277 results about "Angiography" patented technology

The invention provides a contrast image analysis method and device for digital diagnosis and treatment equipment. The method comprises the following steps: predicting a disease progress condition based on a focus volume characteristic parameter, a density statistical parameter and a quantitative index of a focus surrounding tissue density difference; judging whether the prediction result of the disease progress condition reaches a preset risk threshold range or not, if the prediction result is within the preset risk threshold range, judging that the disease progress condition is in a risk state, and generating high-risk early warning information; and judging the severity of the illness state according to the judgment result of the illness state progress condition prediction and in combination with the fitting data of the focus volume and density changing along with time, performing stage classification on the focus progress process, and generating a quantitative illness state diagnosis report.

The invention, which relates to the technical field of medical image processing and intelligent diagnosis, discloses an ERCP image intelligent identification system based on multi-modal fusion, comprising an image acquisition module, an image preprocessing module, a multi-modal fusion model module, a parameter measurement module, an early warning judgment module and a real-time interaction module. The image acquisition module is used for acquiring an ERCP radiography original image in a DICOM format, performing privacy information desensitization processing on the original image, and reserving pixel pitch and catheter size parameters in DICOM metadata; the system has the beneficial effects that privacy protection and parameter retention are realized through the image acquisition module, precise structure segmentation is realized in combination with a multi-modal fusion model, key indexes are automatically quantized by means of the parameter measurement module, intraoperative visualization is enhanced in cooperation with the real-time interaction module, and the accuracy of the system is improved. The problems that traditional ERCP analysis depends on experience and lacks quantitative standards and decision lags are effectively solved, and the method has the remarkable advantages that diagnosis precision is improved, operation time is shortened, and the medical resource utilization rate is optimized.

The invention relates to the technical field of medical image processing, in particular to a pulmonary vascular motion compensation method based on a space-time sequence, and the method comprises the steps: constructing a four-dimensional space-time data set based on a three-dimensional CT image sequence and monitoring data; based on the four-dimensional spatiotemporal data set, calculating a motion field between adjacent data frames to obtain a continuous motion field based on pixels; according to the pixel-based continuous motion field, vascular motion is decomposed to obtain separated cardiopulmonary motion; calculating a predictive field and a local predictive field of vascular motion based on the separated cardiopulmonary motion; according to the local prediction field, adjusting imaging parameters of the angiography system; estimating a non-rigid motion field between the images through a frame registration method based on the separated cardiopulmonary motion and the adjusted imaging parameters; according to the non-rigid motion field, an image after motion compensation is generated through a multi-frame image fusion method, and high-precision compensation of complex pulmonary vascular motion is achieved by constructing a four-dimensional spatiotemporal data set and combining an innovative vascular motion decomposition model.

A method and apparatus for calculating the blood flow rate in a coronary artery, an electronic device and a storage medium. The method for calculating the blood flow rate in a coronary artery comprises the following steps: S1, acquiring an angiography image of the coronary artery, segmenting the angiography image of the coronary artery by using deep learning, and obtaining segmented images of a main vessel (S1); S2, calculating the length of the main vessel in each segmented image frame on the basis of the segmented images of the main vessel (S2); and S3, obtaining the blood flow rate in the main vessel on the basis of the calculated change of the lengths of the main vessel with time (S3). By using the method and apparatus for calculating the blood flow rate in a coronary artery and the electronic device, the automation of the calculation of the blood flow rate in a coronary artery is achieved, the calculated blood flow rate in the coronary artery is more accurate, and the calculation method is simple.

An ophthalmic imaging system provides an automatic focus mechanism based on the difference of consecutive scan lines. The system also provides of user selection of a focus point within a fundus image. A neural network automatically identifies the optic nerve head in an FA or ICGA image, which may be used to determine fixation angle. The system also provides additional scan tables for multiple imaging modalities to accommodate photophobia patients and multi-spectrum imaging options.

