567 results about "Compute tomography" patented technology

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

A system includes an attenuation corrector configured to generate Computed Tomography-(CT-) based attenuation correction data from CT image data, a Positron Emission Tomography (PET) reconstructor configured to reconstruct first PET image data based on PET projection data and the CT-based attenuation correction data, an attenuation correction artifact mitigator configured to analyze the first PET image data for a presence of attenuation correction artifact, an inference engine configured to predict attenuation correction data based on non-attenuation corrected PET image data in response to the presence of attenuation correction artifact in the first PET image data, and an attenuation correction data updater configured to generate modified attenuation correction data based on the CT-based attenuation correction data and the predicted attenuation correction data. The PET reconstructor is further configured to reconstruct second PET image data based on the PET projection data and the modified attenuation correction data.

The invention discloses a microscopic crack dynamic in-situ characterization method, and relates to the technical field of crack in-situ detection. According to the method, a plurality of hardened cement paste bodies are soaked in a sulfate solution, the porosity development trend of the hardened cement paste bodies is obtained through a nuclear magnetic resonance test and a mercury injection test in the soaking process, a test sample and a test sample are prepared after soaking is finished, and the crack propagation length of the test sample is measured through a scanning electron microscope. According to the distance gradient between the sampling angle of the test sample and the exposed surface, space coordinates of crack cracking are converted into a time process along the center axis of the test sample, a delay microscope is established on the microscale, and the test sample is scanned by using X-ray computed tomography equipment in a matched mode; a crack propagation animation image is formed based on a crack form between adjacent X-ray tomography slices, and a crack development process is obtained by continuously capturing high-resolution images, so that omission of time information is avoided, and in-situ detection of microscopic cracks in a hardened cement paste sample is realized.

The invention relates to a computer-based method for gradual subdivision of large computer-aided graphic design (CAD) and part models using different principles to analyze production defects and their criticality levels, and to perform quality and life prediction of the final product.

The invention relates to the technical field of medical data processing, and discloses a non-invasive hemodynamic assessment system for coronary artery stenosis degree, comprising an image acquisition module used for acquiring a coronary artery computed tomography angiography image of a patient; the physiological signal acquisition module is used for synchronously acquiring physiological signals of a patient, and the physiological signals comprise electrocardiosignals and non-invasive arterial blood pressure signals; and the data processing module is used for preprocessing and segmenting the coronary artery computed tomography angiography image. Electrocardiogram and non-invasive arterial blood pressure signals are collected through the sensor, risks and complications caused by invasive examination of traditional coronary artery radiography are avoided, the problem that patients unsuitable for invasive examination such as contrast agent allergy and renal insufficiency are difficult to evaluate is solved, the pain of the patients is relieved, and the acceptability of the patients is improved.

The invention provides a tumor cell assisted positioning system and method based on a composite image, and relates to the technical field of medical image.The system comprises a data acquisition module used for synchronously acquiring radio frequency echo signals, ultrasonic images and computed tomography data of target biological tissue to generate an original fusion data set; the feature processing module is used for performing spatial registration and feature extraction on the original fusion data set to obtain a multi-dimensional feature set; and the identification imaging module is used for extracting a multi-band radio frequency echo signal intensity level value of the target area based on the multi-dimensional feature set, generating a tissue radio frequency characteristic diagram through logarithmic compression, and performing pixel-level fusion on the tissue radio frequency characteristic diagram and the synchronous ultrasonic image to generate a composite image. According to the method, the three-dimensional coordinates of the tumor boundary are determined through multi-modal data fusion and precise calibration in combination with anatomical structure constraints, and the accuracy of tumor cell positioning is improved.

X-ray systems, including apparatus, methods, and medium-encoded computer program products, to obtain computed tomography scan data (e.g., for limited angle computed tomography) include: a stationary X-ray source; and at least one detector configured to obtain limited angle computed tomography scan data of an object, the limited angle computed tomography scan data including projections of the X-rays after interaction with the object having been moved through an angular range with respect to at least one straight line between the stationary X-ray source and the at least one detector, and a conveyor system moves the object in a product handling line, a component of the conveyor system rotates the object around an axis that is normal or oblique to a travel direction of the conveyor system, and the scan data includes the projections of the X-rays after the interaction with the object having been rotated through the angular range around the axis.

