169 results about "Computer tomographic" patented technology

A defect prediction method based on a multi-feature parallel multi-stage neural network (MF-PMSNN), includes: obtaining a trajectory dataset, and preprocessing data of a defect of a workpiece in additive manufacturing (AM); building an MF-PMSNN, and evaluating an output classification result based on evaluation indicators; and performing real-time defect prediction, and deploying a trained MF-PMSNN model to a production environment. The present disclosure combines and effectively matches thermal imaging-based in-situ monitoring data and X-ray computed tomography (XCT)-based in-situ monitoring data to ensure temporal and spatial consistency between the thermal imaging-based in-situ monitoring data and the XCT-based in-situ monitoring data. In this way, a molten pool status and a pore of the workpiece can be captured more comprehensively. The MF-PMSNN is proposed to obtain a molten pool status and the porosity distribution in the data and perform defect prediction.

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 discloses a multi-scale performance evaluation and optimization method and device for well cementation cement stone, and relates to the technical field of oil exploitation, the well cementation cement stone is subjected to nanoindentation test and scanning electron microscope microstructure test, and a mechanical property-microstructure test result is obtained; performing electronic computer tomography and mercury injection test on the well cementation cement stone, and constructing a pore network three-dimensional model; performing triaxial mechanical property test, thermogravimetric analysis and thermal expansion test on the well cementation cement stone to obtain a thermal-mechanical coupling test result; performing X-ray diffraction and X-ray fluorescence spectrum analysis on the well cementation cement stone to obtain mineral phase information and element information of the cement stone; according to the method, a mechanical property-microstructure test result, topological parameters, a thermal-mechanical coupling test result, cement stone mineral phase information and element information are fused, an optimal design scheme for target well conditions is generated, multi-dimensional performance testing and characterization are achieved, and an accurate basis is provided for well cementation cement stone optimal design and quality control.

The invention relates to a computed tomography reconstruction method and system for a sparse angle of a cylindrical lithium battery, and the method comprises the steps: obtaining a cylindrical lithium battery projection image sequence as original projection data, and carrying out the diagonal interpolation processing of the original projection data, and generating complete sinogram data; performing Radon inverse transformation on the sinogram data to generate an initial iteration reconstruction image and taking the initial iteration reconstruction image as an iteration initial value, and performing iteration optimization by adopting a simultaneous algebraic reconstruction technology as a data fidelity term to obtain an optimized reconstruction image; designing a tangential smoothing filter, and filtering the optimized reconstructed image to suppress sparse artifacts; a radial weighting template is designed, and differential smoothing processing is carried out on the reconstructed image after sparse artifact suppression according to the distance between pixels and the center of the image; and performing reconstruction image optimization, sparse artifact suppression and differential smoothing processing circularly until a preset number of iterations is reached, thereby obtaining a final reconstruction image. According to the method disclosed by the invention, detail information of the image is effectively recovered while sparse artifacts are suppressed.

The present invention discloses a method for generating a channel spectrum response difference correction table and a channel spacing correction table, a computer program, a computer readable storage medium and a computer tomography device. The method for generating a channel spectrum response difference correction table and a channel spacing correction table includes using parallel projection P paral (k, r) determines the channel position corresponding to the center of the phantom, and calculates the simulated parallel projection and the ideal projection P accordingly sim (k, r), and finally according to the projection difference diff(k, l, r) and the simulated ideal projection P sim (k, r), calculate the channel spectrum response difference correction table t CCR (k, l) and channel spacing correction table t spacing The method, computer program, computer-readable storage medium, and computer tomography device of the present invention for generating a channel spectrum response difference correction table and a channel spacing correction table are not affected by residual beam hardening and can effectively generate ideal projections.

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.

A processing device for obtaining gap-filler sinogram information is adapted to obtain first sinogram information associated with a first spectral parameter, and second sinogram information associated with a second spectral parameter, and is adapted to obtain the gap-filler sinogram information which is associated with the second spectral parameter and which fills the one gap in the second sinogram information, on the basis of the first sinogram information and the second sinogram information. A computer program, a method, and a computer implementation are also described.

A radiation source mechanism is provided, including: a body and a first support assembly installed at a lower end of the body to support the lower end of the body to smoothly rotate around the first axis relative to a first working surface below the first support assembly . A part of the first support assembly has a disengagement state of being disengaged from the first working surface, and a contact state of being in contact with the first working surface in a process of the body disengaging from a closed position. A computer tomography device is further provided, including a machine frame, a detector mechanism and a radiation source mechanism, the radiation source mechanism is movable between a closed position of shielding the detector mechanism and an open position of exposing the detector mechanism.

