledge was leveraged to program these soft machines and produce complex functionalities, for example sequential motion obtained from a monotonic stimulus. It is expected that the flexibility, robustness, and predictive nature of the disclosed methodology will accelerate the proliferation of soft robotics by enabling the assembly of complex actuators, for example long, tortuous, or vascular structures, thereby paving the way towards new functionalities stemming from geometric and material nonlinearities.

This application discloses a two-stage segmentation method for vascular structures in medical CT images and related devices. The method includes: acquiring a medical CT image and its coarse segmentation image; performing vascular morphology interpolation processing on the coarse segmentation image and performing HU value similarity determination to generate first annotation data and HU value similarity mapping data; performing vascular connectivity determination on the HU value similarity mapping data, extracting conditional labels, and generating second annotation data; and using a boundary erosion-based vascular extraction method to extract the target area in the repaired and compensated data, ultimately obtaining the target segmentation result. By combining HU value determination, vascular connectivity analysis, and boundary erosion technology, the present invention improves the accuracy and connectivity of vascular segmentation and is suitable for the fine extraction of complex vascular structures.

A computer-implemented method according to the present invention, comprising receiving (100) a patient-specific volumetric image comprising structural information about the patient's vessels represented in voxels, including extracting and / or presenting patient-specific data using a centerline, comprises a correction step (150), which comprises an iterative process including at least an operation of centering (552) the points defining the centerline by further locating them from the most proximal plane defined in the geometry, and an operation of equalizing (553) the points defining the centerline by approximating each point to the average position of its neighbors. The present invention further relates to a computer program product and an imaging system.

Embodiments herein rapidly transform computed tomography angiography (CTA) data into an interactive, holographic visualization for clinical use. In one embodiment, a system for visualizing computed tomography angiography (CTA) images comprises a data interface configured to receive CTA image data in a Digital Imaging and Communications in Medicine (DICOM) format. The system also comprises a processor configured to render a plurality of two-dimensional CTA slice images into a three-dimensional (3D) volumetric dataset from the CTA image data, to filter the 3D volumetric dataset to resolve vascular structures relative to surrounding tissue and bone, and to generate an extended reality (XR) holographic representation of the filtered 3D volumetric dataset. The system also comprises an XR display device configured to present the holographic representation to a user for the user to manipulate the holographic representation and identify a vascular abnormality.

A platform device for material excision or removal from vascular structures for either handheld or stereotactic table or robotics platform use may comprise a work element or elements configured to selectively open and close at least one articulable beak or scoopula configured to penetrate and remove intra-vascular materials or obstructions, or follow a central lumen of another device or over a wire in a longitudinal direction. A telescoping set of inner and outer tube axially actuated together and to actuate a beak set, axially actuated differentially, whether controlled mechanically or electronically may be combined with an outer tubular sheath, which may be full circumference or partial circumference along its length, to form a system for rotational non-sharp dissection, coring, severing off, transporting or in the reverse, depositing various fluids and solids for a variety of clinical uses.

The present disclosure provides a bioengineered scaffold, a preparation method thereof, and regenerative tissue, relating to the fields of tissue engineering and regenerative medicine. The bioengineered scaffold provided by the present disclosure retains the complex and unobstructed vascular structure of the target organ. As an engineered material, it has a biomimetic structure and mechanical strength that closely resembles that of a living target organ, supports cell adhesion and growth, facilitates the formation of new functional tissues for medical purposes, and provides new ideas for the construction of in vitro organ function models.