A cross-regional and cross-view learning (C2RV) framework is provided for sparse-view reconstruction in cone-beam computed tomography (CBCT) by advantageously leveraging cross-region and cross-view feature learning to enhance representation of a point in 3D space before estimating an attenuation coefficient of the point. Specifically, multi-scale 3D volumetric representations (MS-3DV) are first introduced, where features are obtained by back-projecting multi-view features at different scales to the 3D space. Explicit MS-3DV enable cross-regional learning in the 3D space, providing richer information that helps better identify different internal anatomy structures. Hence, features of the point can be queried in a hybrid way, i.e. multi-scale voxel-aligned features from MS-3DV and multi-view pixel-aligned features from projections. Instead of considering queried features equally, scale-view cross-attention (SVC-Att) is used to adaptively learn aggregation weights by self-attention and cross-attention. Finally, multi-scale and multi-view features are aggregated to estimate the attenuation coefficient.

A computer-implemented method of providing plaque data (110) for a plaque deposit (120) in a vessel (130), is provided. The method includes: receiving (S110) computed tomography, CT, data (140) representing the vessel (130); generating (S120), from the CT data (140), a cross-sectional representation (150) of the vessel (130) at each of a plurality of positions (A-A′, B-B′) along the vessel; extracting (S130), from the cross-sectional representations, plaque data (110) comprising at least one measurement of the plaque deposit (120) at the plurality of positions along the vessel; and outputting (S140) a graphical representation of the plaque data (110).

The present disclosure provides a workpiece defect detection method, device and equipment and storage medium, relates to the technical field of defect detection, and can improve the accuracy of defect detection. The method comprises the following steps: acquiring a computed tomography (CT) image set corresponding to a first workpiece, the CT image set comprising a plurality of first CT images, different first CT images corresponding to different parts of the first workpiece; performing image cutting on each first CT image respectively to obtain a plurality of first cut images; in order to avoid the loss of feature information in the first CT image caused by the size change of the defect detection model on the first CT image with large size, the first CT image with large size is divided into a plurality of first cut images with small size, and then all the first cut images are input into a pre-established defect detection model, so that a defect detection result of the first workpiece can be obtained, and the defect detection model can accurately detect defects of the first workpiece based on the image features in the first CT image.

The invention relates to a medical imaging system (1) comprising a computer tomography gantry (20), a carriage (F), a main rail system (L) and a safety rail system (S), - wherein the computer tomography gantry (20) is movably mounted by means of the carriage (F) and the main rail system (L) such that a translational movement of the computer tomography gantry (20) can be carried out along the main rail system (L), - wherein the carriage (F) and the main rail system (L) are designed to transmit a drive force for the translational movement of the computed tomography gantry (20) from the carriage (F) to the main rail system (L) in a force-locking manner, - wherein the safety rail system (S) comprises at least one safety rail (SL) and at least one coupling element (KO), wherein the at least one safety rail (SL) is arranged on a floor section and is aligned in the same direction as the main rail system (L), wherein the coupling element (KO) is arranged on the carriage (F) or on the computed tomography gantry (20), wherein the coupling element (KO) is connected to the safety rail (SL) by means of a rear grip and / or in a form-fitting manner to prevent the computed tomography gantry (20) from tipping over.

The invention relates to a scatter correction method, a cone beam computed tomography (CBCT) system and a computer storage medium. The method comprises the following steps: performing cone beam CT imaging on a to-be-measured object to obtain a first group of projection data of the to-be-measured object, and performing reconstruction according to the first group of projection data to obtain a first volume image containing scattering artifacts; performing fan-beam CT imaging on a plurality of different heights of the to-be-measured object to obtain a plurality of groups of fan-beam projection data of the to-be-measured object, performing reconstruction according to the plurality of groups of fan-beam projection data to obtain a plurality of fan-beam slice images, and using the plurality of fan-beam slice images as reference images without scattering artifacts; extracting a plurality of cone beam slice images from the first volume image; performing image registration on the plurality of fan-beam slice images and the plurality of cone-beam slice images; and training a scattering correction model based on deep learning by using the plurality of registered image pairs to obtain a trained scattering correction model.

A method, apparatus, and computer-readable storage medium for controlling X-ray computed tomography (CT) imaging. A first set of projection data is acquired in a first CT scan of an object with a CT imaging apparatus. The first CT image data is reconstructed from the first set of projection data. X-ray tube current modulation information is determined for a second CT scan of the object, based on a noise propagation model between X-ray projection data and CT image data, and using, as inputs, the obtained first set of projection data, information indicating an imaging region-of-interest (ROI) for the second CT scan, and a target image quality level in the imaging ROI. The second CT scan of the object is obtained based on the obtained X-ray tube current modulation information.