The invention discloses a small sample abdomen multi-organ image segmentation method based on a prototype network and cross attention. The method comprises the following steps: firstly, preprocessing an abdominal computed tomography (CT) image, and completing resampling and intensity normalization; dividing the preprocessed image into a support set and a query set, and constructing a small sample segmentation task; a support image and a query image are input into a deep learning network model, a cross attention module is introduced into a multi-layer structure of an encoder to explicitly model foreground, background and boundary regions, interaction and fusion among different level features are enhanced, and pixel-by-pixel matching and distinguishing of support prototype and query features are realized in combination with a double-branch contrast learning structure. According to the method, a cross attention mechanism is introduced into multiple layers of the encoder, the correlation and boundary expression ability between the features are effectively enhanced, the discrimination and robustness of the model under the small sample condition are further improved through a double-branch contrast learning structure, and therefore under the condition that labeling data is limited, the accuracy and robustness of the model are improved. And efficient and automatic segmentation of multiple organs of the abdomen is realized.

An electrical connection material having a contrast agent infused within, and related systems and methods of detection thereof, are disclosed herein. Computer electronics such as printed circuit boards have electronic components attached to a substrate by an electrical connection material. The electrical connection material has at least one component infused with a contrast agent. The contrast agent is of a concentration to provide a visual distinction or highlight when imaged by an inspection system, such as an x-ray system of computed tomography (CT) scanning system. The presence of the contrast agent demonstrates that not all of residual electrical connection material has been cleaned away from the printed circuit board after manufacture.

The present invention discloses a method, system, device, and medium for selecting pseudo-labels for computer tomography image segmentation based on a multimodal model. The method includes: using Totalsegmentator to label unlabeled anatomical organ types in the original CT image dataset to obtain noisy pseudo-labels; degrading the manually labeled labels; forming a training set with the original CT images, manually labeled labels, and degraded manually labeled labels, and inputting it into the image encoder of BiomedCLIP to extract visual features; initializing the prompt words and inputting them into the text encoder of BiomedCLIP to extract text features; constructing a joint loss function to supervise and train the prompt words, superimposing the category information of the noisy pseudo-labels on the trained prompt words, and then inputting them into the text encoder to obtain text features, and inputting the original CT images and noisy pseudo-labels after transformation into the image encoder to extract visual features; judging the quality of the noisy pseudo-labels based on the similarity between the text features and visual features. The present invention improves the efficiency of screening high-quality pseudo-labels.

1. Name of the Design Product: Computer Tomography Scanner Screen. 2. Use of the Design Product: For operating the page and displaying tomographic scan images. 3. Design Key Points of the Design Product: Lies in the shape. 4. Picture or Photograph that Best Illustrates the Design Key Points: Perspective View 1.

An X-ray computer tomographic apparatus according to an embodiment includes a gantry, a bearing, a driver, and a stand. The gantry rotatably supports a rotation unit on which an X-ray tube is mounted. The bearing is connected to the gantry and includes an inner ring with a plurality of teeth on an inner circumferential surface and an outer ring that is connected to an outer circumference of the inner ring via a plurality of rolling elements. The driver causes the inner ring to rotate via the teeth. The driver is mounted on the stand and the stand is connected to the outer ring and is provided on a floor surface.

Provided herein are methods, apparatuses, computer program products, and systems for generating textured surfaces using computed tomography. One method can include generating a computer data structure representing three-dimensional surfaces of a physical object from (i) segmentation of volumetric data reconstructed from radiographs, (ii) position information and orientation information of the physical object, and (iii) size information of the physical object; adding texture data to the three-dimensional surfaces represented in the computer data structure using images captured by at least one optical camera, the images being of the physical object at different rotational orientations, by projecting data of the images onto the three-dimensional surfaces using the size information, the position information, and the orientation information of the physical object and predetermined position, location, and parameters of the at least one optical camera; and providing the computer data structure for processing of the three-dimensional surfaces.

The application provides a sample bin offset determination method and device and a computer tomography scanning equipment. The method comprises the following steps: obtaining a plurality of projection images of the sample bin when the sample bin is located on a preset bed; the plurality of projection images comprise a first projection image, a second projection image and a plurality of third projection images obtained by scanning the sample bin when the scanning equipment is located at a first angle, a second angle and a plurality of third angles, and the angle difference between the first angle and the second angle is 180°; determining a first offset and an offset angle of the sample bin based on the first projection image, the second projection image and the plurality of third projection images; and / or obtaining a plurality of local projection images of the sample bin when the scanning system is located at a preset angle; and determining a second offset of the sample bin based on the plurality of local projection images. The first offset and the second offset can be determined without reconstructing the first projection image, the second projection image and the third projection image, the determination efficiency of the sample bin offset is improved, and the service life of the scanning system is improved.