The invention discloses a whole-process vascular interventional operation training method and system based on a virtual reality technology, and the method comprises the steps: firstly, developing a vascular interventional mechanics and collision simulation module, and achieving the deformation simulation of a blood vessel and a guide wire through the combination of position dynamics, a region shape matching technology and a Cosseerat elastic rod model, the collision between the blood vessel and the guide wire is treated by adopting a wide-to-narrow three-layer progressive collision detection method; secondly, constructing an immersive virtual operation scene through model construction and rendering optimization; visual angle roaming, instrument operation and interface interaction are fused, and multi-mode interaction control driving intervention training is achieved; finally, based on a'module-event 'dual-drive framework, operation tasks such as anesthesia, puncture, radiography, intervention, stent release and hemostasis are sequentially connected in series through a unified state machine, and whole-process training is achieved. The accuracy and stability of blood vessel and guide wire simulation are improved, the training immersion sense is enhanced, interventional operation whole-process training is achieved, and high teaching and practical value is achieved.

A system and / or method uses a trained U-Net neural network to remove flow artifacts from optical coherence tomography (OCT) angiography (OCTA) data. The trained U-Net receives as input both OCT structural volume data and OCTA volume data, but expands the OCTA volume data to include depth information. The U-Net applies dynamic pooling along the depth direction and weighs more heavily the portion of the data that follows (e.g., along the contours of) select retinal layers. In this manner the U-Net applies contextually different computations at different axial locations based at least in part on the depth index information and / or (e.g., proximity to) the select retinal layers. The U-net outputs OCT volume data of reduced flow artifacts as compared with the input OCTA data.

The invention relates to the technical field of medical information, in particular to a coronary artery stenosis intelligent diagnosis system and method based on deep learning, and realizes efficient and accurate diagnosis through data acquisition, marking, convolutional neural network model construction, evaluation and acceleration modules. The system preprocesses the contrast image, marks the narrow position and degree, constructs a convolutional neural network model combined with an attention degree graph, and optimizes parameters to improve the accuracy; through parallel processing, the diagnosis time is shortened, the problems of an existing diagnosis method are systematically and innovatively solved, and the method has high accuracy, high efficiency and good interpretability and is expected to become an important clinical auxiliary tool for early screening and emergency diagnosis of the coronary heart disease.

The present invention discloses a focal learning-based method for intelligent CT angiography imaging. (1) Acquiring NCCT images and their corresponding real CTA images; (2) Constructing an adversarial network model comprising a generator, a corrector, and a discriminator; (3) Formulating a joint focal learning loss function for the generator-corrector pair and a separate loss function for the discriminator; (4) Training the adversarial network model using the training set, and validating the trained model using the validation set; (5) Identifying the generator with the best test performance by virtue to the test set. The invention establishes a joint focal learning loss function, which allows the generator to create synthetic CTA images that more effectively emphasize target areas, like vascular tissues. Furthermore, a corrector is incorporated into the invention to facilitate improved registration and alignment between NCCT images and CTA images.

The invention relates to a real-time three-dimensional reconstruction method based on intracavity ultrasound and fistula radiography, which comprises the following steps of: extracting a fistula channel projection path through a fistula expansion outline after a contrast agent is injected during radiography; the ultrasonic probe is linked to detect the tissue interface change in the image, and reverse matching is carried out on the tissue interface change and a high-density area of the radiography image; establishing a spatial perspective calibration system based on an imaging cone projection relation, and estimating a rotation / translation pose of the intracavity ultrasound image ICE in a three-dimensional space; outputting a spatial state sequence of the ICE frame; performing curved surface resampling on the ICE image, and establishing a sectioned image stack; continuously receiving a new ICE image and a contrast frame; performing model fine tuning through a local deformation field; operating a spatial feature extraction network on the fistula volume model; judging local expansion / contraction and curvature change in the three-dimensional model to identify a fistula opening point; a fistula main channel and branch paths are analyzed, and the fistula complexity level is judged through topology analysis; and comparing with a preoperative map to assist in preoperative pre-judgment and intra-operative path planning.

The invention belongs to the technical field of medical imaging, and discloses an angiography high-pressure injection system which comprises the following steps: acquiring physiological data information of a patient, and calculating an individualized contrast agent injection parameter coefficient based on the physiological data information; acquiring anatomical structure information of a blood vessel region of the target patient, and performing pressure evaluation based on hemodynamic characteristics to obtain blood vessel pressure bearing capacity and injection pressure regulation factors; performing physiological status assessment based on the physiological data information of the patient to obtain a physiological status influence factor and generate a risk marker value; performing multi-source data fusion according to the injection pressure regulation factor, the physiological status risk marker value and the historical radiography record, and constructing the radiography safety degree of the blood vessel sub-region; an optimal injection parameter path is obtained based on the radiography safety degree in combination with a deep reinforcement learning algorithm, and high-pressure injection equipment is controlled to execute injection operation; according to the invention, accurate and personalized control of contrast agent injection is realized, and the safety and imaging effect of contrast examination are remarkably improved.

