833 results about "Tomographic image" patented technology

Improved novel 3D imaging and reconstruction methods for quantitative and accurate 3D imaging used in multiple industries, segments, and applications within each. Improvement includes methods and hardware configured to improve performance and lower radiation and improve accessibility. Using simplified system matrix and datasets of little scatter interference in tomographic image acquisition and reconstruction, in some cases, using time continuity and spatial continuity to generate parameter data for substances within VOI for a number of key applications in medical and non-medical fields.

The invention discloses a welding detection method and application thereof in welding monitoring of an LNG (liquefied natural gas) storage tank. According to the scheme, 3D point cloud data, surface image data and cross-sectional image data acquired from a welding seam by combining a 3D laser scanner, an industrial camera and an ultrasonic phased array probe are subjected to multi-modal data fusion; the fusion data is input into a trained defect recognition model, so that the weld joint can have a more perfect data source in quality detection classification, and a reliable data basis is provided for weld joint detection; besides, according to the scheme, a welding seam model based on digital twinning is constructed on the basis of fusion data, in this way, when the welding seam has defects caused by abnormal welding, the welding seam model constructed by the digital twinning model can be used for positioning the defects and outputting local welding seam model data with the defects, and meanwhile, by combining a detection result, the defect detection accuracy is improved. And more visual and reliable detection feedback can be provided for background management personnel.

The invention discloses a fire-fighting non-destructive detection method based on multi-modal detection, and relates to the technical field of fire-fighting engineering, and the method comprises the following steps: carrying out the rapid scanning of fire-fighting equipment through an ultrasonic array, analyzing and positioning a suspected defect region based on a time domain reflection signal, and generating a three-dimensional coordinate mapping model; and performing multi-angle X-ray projection acquisition on the positioning area, and generating an internal structure cross-sectional image through a three-dimensional reconstruction algorithm. According to the fire-fighting non-destructive detection method provided by the invention, by integrating various technical means such as ultrasonic array scanning, X-ray tomography, thermal imaging analysis and microwave sealing detection, comprehensive and accurate identification of potential defects of fire-fighting equipment is realized, and the dimensionality and precision of defect detection are improved; the structural integrity of the equipment is kept to the maximum extent, extra risks caused by destructive detection are avoided, and the reliability and accuracy of detection are improved through a data fusion algorithm.

An AI image diagnosis apparatus into which three-dimensional tooth image data captured by a dental OCT device is inputted and which analyzes the inputted three-dimensional tooth image data, comprising: a model executor that sequentially inputs two-dimensional tomographic image data constituting the diagnostic target three-dimensional tooth image data into a trained model, to thereby obtain, as an execution processing result of the trained model, lesion information data, which is data related to a part identified as a characteristic part such as a lesion in the inputted tomographic image data, for each piece of tomographic image data in the three-dimensional tooth image data, and detect lesions from the inputted three-dimensional tooth image data using the obtained lesion information data, wherein the trained model is constructed by training of three-dimensional tooth image data of multiple examinees captured by a dental OCT device in the past.

The invention discloses a method for detecting internal body defects of a crystal. The method comprises the following steps: S1, taking the detected crystal with the internal body defects; s2, irradiating from the side surface of the crystal by using a line light source so as to obtain a crystal tomographic image containing scattered light of body defects in the crystal; correcting the size of the crystal chromatography image; s3, performing time sequence analysis and processing on the corrected crystal chromatography image, and highlighting the characteristics of defect points; s4, in combination with the internal defect position of the known crystal and the detection time sequence signal of the position, training the RNN model to obtain a crystal internal defect detection model; s5, repeatedly training to obtain a final crystal internal defect detection model; and S6, inputting the time sequence signal of the crystal to be detected, which is obtained by processing in the steps S2 to S3, into the final model, so as to obtain the position information of the internal defect. And S7, filtering out false defects according to the representation of the defects on the plane. The method solves the problems of complex identification process and low identification efficiency of the internal defect of the crystal at present.