The application discloses a UHPC anti-bending test initial cracking strength determination method and system based on an image method, and belongs to the technical field of mechanical property testing, and comprises the following steps: spraying random speckles on the surface of a UHPC test piece, pre-embedding an optical fiber Bragg grating sensor array in the test piece, and performing X-ray computed tomography on test pieces in the same batch to establish a fiber-matrix interface transition zone damage evolution model; when three-point anti-bending loading is performed on the test piece, surface speckle images are collected in real time, the DIC algorithm is used to calculate the full-field strain distribution, and the surface initial cracking time and the strain concentration area coordinates are determined in combination with a criterion; meanwhile, the wavelength drift of the optical fiber is monitored, the internal initial cracking time is determined by calculating the coefficient of variation; the interface debonding rate and the interface initial cracking point time are calculated according to the model and the real-time interface stress; a multi-source initial cracking characteristic data set containing the surface strain, the internal strain anomaly and the interface debonding rate is constructed, the weight coefficients are obtained by inputting the neural network model, and the initial cracking anti-bending strength is calculated by weighted summation.

The application discloses a digital rock reconstruction method and device, equipment and medium, and relates to the field of digital rock reconstruction, and comprises the following steps: extracting micro-texture features in two-dimensional scanning electron microscope image data to obtain corresponding texture feature vectors, and performing integration processing on the texture feature vectors to obtain target texture feature vectors; extracting structural feature information from three-dimensional computer tomography image data, splicing the target texture feature vectors and target noise, and mapping the spliced vectors into target style vectors; fusing the structural feature information and the target style vectors to obtain a three-dimensional feature map, capturing geometric feature responses corresponding to local geometric structures of rocks in the three-dimensional feature map, and generating spatial modulation weights matched with the local geometric structures of the rocks; performing voxel-by-voxel weighted modulation on the three-dimensional feature map according to the spatial modulation weights to obtain a target feature map, and generating target three-dimensional digital rock body data based on the target feature map.

A computer-implemented method (100) for extending a training data set for machine learning, the method comprising: - Providing a data set of an object to be examined, wherein the data set has coordinate values and measured values for each coordinate, wherein the data set is derived from an image acquisition method of a computer tomograph, - Identifying an anomaly in a section of the data set that corresponds to a sub-area of the object to be examined, - Classifying the anomaly into a number of predefined classification classes using a first machine learning model trained with a first training data set, - Determining a difference value of the anomaly compared to the trained first machine learning model on the basis of a combination of novelty measures by means of a second machine learning model trained with a second training data set, wherein the first training data set and the second training data set are created by a respective random selection of data sets from an overall training data set, and - Supplementing the first training data set with data concerning the identified anomaly if the difference value is above a first predefined threshold.

The invention discloses a CT sparse angle reconstruction method based on deep learning, and relates to the technical field of industrial image processing and computed tomography, and the method comprises the following steps: S1, data classification and preprocessing: obtaining original CT sinogram data, segmenting the original CT sinogram data through a 128 * 128 pixel sliding window with the overlapping rate of 15%, and obtaining a CT sparse angle; carrying out classification through a three-level classification index system to obtain 12 types of small sinogram blocks with labels; s2, sine domain deep learning data enhancement training: performing interval sampling deletion operation on the classified 12 types of sinogram small blocks, constructing a sample triple, establishing a sine domain generation network of a double-discrimination network structure, and taking the sample triple as training data to obtain a sine domain completion model after training is completed; s3, training and repairing the projection domain repair graph based on the U-Net network; s4, carrying out artifact removal processing on the reconstructed image; the method effectively optimizes the image quality.

The application belongs to the field of industrial CT image processing, and discloses a kind of extremely sparse angle CT reconstruction method of multi-stage processing neural network based on dual-domain joint mode, for solving the problem that traditional reconstruction method cannot reconstruct high-quality CT image under extremely sparse sampling view angle, the reconstruction method of the application first carries out extremely sparse sampling to the full-angle projection data A obtained by computer tomography system acquisition and obtains projection data B;Then projection data B is input to the trained sinusoidal domain network after interpolation to obtain optimized projection data C;Then projection data C is primarily reconstructed into CT image D using fast iterative soft threshold shrinkage algorithm (FISTA);Finally, a trained image domain network is used to optimize image D to obtain high-quality CT image E.The method of the application can reconstruct CT image under extremely sparse angle sampling, and the reconstructed CT image has higher structural integrity and clarity.

