192 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 relates to a device 1 for the linear movement of a gantry of a computed tomography scanner. The device comprises a carriage 2 with a first part 4 of a measuring system and a guide rail 5 received in a recess 11 in the floor 12. The guide rail 5 has a second part 8 of a measuring system in a recess 7, the recess 7 being covered by a cover plate 9. The carriage 2 has a friction roller 3 which rolls on a guide shaft 10 projecting from the floor 12 and received in a groove of the guide rail for linear movement.

The invention belongs to the field of industrial CT image processing, discloses an extremely sparse angle CT reconstruction method of a multi-stage processing neural network based on a double-domain joint mode, and is used for solving the problem that a traditional reconstruction method cannot reconstruct a high-quality CT image at an extremely sparse sampling view angle. The reconstruction method comprises the following steps: firstly, carrying out extremely sparse sampling on full-angle projection data A acquired by a computed tomography system to obtain projection data B; interpolating the projection data B, and inputting the interpolated projection data B into the trained sine domain network to obtain optimized projection data C; secondly, preliminarily reconstructing the projection data C into a CT image D by using a fast iterative soft threshold shrinkage algorithm (FISTA); and finally, optimizing the image D through a trained image domain network to obtain a high-quality CT image E. According to the method, the CT image under extremely sparse angle sampling can be reconstructed, and the reconstructed CT image has higher structural integrity and definition.

An exemplary non-contrast computer tomography (CT) system and method are disclosed employing AI-denoiser and dynamic range correction that remaps pixels on a portion of the brain to preserve stroke or net water uptake information in the image to accurately generate a whole brain image that readily show changes in net water uptake that could used to identify earlier stage or outset of a stroke or like injuries or conditions. The exemplary system and method can additionally provide quantification of net water uptake, e.g., for stroke diagnosis, in addition to visualization. The AI-denoiser and dynamic range correction may operate with an image inhomogeneity correction operation that can correct for subtle residual artifacts (e.g., cupping artifacts) that increases with skull thickness.

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.

A computer tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (116) configured to detect x-ray radiation and generate a signal indicative thereof, and a reconstructor (118) configured to reconstruct the signal and generate sequential spares time line perfusion volumetric image data. The computer tomography scanner further includes a processor (132) configured to process the sequential spares time line perfusion volumetric image data using a trained neural network of a perfusion data enhancing module (136) to produce sequential dense time line perfusion volumetric image data.

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.

Computed tomography system comprising an examination area (2) and a static gantry (3), comprising a base frame (4), an annular detector arrangement (6) with detector elements and an annular X-ray source arrangement (5) with X-ray sources, each of which at least partially encloses the examination area (2) and has a substantially common axial direction, wherein the detector arrangement (6) and the X-ray source arrangement (5) are attached or attachable to the base frame (4) from different sides in axial direction, wherein the computed tomography system comprises at least one guide rail (10) which runs essentially in the axial direction and with which an offset of the detector arrangement (6), in particular of individual detector segments (61) of the detector arrangement (6), can be guided in the axial direction, wherein the computed tomography system comprises at least one eccentric disc (45) for installing the detector arrangement (6) in conjunction with at least one guide rail (10).

The invention discloses an integrated large thin-wall die casting defect infiltration assessment method and device, and relates to the technical field of die casting process optimizing.The method comprises the steps that a micro-X-ray computed tomography technology is used for imaging an impregnated die casting sample, and two-dimensional and three-dimensional CT images of the sample are obtained, the die casting sample is obtained through vacuum pressure impregnation treatment; segmenting the CT image through a machine learning image segmentation technology, and identifying an aluminum alloy, an air pore and an infiltration sealant in the die casting; measuring the effective atomic number of the sealant material through a dual-energy X-ray quantitative analysis technology so as to identify the infiltrated sealing material; and evaluating the infiltrating effect of the die casting defect.

The present invention relates to systems, methods and computer programs for training and using a machine learning model to generate synthetic contrast-enhanced computed tomography images with the help of magnetic resonance contrast agents.

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 invention relates to the technical field of microscopic characterization of pavement material pore structures, in particular to a pore network model construction method of asphalt pavement pores, which comprises the following steps: firstly, measuring the three-dimensional size of a pavement structure material sample, and setting scanning parameters of computed tomography equipment according to the data of the three-dimensional size to obtain a two-dimensional slice image; then processing the two-dimensional slice image through image processing so as to improve the image quality of the two-dimensional slice image, obtaining a three-dimensional image through three-dimensional modeling, and then identifying and separating the three-dimensional image by adopting an adaptive threshold segmentation algorithm so as to reserve a pore structure in the three-dimensional image; and finally, identifying and retaining mutually communicated pores in the three-dimensional image through connectivity analysis, namely, providing clear image materials for the construction of a subsequent pore network structure through image processing, threshold segmentation and connectivity analysis, and finally, obtaining the pore network structure through a maximum sphere algorithm. Therefore, the pore network structure model of the communicated pores can be accurately and completely constructed.

The invention discloses a circuit board back drilling STUB detection method and system, and the method comprises the following steps: obtaining a cross section image set generated by scanning a to-be-detected circuit board through employing an electronic computer tomography technology, and the cross section image set comprises a plurality of cross section images which are sequentially arranged according to the height of a cross section; analyzing the cross section images in the image set in sequence, determining the position of the end of the remaining Stub, and recording the position as a first position; determining the position of a layer which is electrically connected with the residual Stub and is closest to the end of the residual Stub, and recording the position as a second position; and calculating the distance between the first position and the second position to determine whether the back drilling hole Stub is qualified or not. The position relation between the Stub in the back drilling hole and the signal layer is accurately detected through the tomography technology and the artificial intelligence technology, an expensive camera is not needed, and a finished circuit board does not need to be damaged.

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 present invention relates to an apparatus (10) for correcting computer tomography (“CT”) X-ray data acquired at high relative pitch, the apparatus comprising: an input unit (20); a processing unit (30); and an output unit (40). The input unit is configured to provide the processing unit with CT X-ray data of a body part of a person acquired at high relative pitch. The processing unit is configured to determine CT slice reconstruction data of the body part of the person with no or reduced high relative pitch operation reconstruction artefacts using a machine learning algorithm. The machine learning algorithm was trained on the basis of CT slice reconstruction data, and wherein the CT slice reconstruction data comprised first CT slice reconstruction data with high relative pitch reconstruction artefacts and comprised second CT slice reconstruction data with less, less severe, or no high relative pitch reconstruction artefacts. The output unit is configured to output the CT slice reconstruction data of the body part of the person.

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.