The embodiment of the invention discloses a root canal feature form extraction method, device and equipment based on CBCT data and a medium, and belongs to the technical field of stomatology and medical image.The method comprises the steps that filtering denoising and dental pulp segmentation processing are carried out on a CBCT image, and surface drawing three-dimensional reconstruction is carried out on a tooth root canal to generate a three-dimensional model; based on regular point cloud data in the three-dimensional model, adopting a point-surface method to realize curved surface reconstruction, and generating a root canal entity model; and performing coordinate alignment processing on the root canal solid model in a three-dimensional space, and measuring the length of the root canal, the direction of a root canal orifice, the curvature of the root canal and the depth of dental pulp by fitting the approximate center line of the root canal. According to the method, the corresponding three-dimensional model is quickly reconstructed based on the CBCT cross-sectional image obtained by scanning the teeth of the patient, and related data such as the dental pulp depth and the size of the root canal orifice are acquired from the three-dimensional model of the teeth so as to assist the oral surgery.

The invention discloses a DBT reconstruction method based on three-dimensional coordinate displacement, a medium and electronic equipment, and the method comprises the steps: firstly obtaining multi-angle X-ray projection data, and carrying out the preprocessing of the X-ray projection data; then, the preprocessed projection data is corrected in a three-dimensional coordinate system; after interpolation processing is conducted on the projection data, an autoregressive source moving average separation method is adopted for processing, and a cross-sectional image of any depth is obtained; and finally, carrying out image enhancement on the cross-sectional image. According to the method, DBT reconstruction is carried out through three-dimensional displacement by utilizing multi-angle X-ray projection, and the structure in the reconstruction layer is effectively focused through multi-angle projection displacement correction and magnification standardization, so that the structure in the reconstruction layer is clearer, artifacts in the reconstruction layer are reduced, the contrast of the image of the reconstruction layer is improved, and observation of breast lesions is more facilitated.

The invention discloses a multi-fluorescence imaging device capable of adjusting visual field area and optical tomography, which comprises a multi-wavelength light source, the multi-wavelength light source comprises a wavelength variable light source or a light source array, and the wavelength variable light source and the light source array are independent and can realize multi-wavelength output. The optical fibers are mounted on the multi-wavelength light source and transmit light according to different wavelengths, the optical fibers are circularly placed at certain intervals, and the diameter of the optical fibers is changed according to applied voltage and applied temperature to form an annular magnetic disk. The wavelength of the exciting light can be freely adjusted by utilizing a multi-wavelength light source or realizing the exciting light with variable wavelength, a plurality of contrast agents are excited to obtain a multi-wavelength fluorescence image, the characteristics of the plurality of contrast agents can be simultaneously observed, and the universal fluorescence imaging device is widely applied to multi-antigen analysis and treatment. The method can be used for measuring lesion light cross-sectional images and has the advantage of analyzing lesion cross-sectional structures in real time.

The invention discloses a control method, system and equipment of an intelligent throat surgical robot and a medium. According to the scheme, a three-dimensional tissue model is constructed according to visual image data, laser imaging data and tomography image data; determining lesion data according to the historical lesion data and the tomography image data, superposing the lesion data and the three-dimensional tissue model, and determining a target operation model; determining prediction trajectory data according to the tomography image data and the physiological motion data; planning a path according to the predicted trajectory data, the target operation model and the constraint condition, and controlling the operation robot to perform an operation according to the planned path; through multi-modal data fusion, a three-dimensional tissue model is established to improve the surgical accuracy; the data are analyzed to determine the lesion direction, the motion trail of the organ tissue is predicted, the three-dimensional tissue model is integrated for path planning, the interference of physiological activities of the organ tissue on the operation is avoided, and the safety of the operation is improved. The embodiment of the invention can be widely applied to the field of intelligent robots.