A composite inspection system and method for aviation is provided. NDI system and method for the non-destructive inspection of aeronautical composite parts, comprising:-an ultrasound inspection device for generating raw inspection data of the composite part by using phased array, full focus method and phase coherent imaging; -a computed tomography module for obtaining information of the volume of the composite component; -data processing means of cloud shared resources configured to: receive and process raw inspection data from the ultrasound inspection device and information from the computed tomography module, and to apply an AI model created for identifying defects within the composite part, the AI model is trained using an inspection data set obtained from ultrasound and computed tomography inspections and is configured to classify defects of the composite part according to a list of defect types and to provide a classification output to make a decision.

The invention relates to the field of medical image processing, in particular to a cross-modal medical image alignment method based on anatomical feature one-dimensional distribution, and the method comprises the steps: obtaining a computed tomography (CT) image sequence and a magnetic resonance imaging (MRI) image sequence of a hip joint region of a patient; extracting a CT one-dimensional anatomical feature sequence representing the morphological change of the first side femoral head from the CT image sequence; extracting an MRI one-dimensional anatomical feature sequence representing the morphological change of the first side femoral head from the MRI image sequence; based on the CT one-dimensional anatomical feature sequence and the MRI one-dimensional anatomical feature sequence, a similarity alignment process is executed; wherein the similarity alignment process comprises the following steps: determining an optimal similarity alignment offset between a CT image sequence and an MRI image sequence, and aligning the MRI image sequence and the CT image sequence according to the optimal similarity alignment offset. According to the method, the cross-modal image alignment problem is subjected to dimensionality reduction into one-dimensional anatomical feature sequence matching, so that the precision, efficiency and robustness of registration are remarkably improved.

The invention provides a PET-CT (positron emission tomography-computed tomography) lung cancer image fusion segmentation method and system, and relates to the technical field of image segmentation. Carrying out convolution processing on the data through a convolutional neural network to obtain PET metabolic parameters and CT density value distribution data; performing cross-modal feature association on the PET feature map and the CT feature map to obtain an associated PET feature map and an associated CT feature map; segmenting the PET feature segmentation result and the CT feature segmentation result to obtain a PET feature segmentation result and a CT feature segmentation result; according to the segmentation result, combining PET metabolic parameters and CT density value distribution data to obtain fusion feature parameters; and carrying out conjoint analysis on the PET feature segmentation result, the CT feature segmentation result and the fusion feature parameters to obtain a PET-CT image fusion segmentation result of the early-stage tiny lesion of the lung cancer, thereby realizing accurate PET-CT image fusion segmentation of the early-stage tiny lesion of the lung cancer.

Systems and methods are provided for calibrating computed tomography (CT) system. In one example, a method for a computed tomography (CT) system comprises, during a calibration of the CT system, measuring a position of a detector element of a detector array of the CT system using a wire of a wire phantom coupled to a table of the CT system, during a rotational scan performed using the CT system; and during a subsequent scan performed on a subject using the CT system, applying the measured position of the detector element rather than a design target position of the detector element during reconstruction of an image from projection data acquired via the CT system; and displaying the reconstructed image on a display device of the CT system.

A self-supervised learning framework learns fine-grained features, high-level global features, and contextual relationship features of anatomical structures in medical images of a plurality of patients. The framework receives computed tomography three-dimensional volumes for a number of patients (“patient volumes”) and learns sub-volume relationships within the patient volumes through 3D sub-volume order prediction of correct positions in shuffled sub-volumes. The framework further learns fine-grained image features within the patient volumes through volume appearance recovery from a set of misplaced sub-volumes. Finally, the framework learns high-level global image features of anatomical structures in the patient volumes by maximizing an agreement between two spatially related views through a student-teacher network of the self-supervised learning framework.

The invention discloses a method for determining a hydrogen and water relative permeability curve of an underground hydrogen storage and related equipment, and relates to the field of underground space engineering.The method comprises the steps that a microscopic computed tomography image of a rock sample in the hydrogen flooding process is obtained; performing characterization unit analysis on the pore structure of the microscopic computed tomography image to construct a first pore structured grid model, the first pore structured grid model being used for characterizing the pore structure of the rock sample, and the first pore structured grid model comprising a coordinate system of the pore structure; by taking a coordinate system of the first pore structured grid model as a reference, mapping the wettability distribution data to the first pore structured grid model through the corresponding coordinate position to obtain a second pore structured grid model, the wettability distribution data is calculated along a three-phase contact line of rock, hydrogen and water extracted from the microscopic computed tomography image; and performing water-driven hydrogen two-phase flow simulation operation on the second pore structured grid model to obtain a hydrogen and water relative permeability curve of the hydrogen storage bank.