The embodiment of the invention discloses a long-range blood vessel imaging method and system.The method is applied to C-shaped arm imaging equipment, the C-shaped arm imaging equipment comprises a C-shaped arm, and a ray emitter and a ray detector are arranged at the two ends of the C-shaped arm respectively; the method comprises the following steps: injecting a contrast agent into a blood vessel of a target object; projection data are obtained in a spiral scanning mode through a ray emitter and a ray detector at the two ends of the C-shaped arm; and generating an angiography image based on the projection data.

The invention discloses a blood vessel three-dimensional reconstruction method and device based on a multi-view angiography image, computer equipment and a storage medium, and relates to the field of blood vessel simulation, and the method comprises the following steps: firstly, selecting a key frame from a multi-angle angiography sequence, carrying out high-precision blood vessel tree segmentation, and determining a reference image; secondly, constructing a three-dimensional projection cone by extracting projection parameters, taking an intersection of the three-dimensional projection cone and discretizing the intersection, iteratively screening by using a reference image and other visual angle information, and precisely defining a blood vessel tree spatial domain; the three-dimensional model reconstruction method is characterized in that a spatial dispersion and traversal projection thought is introduced, so that the calculation complexity is not remarkably improved when the number of visual angles is increased, and the limitation of the number of visual angles in traditional multi-visual-angle reconstruction is broken through; the method has the advantages of high precision and high expansibility, and can meet the harsh requirements of hemodynamic simulation and other high-end clinical applications on the precision of the blood vessel model.

The invention relates to the technical field of image processing, in particular to a cerebrovascular image recognition and analysis system based on deep learning. The system comprises a cerebrovascular image registration module, a CT pixel communication module, a lesion area identification and segmentation module and a lesion risk assessment module, and can obtain a cerebrovascular CT angiography image, perform image enhancement and multi-modal image registration, and generate a cerebrovascular CT multi-modal registration image sequence; acquiring cerebrovascular CT pixel point clouds in the corresponding modals and performing blood vessel cluster connectivity analysis to generate corresponding cerebrovascular CT pixel cluster connectivity graphs in the respective modals; and based on a deep learning multi-branch network architecture, introducing an attention mechanism to carry out lesion region identification and segmentation, obtaining a corresponding cerebrovascular stenosis degree and a lesion thrombus area, and carrying out lesion risk number evaluation on the corresponding cerebrovascular CT image lesion region to obtain a cerebrovascular CT lesion risk number. According to the invention, accurate recognition of the cerebral vascular image lesion condition can be realized.

A lung CT blood vessel image segmentation and reconstruction system based on multiple interpretable features performs further fine-grained segmentation on the basis of main airway features through a feature extraction module to obtain a plurality of connected domains, performs multi-stage feature extraction on the connected domains, and outputs multi-scale feature information; the blood vessel segmentation module generates an information matrix according to the indexes and the pulmonary parenchyma mask, generates a blood vessel network segmentation result through an adversarial network and performs post-processing; and the model reconstruction module performs three-dimensional reconstruction on the blood vessel network segmentation result and the pulmonary parenchyma mask to obtain a 3D model. According to the method, on the basis of anatomical priori knowledge of pulmonary vessels and airways, a large amount of multi-scale feature information is utilized, the information loss of the two-stage segmentation network is reduced, and the network adaptability and the learning ability of the relation between pixels under the complex condition are improved. In the aspect of system design, a modular design and management mode is adopted, efficient resource access is achieved, and meanwhile the operability and usability of the system are improved through end-to-end process design.