The invention relates to an X-ray multi-contrast imaging image quality enhancement method based on feature prior learning, and belongs to the technical field of deep learning and X-ray multi-contrast imaging. The method comprises the following steps: acquiring low-image-quality absorption, phase and dark field contrast computed tomography images and corresponding normal-image-quality three-contrast computed tomography images by using an X-ray grating multi-contrast imaging system, and constructing a training data set; constructing a conditional generative adversarial network image quality enhancement model based on feature prior learning; using the training data set to train a conditional generative adversarial network image quality enhancement model; and using the trained conditional generative adversarial network image quality enhancement model to enhance the obtained low-image-quality absorption, phase and dark field contrast computed tomography image. According to the invention, the low-quality multi-contrast images obtained under different conditions can be recovered and enhanced, and the application of the X-ray multi-contrast imaging technology in biomedical clinic and frontier material science is promoted.

The invention provides a blasting hidden danger troubleshooting system based on unmanned aerial vehicle tomography, and the system comprises a data collection assembly which carries out the three-dimensional modeling and tomography of a blasting target region through an unmanned aerial vehicle carrying a laser radar or a multispectral sensor, and generates high-resolution point cloud data and a tomographic image; the risk assessment assembly analyzes the point cloud data and the cross-sectional image, automatically identifies blasting hidden dangers such as rock mass looseness, crack propagation and geological faults, performs risk assessment in combination with a geomechanical model, and generates a hidden danger distribution diagram and a risk level report; the dynamic early warning assembly monitors a blasting key area for a long time, the unmanned aerial vehicle scans a blasting target area regularly, and a three-dimensional model and hidden danger data are updated in real time; and when hidden danger change is detected, an early warning mechanism is automatically triggered, and early warning information is pushed through a visual interface or a mobile terminal. The invention provides an intelligent and efficient blasting safety monitoring and early warning system, and the safety of blasting engineering is improved.

The invention discloses a postoperative craniocerebral electrode trajectory determination method, device, system and equipment and a medium. The method comprises the following steps: acquiring a plurality of craniocerebral cross-sectional images of a target object after electrode implantation, and determining a plurality of target cross-sectional images containing electrodes from the plurality of craniocerebral cross-sectional images based on electrode implantation characteristics; and determining electrode positions in the plurality of target cross-sectional images according to electrode characteristics of electrodes in the target cross-sectional images. According to the electrode positions in the multiple target cross-sectional images and the position relation of the multiple target cross-sectional images, the electrode track in the brain of the target object is determined, postoperative medical images are automatically analyzed, manual intervention is not needed, the manual intervention cost is reduced, and meanwhile high robustness and reliability of determining the brain electrode track are guaranteed.

The invention provides a deep learning defect identification system and method based on simulation generation of a simulation data set. The system comprises a defect model generation module, a virtual three-dimensional CT module and a defect identification deep learning module. The defect model generation module is used for generating a defect-containing CAD model; the virtual three-dimensional CT module is used for performing CT scanning and reconstruction on the CAD model; the defect recognition deep learning module is used for marking CT sectional images and using marked data for network training, the problem that an X-ray image data set is insufficient is solved, and the method is safe, rapid and independent of equipment; the problem of inaccurate marking in deep learning defect identification is also solved, and the defect identification precision of the deep learning module obtained by training is improved.

Embodiments of the present application relate to the technical field of medical image processing, and provide a medical image recognition method and apparatus, a device and a storage medium. The method comprises: acquiring a target image set, the target image set comprising a plurality of target whole-body tomographic images collected in a first preset time sequence; extracting organ tomographic images of a target organ from the target whole-body tomographic images; constructing an initial organ matrix for the target organ tomographic images of each target whole-body tomographic image; calculating a metabolic reference matrix and the initial organ matrix on the basis of a preset object metabolic difference sub-network, to obtain an organ metabolic difference matrix; and performing metabolic difference recognition on the basis of the organ metabolic difference matrix and the metabolic reference matrix, to obtain a target metabolic difference map that represents metabolic difference information between any two target organs in the target whole-body tomographic images. The embodiments of the present application can improve the recognition accuracy of images acquired by positron emission tomography, thereby improving the accuracy of determining metabolic conditions of individual objects.