To improve accuracy of tube current modulation, responsiveness, and an accepted modulation width and the like at the time of kV switching.SOLUTION: A storage unit stores characteristics data showing a relation between a filament current value and a tube current output value upon switching between a first tube voltage and a second tube voltage lower than the first tube voltage. A setting unit is a unit that sets a tube current setting value for performing tube current modulation in parallel with switching between the first tube voltage and the second tube voltage. The setting unit sets the first tube current setting value at the time of application of either the first tube voltage or the second tube voltage as the reference tube voltage, and sets a second tube current setting value based on the characteristics data and the first tube current setting value at the time of application of the other tube voltage. In synchronization with the switching between the first tube voltage and the second tube voltage, a tube current control circuit switches between tube current feedback control based on the first tube current setting value at the time of application of the reference tube voltage, and tube current feedback control based on the second tube voltage setting value at the time of application of the other tune voltage.SELECTED DRAWING: Figure 6

Noise-reduced images of a subject are generated from x-ray projection data acquired from the subject using a computed tomography (“CT”) system. A neural network or other machine learning algorithm is trained to receive images reconstructed from the projection data as an input and to generate an output as noise-reduced images. The neural network or other machine learning algorithm is trained using a self-training procedure, in which the training data used to train the neural network or other machine learning algorithm are generated directly from the projection data acquired from the subject using data augmentation (e.g., random rotations of the projection data and / or noise insertion).

The invention discloses a pig brain deep brain stimulation DBS electrode planning and reconstruction system and method, and belongs to the technical field of medical image processing and neurosurgery path planning. An automatic registration module based on a magnetic resonance (MR) image and a pig brain standard map is used for accurately identifying a target brain region; the in-target optimal electrode trajectory planning module based on principal component analysis (PCA) and the electric field simulation module based on a Hodgkin-Huxley model are used for optimizing the treatment effect and predicting the tissue activation volume (VTA); and a multi-modal hybrid registration module based on postoperative computed tomography (CT) and preoperative MR images, which is used for accurately reconstructing the actual position of the electrode. According to the invention, through combination of automatic and data-driven planning and accurate postoperative verification, the accuracy, reliability and efficiency of electrode implantation in pig brain DBS research are significantly improved.

A method, system, device and storage medium for measuring and classifying CT-FFR, dividing lesions into different types according to coronary artery computed tomography angiography (CCTA) images, determining lesion lengths at lesion positions according to different lesion types, and determining the lesion lengths at the lesion positions according to different lesion types. Then, the measurement range of computed tomography fractional flow reserve is determined according to the focus lengths of different lesion types, a statistical matrix is established to calculate the numerical values of all computed tomography fractional flow reserve CT-FFR in the measurement range, and finally, hemodynamic classification is conducted on calculation results. According to the CT-FFR evaluation method, the accuracy and the stability of CT-FFR evaluation are enhanced by carrying out pathological change specificity classification on the pathological change in the haemodynamics sense, interference is reduced, and a more reliable solution is provided for clinicians and patients.

Computer processing techniques are described for augmenting computed tomography (CT) images with synthetic artifacts for artificial intelligence (AI) applications. According to an example, a computer-implemented method can include generating, by a system comprising a processor, synthetic artifact data corresponding to one or more CT image artifacts, wherein the synthetic artifact data comprises anatomy agnostic synthetic representations of the one or more CT image artifacts. The method further includes generating, by the system, augmented CT images comprising the one or more CT image artifacts using the synthetic artifact data. In one or more examples, the method can further include training, by the system, a medical image inferencing model to perform an inferencing task using the augmented CT images as training images.

The invention discloses a rapid prediction method for a particle thermal-mechanical coupling process, and the method comprises the steps: scanning energy carrier particles based on a microfocus computed tomography technology, and generating a three-dimensional digital model; importing the three-dimensional digital model into a multi-physics field simulation platform, and establishing a transient full-order model; based on the transient full-order model, generating a training data set and a test data set under different working conditions and corresponding temperature fields and stress fields; performing decomposition calculation on the training data set by using an intrinsic orthogonal decomposition POD reduced-order model to obtain a POD primary function and a spectral coefficient; establishing a mapping model from an input working condition to an output spectral coefficient based on a BP neural network; inputting the target working condition into the mapping model, and outputting a POD spectral coefficient; and on the basis of the calculation result and the POD spectral coefficient, reconstructing full-field physical quantity distribution at any moment under the target working condition through linear combination. The method solves the problems that traditional numerical simulation is low in calculation efficiency and large in resource consumption.