The invention relates to a conveying device, a catheter assembly comprises an inner core tube and an outer sheath tube, the inner core tube is movably assembled in a sheath tube inner cavity of the outer sheath tube, a restraining shaft cavity is formed between the outer wall of the inner core tube and the inner wall of the outer sheath tube and used for restraining a stent graft, and an adjusting assembly comprises an adjusting wire body. The adjusting line body is movably arranged in a collecting shaft cavity between the inner core tube and the outer sheath tube in a penetrating mode and used for being detachably connected with the far-end area of the small bent side of the stent graft. And an operator pulls the adjusting wire body outside the living body to adjust the position of the far-end area of the small bent side of the stent graft. In the adjusting process, the large bent side of the stent graft keeps still, the small bent side of the stent graft deviates towards the near end by a certain angle, an operator can judge whether the small bent side of the stent graft is adjusted to the expected form or not through auxiliary means such as DSA radiography, and finally the far-end area of the small bent side of the stent graft forms a good adherence effect. And the'beak 'phenomenon is solved.

The invention discloses an intracranial anterior artery circulation functional evaluation method and device, computer equipment and a storage medium, and relates to the field of vascular function evaluation, and the method comprises the steps: based on an intracranial angiography image and near-end pressure data, carrying out the five working processes of data input, blood vessel segmentation and segmentation, boundary condition calculation, hemodynamic simulation and functional parameter calculation; according to the method, functional parameters verified in the coronary artery field are migrated to intracranial blood vessel evaluation without additional equipment, the clinical problem that traditional morphological evaluation is not matched with functionality is solved, individualized function quantification is achieved by integrating blood vessel morphological characteristics and hemodynamic simulation, and the method is suitable for being applied to intracranial blood vessel evaluation. According to the method, an objective basis is provided for cerebrovascular disease diagnosis and treatment, the intracranial vascular disease is promoted to be converted from anatomy orientation to function orientation, the clinical decision accuracy is remarkably improved, popularization and application of functional evaluation in the field of neural intervention are accelerated, and construction of a cerebrovascular disease accurate diagnosis and treatment system is assisted.

The invention provides a DSA-oriented X-ray coronary angiography image blood vessel segmentation method and system, and relates to the technical field of image processing and deep learning. The method comprises the following steps: firstly, acquiring a target segmentation image and a reference image based on X-ray coronary artery angiography video data, and forming a training sample by the reference image and the target segmentation image to construct a training data set; then constructing a segmentation network model; the segmentation network model comprises a main encoder, an auxiliary encoder, a plurality of up-sampling attention modules, a similarity attention module and a decoder; calculating a total loss function by using the segmentation mask of the target segmentation image, and optimizing the segmentation network model; and finally, inputting an X-ray coronary artery angiography image data set needing to be segmented into the trained segmentation network model to obtain a segmentation result. According to the method, an X-ray perspective image before radiography is compared with a target segmentation image, so that a neural network is helped to distinguish a foreground from a background, and blood vessels are accurately recognized.

A method of performing 3D vessel tree reconstruction includes providing segmented binary angiography images, applying a distance transform to the images, and generating distance transformed binary angiography images. The set of distance transformed binary angiography images are provided to a trained 3D vessel reconstruction machine learning model capable of reconstructing 3D vessels. The 3D vessel tree reconstruction machine learning model includes a multi-stage convolutional neural network comprising a multi-stage architecture with (i) a vessel centerline stage, and (ii) a radius reconstruction stage. Resultant 3D reconstructed vessel trees may be used in performing clinical assessment of coronary vessel health, and occlusion.

The invention discloses an intracranial artery stenosis functional evaluation method, device and equipment and a storage medium, and the method comprises the steps: processing an obtained intracranial artery angiography image, obtaining a two-dimensional center line and a two-dimensional contour line of a target blood vessel, and determining the three-dimensional diameter of the target blood vessel; determining the three-dimensional length of the target blood vessel according to the gray value distribution on the two-dimensional center line of the target blood vessel; image coordinates of a starting point and an ending point of the target blood vessel are determined according to the two-dimensional center line of the target blood vessel, and finally the average blood flow velocity in the target blood vessel is obtained; and based on the three-dimensional diameter, the three-dimensional length and the average blood flow velocity of the target blood vessel, completing intracranial artery stenosis functional evaluation. According to the method, the three-dimensional length of the target blood vessel is directly calculated from the gray value distribution on the two-dimensional center line in a more concise mode, the complex three-dimensional reconstruction step is avoided, the calculation process is simplified, and meanwhile the evaluation precision and reliability are improved.