The invention discloses an optical coherence tomography device for optimizing aberration digital compensation and an imaging method, the device comprises an optical coherence tomography device and an image processing module, and the optical coherence tomography device comprises an illumination module, an interference module, an area array detection module and a synchronous control module. The imaging method comprises the following steps: setting device light source parameters according to coherent gating, and determining a sampling matching relation of the area array detection module according to a sampling theorem; selecting a frequency sweeping mode or a time domain mode, electrifying to activate a corresponding light source and a synchronous time sequence, and adjusting the position of a reference mirror; collecting a full-field interference sequence of the area-array camera, performing digital aberration compensation, performing compensation and denoising on volume data of different depths, and exporting a three-dimensional diffraction limit image; and outputting a three-dimensional tomographic image with transverse diffraction limit and axial micron-level resolution. According to the invention, the imaging resolution and the imaging speed of optical coherence tomography are improved under the conditions that the camera specification is reduced, the GPU dependence is reduced and the system cost is reduced.

The invention discloses an optical coherence tomography optical path structure and a dispersion compensation method thereof. The light path structure comprises a coherent light source, a 2 * 2 optical fiber coupler, a reference light path, a sample light path, an equal-wave-number spectrograph and an upper computer; low-coherence broadband light emitted by the coherent light source is divided into reference light and sample light after passing through the 2 * 2 optical fiber coupler, the reference light and the sample light are sequentially transmitted in a reference light path and a sample light path, the reference light and the sample light are retroreflected through a sample and a reference mirror and are retroreflected to the 2 * 2 optical fiber coupler according to an original light path, interference is generated, and interference light beams are transmitted to the equal-wave-number spectrometer. Interference light beams are split to an upper computer according to equal wave number intervals, and a tomographic image of a sample is obtained through algorithm processing of the upper computer. By means of the structure, the existing dispersion loss problem is solved from the source, most dispersion generated by a light path is solved, other dispersion is eliminated by adding a dispersion compensating mirror made of optical glass materials with certain thickness in the light path through algorithm calculation, the algorithm is simple, convenient and fast, and the imaging speed is high.

The invention belongs to the technical field of discrete element simulation, and particularly relates to a digital twinborn discrete element model construction method based on CT (Computed Tomography) scanning, which comprises the following three steps of: in a CT scanning stage, acquiring a high-resolution cross-sectional image by optimizing scanning parameters, and carrying out image noise reduction and feature enhancement processing; in the structural analysis stage, a segmentation model is constructed based on a deep convolutional neural network, and automatic annotation of a scanned image and intelligent prediction of a three-dimensional space topological relation are realized; in the discrete element modeling stage, the reconstructed three-dimensional grid model is converted into a discrete element model with real physical attributes through parameter calibration, and discrete element simulation is achieved. According to the method, the non-destructive detection advantage of industrial CT and the feature extraction capacity of deep learning are combined, microstructure characterization is converted into macroscopic performance prediction, and a reliable simulation analysis method is provided for performance optimization of the composite material.

The invention discloses a robot-assisted real-time three-dimensional Doppler OCT (optical coherence tomography) angiography method and system, relates to the technical field of medical imaging, and can be used for carrying out real-time and large-view-field three-dimensional Doppler optical coherence imaging evaluation during a microvascular anastomosis operation and improving the anastomosis operation evaluation accuracy and efficiency. According to the system, a spectral domain optical coherence tomography system SD-OCT is used for performing spectral domain optical coherence tomography on a sample to obtain a sample cross-sectional image, and the sample cross-sectional image is sent to a display to be displayed. The OCT imaging probe and the binocular depth camera are integrally installed at the tail end of the robot arm, and the robot arm operates the OCT probe and the binocular depth camera to move to different positions and angles to image a sample. The OCT imaging probe collects optical signals reflected by a sample, and the signal processing host and the displayer receive spectral signals transmitted by the OCT imaging probe and display the three-dimensional structural characteristics of the microvessel and the blood flow velocity condition in the blood vessel.

A computer cuts out, from a tomographic image obtained by imaging the inside of a human body, a plurality of partial images, which include the same position in the tomographic image and have different sizes, calculates a probability of each of the plurality of partial images being a region of a specified lesion, calculates an integrated value by integrating the probabilities calculated from the plurality of partial images using a calculation that increases a contribution of probabilities calculated from partial images corresponding to at least an intermediate size out of the plurality of partial images, and identifies the same position as a region of the specified lesion when the integrated value exceeds a predetermined threshold.