The embodiment of the disclosure relates to a computer tomography image-based acetabular cup prediction system and method, and relates to the technical field of medical image analysis, and the user information and the computer tomography image of a target user are acquired; the computer tomography image is subjected to image segmentation, and an acetabular region of interest is acquired; the corresponding Hounsfield unit value and the acetabular center coordinates of each pixel coordinate are acquired based on the acetabular region of interest, the screening area of the acetabular cup is determined based on the acetabular center coordinates and the received screening conditions, the Hounsfield unit value corresponding to each pixel coordinate is processed based on a preset dynamic threshold method, and the cortical bone Hounsfield unit average value and the cancellous bone Hounsfield unit average value of the screening area are obtained; prediction analysis is carried out based on the user information and the cortical bone Hounsfield unit average value and the cancellous bone Hounsfield unit average value of the screening area, and a prediction result is generated, so that more objective and accurate acetabular cup stability evaluation can be provided based on the prediction result.

The present invention pertains to a method and system for image reconstruction for computer tomography, in particular for reducing beam hardening effects, wherein image reconstruction is performed based on the generalized Lambert-Beer law for poly-chromatic sources.

The application provides an industrial computer tomography method, a control terminal and a scanning system. The method comprises the following steps: acquiring a first projection image and a second projection image when a rotating table is at a first rotation angle and a second rotation angle, and determining a first pixel value and a second pixel value; determining a first X distance and a first Y distance according to the first pixel value, the second pixel value, a number of projection image pixel matrix rows, a detector distance, a rotating table distance and a unit pixel size; determining a second X distance and a second Y distance of multiple subsequent rotations according to the first X distance, the first Y distance and a rotation number; controlling the rotating table to perform a first rotation and controlling a machine table to scan according to the first X distance and the first Y distance; and controlling the rotating table to perform each subsequent rotation and controlling the machine table to scan based on the second X distance and the second Y distance corresponding to each rotation in the multiple subsequent rotations. The application efficiently and conveniently obtains a clear scanning image by pre-calculating the rotating table movement distance and controlling the rotating table movement.

The present application relates to the technical field of image data processing, and discloses a corn kernel and embryo volume high-throughput visual analysis method and system based on deep learning, which can obtain at least one folder according to a file input path set by a user, and each folder includes a gray scale sequence obtained by computer tomography (CT) slicing of corn kernel groups in a horizontal direction. Based on a parallel pipeline processing mode, a set batch size and a deep learning segmentation model, the gray scale sequence in the folder is preprocessed and image segmented to obtain kernel binary segmentation mask sequences and embryo binary segmentation mask sequences, and then the kernel quantity and the volume related data of the kernel and the embryo are determined, and the related images and data are visually output. The present application can complete the processing of the high-throughput data of the CT slice sequence of the corn kernel, and the nondestructive measurement and visual display of the volume related data of the kernel and the embryo, and effectively improve the measurement efficiency.

This invention discloses a non-destructive testing method and system for via filling in glass substrates based on CT scanning, belonging to the field of advanced packaging inspection technology. The key technical points are: performing X-ray computed tomography (CT) scanning on a glass substrate with metal-filled vias to acquire a two-dimensional projection image sequence; performing three-dimensional reconstruction on the two-dimensional projection image sequence to generate three-dimensional volume data of the internal structure of the metal-filled vias; performing image segmentation on the three-dimensional volume data to distinguish the glass substrate, metal filler, and internal defect regions to obtain the segmentation result; and calculating three-dimensional quantitative parameters characterizing the filling quality of the metal-filled vias based on the segmentation result, including at least one of three-dimensional fill rate, three-dimensional void rate, or filling uniformity coefficient. This invention achieves a comprehensive leap in detection capabilities in spatial dimension, evaluation mode, and detection essence, laying a solid technical foundation for the research and development iteration and mass production quality control of advanced packaging glass substrates.

The application relates to a computer tomography tube current modulation method, device and electronic device, wherein the computer tomography tube current modulation method comprises the following steps: acquiring a first moving speed of a scanning bed and a first scanning tube current, wherein the first scanning tube current is a tube current applied by the scanning bed at the first moving speed; acquiring a second moving speed of the scanning bed; and generating a second scanning tube current according to the first moving speed, the second moving speed and the first scanning tube current, wherein the second scanning tube current is a tube current applied by the scanning bed at the second moving speed. Through the application, a computer device can modulate the first scanning tube current applied by the scanning bed at the first moving speed into the second scanning tube current applied at the second moving speed, thereby solving the problem that there is no scanning tube current modulation method for computer tomography under a variable speed state in the prior art.