A deformation analysis device and a non-destructive analysis method for a secondary battery are capable of accurately diagnosing electrode deformation of the secondary battery using a computed tomography (CT) image. The deformation analysis method for the secondary battery includes performing computed tomography (CT) scanning on each of a first area and a second area of the secondary battery to acquire a first image corresponding to the first area and a second image corresponding to the second area, measuring a first angle from a winding front end to a winding distal end of an electrode of an electrode assembly in the first image and measuring a second angle from the winding front end to the winding distal end of the electrode of the electrode assembly in the second image, and determining that the electrode assembly is deformed if a difference value between the first angle and the second angle is greater than a reference value.

The invention discloses a PET-CT (positron emission tomography-computed tomography) radiopharmaceutical closed automatic dispensing and dosing machine which comprises a machine base and an injection bin fixedly mounted on the machine base, an air cylinder mechanism is fixedly mounted at the top of the back of the injection bin, and a closed box is fixedly mounted on one side of the injection bin. And a transmission mechanism pushed by the air cylinder mechanism is installed in the closed box in a sliding mode, a mechanical clamping mechanism is installed at the bottom of the transmission mechanism in a transmission mode, and an arm support is fixedly installed at the position, located at the placing opening, in the injection bin. According to the invention, the mechanical clamping mechanism repeatedly acts to clamp the disposable syringe, the problem of more pipeline consumables is solved by directly adopting the mechanical clamping mechanism to extract liquid medicine at a fixed point and inject the liquid medicine at a fixed point, meanwhile, the flowing time of the stock solution in the pipeline is also reduced, the disposable syringe directly injects the stock solution into the heparin cap, and the operation is simple and convenient. And the accuracy of the nuclide value is ensured to the greatest extent.

The present invention discloses a method for reconstructing cardiac interventional surgery scenes based on 3D ultrasound imaging rendering, comprising collecting preoperative computed tomography or magnetic resonance imaging images as prior information; preprocessing a real-time 3D ultrasound image sequence; voxelizing the images in combination with the prior information, converting the image data into a voxel representation in three-dimensional space, and creating an initial set of 3D Gaussian functions; optimizing the parameters of the initial Gaussian functions through an iterative optimization algorithm to minimize the difference between the reconstructed image and the actual image; alternately performing adaptive density control of the 3D Gaussian functions in combination with the shape constraints of the interventional catheter; and rendering tile by tile using a tile-based Gaussian sputtering technique to achieve real-time 3D scene reconstruction and display. The present invention combines prior information and the shape constraints of the interventional catheter, uses 3D Gaussian functions to represent and simulate various structures in the surgical scene, and achieves efficient rendering through the tile-based sputtering technique.

The invention relates to the technical field of cone-beam computed tomography, and discloses a scattering correction method and system for cone-beam computed tomography. The method comprises the following steps: acquiring original projection data and main scattering data acquired by a first flat panel detector; wherein the first flat panel detector is arranged in the emission direction of the radiation source; acquiring secondary scattering data acquired by a second flat panel detector; wherein the second flat panel detector is arranged in the scattering direction of the radiation source; determining target projection data based on the original projection data, the main scattering data and the auxiliary scattering data; the target projection data is used for cone beam computed tomography imaging. According to the method, scattering correction can be completed on the premise of not depending on special hardware, not additionally increasing radiation dose and not losing imaging view, and the imaging precision can be effectively improved.

The utility model belongs to the technical field of CT machine main bearing damping, and particularly relates to a high-speed CT machine main bearing damping device which comprises a limiting block. Connecting blocks are fixedly connected between the multiple limiting blocks. Inner rings of double-row angle bearings are fixedly connected to the side walls of the multiple connecting blocks. A connecting rod is slidably connected into the limiting block. The ends of the two connecting rods are fixedly connected with fixing blocks, and the other ends of the two connecting rods are fixedly connected with centrifugal blocks. A fixed convex block is fixedly connected to the side wall of the centrifugal block; clamping wheels are fixedly connected between the corresponding fixing protruding blocks, and the multiple clamping wheels are arranged corresponding to the size of the double-row angle bearing. According to the utility model, the plurality of clamping wheels are arranged to extrude the inner ring of the double-row angle bearing which rotates at a high speed, so that the inner ring and the outer ring of the double-row angle bearing are tightly attached, the vibration condition caused by a raceway structure error and a ball size error is further reduced, the negative influence of bearing vibration on CT imaging is favorably relieved, and the imaging quality of a CT machine is improved.