The invention discloses a pervious concrete microstructure and water permeability prediction method based on artificial intelligence, and belongs to the technical field of pervious concrete performance prediction.The pervious concrete microstructure and water permeability prediction method comprises the steps that S1, a characteristic coarse aggregate particle three-dimensional geometric model data set library is established; s2, establishing an aggregate three-dimensional close packing model in the pervious concrete structure; s3, re-importing the aggregate three-dimensional close packing model into the image modeling software MIMICS, and reversely generating a two-dimensional cross-sectional image which is a coarse aggregate image; s4, constructing a pervious concrete microstructure prediction model by adopting an artificial intelligence method; s5, inputting the coarse aggregate image into the pervious concrete microstructure prediction model built in the step S4 to obtain a pervious concrete prediction image and porosity information; s6, establishing a connected pore model required by permeation to realize the prediction of the water permeability performance; according to the artificial intelligence-based pervious concrete microstructure and water permeability prediction method provided by the invention, refined prediction of the water permeability coefficient of the pervious concrete is realized.

This invention discloses a method for transport of intensity diffraction tomography based on non-interferometric synthetic aperture. By acquiring through-focus intensity stacks under different illumination angles and performing three-dimensional Fourier domain half-space filtering (or 3D Hilbert transform equivalently) on the measured intensity stack, further combining with non-interferometric synthetic aperture, the 3D refractive index tomographic imaging in a non-interferometric manner without the need to meet matched illumination condition can be achieved. Leveraging the inherent advantage of synthetic aperture, the imaging resolution reaches the incoherent diffraction limit, resulting in high-resolution imaging results. The non-interferometric nature of TIDT-NSA offers a simple imaging optical setup, delivers speckle-free imaging quality, and is compatible with an off-the-shelf bright-field microscope.

The invention belongs to the related technical field of three-dimensional shape measurement, and discloses a three-dimensional shape measurement method and system based on continuous vertical scanning structure illumination, and the method comprises the steps: S1, collecting a structured light image of a to-be-measured sample at each scanning position; s2, carrying out background light intensity estimation by using the adjacent structured light images in the time domain, obtaining a uniform illumination image, and correcting the structured light image; s3, performing high-pass filtering on the uniform illumination image to obtain high-frequency in-focus information; performing low-pass filtering on the corrected structured light image to obtain low-frequency in-focus information; fusing the two to obtain an optical tomographic image of the to-be-detected sample; and S4, obtaining the three-dimensional shape of the to-be-detected sample. According to the method, a continuous scanning structured light illumination measurement method is adopted, only one structured illumination phase shift cosine fringe pattern is collected at each scanning position, and high-precision chromatography extraction is carried out, so that high-precision chromatography imaging under the condition of an uninterrupted continuous scanning mode can be realized, and the measurement speed and precision are greatly improved.

The invention discloses a tunnel cross-sectional image analysis method based on image processing, and aims to solve the problems that a surface image is not clear in correspondence with a transient seismic wave method, a geological radar and a resistivity imaging section space, and anomalies at a certain distance in front are difficult to map to a tunnel face. According to the method, anisotropic reforming is carried out by adopting a Fourier neural operator, cross-modal Transform registration of a micro attention mask based on sector geometry, curve mileage and section polar coordinate position coding is combined, analytic geometry mapping and uncertainty propagation are matched, tunnel face structure traces and water seepage texture evidences are fused, and the tunnel face structure traces and the water seepage texture evidences are combined. The technical effects of accurate positioning of abnormity on the tunnel face, position confidence range estimation, risk grading early warning, stripe artifact suppression, abnormal boundary reservation and output of structured results of mileage stake numbers, azimuth angles, distance intervals and the like are achieved.