In part, the disclosure relates to determining a stent deployment location and other parameters using blood vessel data. Stent deployment can be planned such that the amount of blood flow restored from stenting relative to an unstented vessel increases one or more metrics. An end user can specify one or more stent lengths, including a range of stent lengths. In turn, diagnostic tools can generate candidate virtual stents having lengths within the specified range suitable for placement relative to a vessel representation. Blood vessel distance values such as blood vessel diameter, radius, area values, chord values, or other cross-sectional, etc. its length are used to identify stent landing zones. These tools can use or supplement angiography data and / or be co-registered therewith. Optical imaging, ultrasound, angiography or other imaging modalities are used to generate the blood vessel data.

The invention discloses a foaming test device and system. According to the foaming test device, left / right atrial angiography tests can be taken into consideration at the same time, and when the foaming test device is switched into the left atrial angiography working mode, normal saline and a contrast agent can be automatically mixed to form a left atrial angiography mixed solution, and a left atrial angiography foaming solution is further formed; when the foaming test device is switched into a right atrial angiography working mode, normal saline, air and blood can be automatically mixed to form a right atrial angiography mixed solution, and a right atrial angiography foaming solution is further formed; the first injector and the second injector are matched with each other to perform reciprocating injection, so that the foaming efficiency of the left / right atrial angiography mixed liquid can be effectively improved, the time for forming the bubble liquid is shortened, automatic preparation of the left / right atrial angiography bubble liquid is quickly realized, meanwhile, the quality, preparation efficiency and consistency of the bubble liquid can be improved, and the production cost is reduced. And the accuracy of auxiliary screening is improved.

The invention discloses a collaborative optimization multi-task non-enhanced CT blood vessel segmentation device. The collaborative optimization multi-task non-enhanced CT blood vessel segmentation device comprises an image acquisition module, an image preprocessing module, a network construction module, a multi-task multi-scale joint loss function construction module, a branch loss construction module, a joint training module and a branch training module. According to the method, collaborative optimization of double tasks can be promoted while the parameter quantity is greatly reduced in an encoder sharing mode, branch parameters of the tasks are independently and finely adjusted, so that the branch parameters of the tasks are optimal, and the blood vessel form mask and the blood vessel structure mask can be effectively segmented from the non-enhanced CT image.

The invention discloses a guide wire advancing automatic monitoring method in a vascular intervention robot operation. The method comprises the following steps: S1, extracting an intervention path from a preoperative CT contrast image; s2, electromagnetic navigation equipment is placed in the operation space, coordinate values of the experimental model pasted with the electromagnetic sensor mark points in the magnetic field space and coordinate values of the corresponding image coordinate space are obtained, and a registration transformation matrix is obtained; s3, sequentially binding 3-5 electromagnetic sensors at the front end of the guide wire, and pushing the guide wire to an intervention starting point; s4, in the process that the guide wire advances to the intervention end point, electromagnetic navigation equipment is used for obtaining the position Pi, t of an electromagnetic sensor at the moment t, the image coordinate space position Pi, t is obtained based on the registration transformation matrix, and the front end form of the guide wire is fitted and fused and displayed in a reconstructed image; and S5, monitoring according to the intervention path and the relative position relation of the electromagnetic sensor. According to the invention, the moving state of the guide wire in the moving process can be positioned in real time, and moving deviation early warning is given.

The invention relates to the technical field of intelligent nursing, in particular to an intelligent indwelling needle work identification method and system based on an enhanced examination image, and during patient examination, a timing system identifies a photograph of an image (CT or MRI enhanced examination) in a patient folder (discovering angiography image features such as angiography color, texture and the like); a timing system is intelligently triggered to start timing; and when the needle withdrawal time is up, sending out a needle withdrawal notice of the corresponding patient. Therefore, needle withdrawal detection and reminding in CT or MRI enhanced examination in the imaging department can be intelligently realized, a head nurse does not need to carry out remaining needle withdrawal detection at a patient for a long time, the system can intelligently detect the enhanced examination condition and calculate the remaining time, the enhanced examination patient and nurses can be intelligently managed and served, and the system is convenient to use and popularize. The management efficiency of the imaging department enhanced examination patient is improved, excessive occupation of nursing resources is avoided, medical errors and infection risks are reduced, and the medical experience of the patient is improved.

The invention relates to a catheter pump position judgment device and method, a catheter pump, a ventricular assist system, a storage medium and equipment.The catheter pump position judgment device comprises a sampling module, a processing module and a judgment module, and the sampling module is configured to collect operation data of a driving motor of the catheter pump in the conveying process; the operation data at least comprises rotating speed data; the processing module is configured to determine a range value of the rotating speed data; the judgment module is configured to determine the position state of the catheter pump according to the comparison result of the range value and a preset threshold value. The method does not need to depend on an additional pressure sensor or multiple times of radiography, and can judge whether the catheter pump is located in the aorta, the transvalvular position or the left ventricle by collecting the rotating speed data of the driving motor and analyzing the range value of the rotating speed data.

The invention discloses an image processing and simulation analysis method for a carotid artery image, which comprises the following steps: firstly, acquiring a CTA image, observing and selecting, then importing the CTA image, adjusting the image contrast, generating a three-dimensional blood vessel model, extracting a carotid artery focus, and carrying out overall and local surface optimization processing on the model. The method comprises the steps that firstly, a model is built, then local surface cutting naming and grid division are conducted on the model, finally parameters are set, CFD hemodynamic simulation is conducted, a simulation result is subjected to visualization processing, and preoperative and postoperative data are compared and analyzed. According to the method, multi-modal imaging data of brain CT perfusion imaging, brain CT angiography and the like of a patient suffering from carotid artery stenosis can be collected to be used for reconstructing a three-dimensional model of the carotid artery stenosis, the carotid artery hemodynamic characteristics of the patient and the individual characteristics of the patient are analyzed in an auxiliary mode based on computational fluid mechanics, operation mode selection is optimized, and the three-dimensional model of the carotid artery stenosis is reconstructed. Cerebral ischemia reperfusion injury is avoided, clinical prognosis of a patient is improved, and life quality of the patient is improved.

The invention discloses a magnetic resonance 3DTOF angiography method based on variable echo time. The method comprises the following steps: step 1, respectively applying a magnetization transmission pulse and a saturation pulse to an imaging area; step 2, calculating the coding gradient lobe area of the imaging region to obtain the time of flow compensation, and determining the specific change condition of the echo time of the imaging region; 3, adopting a 3DTOF gradient echo sequence to obtain a final scanning sequence time sequence, and scanning to obtain magnetic resonance angiography data; and S4, performing filtering processing on the obtained magnetic resonance angiography data, performing reconstruction, generating a DICOM image, performing MIP three-dimensional reconstruction, and finally obtaining a three-dimensional image of the blood vessel. A variable echo time method is adopted, the closer to the center of a k space, the shorter the echo time is, the fewer signal errors are caused by blood flow, meanwhile, more sufficient time is provided for gradient compensation, artifacts caused by blood flow are reduced, and a higher-quality magnetic resonance blood vessel image is obtained.

The invention discloses a pulmonary artery CT angiography image generation method and device, equipment and a medium, and relates to the technical field of image processing, and the method comprises the steps: obtaining a chest CT plain scanning image of a target object; inputting the chest CT plain scanning image of the target object into the image generation model to obtain a pulmonary artery CT angiography image of the target object; wherein the image generation model is constructed based on a deep learning method, the pulmonary artery CT angiography image is generated by adopting the chest CT plain scanning image, an iodine-containing contrast agent does not need to be used, the cost is low, safety is achieved, and the application range is wide.

The accompanying angiographic catheter comprises an outer tube, a functional catheter and a fixed catheter are arranged in the outer tube in a penetrating mode, an injection connector is arranged at the near end of the functional catheter, and a sliding fixing part used for fixing a target instrument is arranged in the fixed catheter in a penetrating mode; the sliding fixing part comprises a sliding fixing tube, a control part and a fixing part connected to the far end of the control part, the sliding fixing tube is of a cylindrical structure or a tubular structure and penetrates through the fixing catheter, the control part penetrates through the sliding fixing tube, and the fixing part is of a circle structure defined by a thin line; the cross section direction of the outer pipe is of a foldable bending structure, and the bending structure is of an elastic structure which can be expanded and retracted. The device has the advantages of being easy and convenient to operate, accurate in radiography target spot, suitable for functional catheters and fixed catheters of various diameters, suitable for being used in cooperation with target instruments and shadow catheters of various models, and good in passing performance.