The invention provides a micro-damage tomography method based on a nonlinear ultrasonic static component damage index, and particularly relates to the technical field of material monitoring. The method specifically comprises the following steps: S1, arranging sensors on the surface of a to-be-detected component; s2, setting a signal excitation end and a signal receiving end; s3, signal emission is carried out on the signal excitation end, a receiving signal of the signal receiving end is collected, and error preprocessing is carried out on the collected signal; s4, carrying out fast Fourier transform analysis and obtaining a static component energy value; s5, calculating a nonlinear damage index; s6, based on a detection grid formed by a circular array or an orthogonal scanning type array, a nonlinear damage index is used as a parameter to be embedded into an RAPID ellipse positioning algorithm, pixel values of the detection grid in an imaging area are calculated and filled, and a micro-damage tomographic image is generated. According to the method, millimeter-level positioning imaging of microcracks, layering defects and the like in the to-be-monitored structure can be realized by combining the nonlinear damage index with the RAPID ellipse chromatography algorithm.

A mark that identifies a lesion site is displayed on a tomographic image. A two-dimensional map and a graph are displayed regardless of whether or not the mark is displayed. The two-dimensional map represents lesion site probability distribution. The graph represents temporal change of lesion site probabilities.

The invention discloses a pore structure characteristic nondestructive detection method, device and equipment, a medium and a product, and relates to the field of ore nondestructive detection.The method comprises the steps that tomography is conducted on ore particle dispersions with different particle diameters, and ore particle dispersions with different particle diameters are obtained; performing slicing processing on the ore particle discrete body cross-sectional image, and determining a multi-layer slice image; performing three-dimensional reconstruction on the multi-layer slice image to construct a three-dimensional pore model; and according to the three-dimensional pore model, performing nondestructive detection on the pore structure characteristics of the ore particle bulk according to the three-dimensional pore model. According to the invention, effective imaging can be realized in a nondestructive detection process, sample limitation is avoided, and the resolution is improved.

The invention belongs to the field of train structure health monitoring, and discloses a bogie tube plate structure welding seam detection method and device, and the method comprises the steps: obtaining a Lamb wave signal of a to-be-detected tube plate structure welding seam region, and carrying out the preprocessing, and obtaining a preprocessed Lamb wave signal; on the basis of the preprocessed Lamb wave signal, the position of the weld crack defect of the to-be-detected tube plate structure is predicted through the RAPID tomography technology, and a weld crack defect positioning detection result is obtained; inputting the preprocessed Lamb wave signal sequence into a bogie tube plate structure welding seam detection model to obtain a welding seam crack defect quantitative detection result; outputting a welding seam crack defect positioning detection result and a quantitative detection result as welding seam detection results; according to the invention, the positioning and quantitative detection result of the welding seam damage of the bogie tube plate structure can be quickly and accurately obtained, the high-precision detection requirement of the welding seam of the bogie tube plate structure is met, and the safe operation of a rail transit vehicle is ensured.

An image processing device acquires a plurality of projection images, inputs the acquired plurality of projection images to a tomographic image estimation model, which is a trained model generated by performing machine learning on a machine learning model using learning data composed of a set of correct answer data that is three-dimensional data indicating a three-dimensional structure and of a plurality of virtual projection images, onto which the three-dimensional structure has been projected by performing pseudo-projection on the three-dimensional structure with radiation at a plurality of virtual irradiation positions using the three-dimensional data, and which receives the plurality of projection images as an input and outputs an estimated tomographic image group, and acquires the estimated tomographic image group output from the tomographic image estimation model.

The invention discloses a terahertz polyethylene pipeline defect detection method based on an improved MASK-R-CNN network, and the method comprises the following steps: 1, automatically obtaining a detection image through a terahertz tomography method, dividing the image into a training set, a verification set and a test set according to a proportion of 5: 2: 3, and converting the data set into a standard COCO format through Label-Image and Label Me tools; and step 2, inputting the tomographic image into an improved MASK-R-CNN network for training. The terahertz polyethylene pipeline defect detection method based on the improved MASK-R-CNN network disclosed by the invention has the beneficial effects that accurate detection and identification of PE pipeline hot melting joint defects are realized under complex background noise interference by constructing an improved MASK-R-CNN polyethylene pipeline defect identification model based on CBAM.

The invention provides auxiliary puncture equipment, a puncture system and a computer readable storage medium, and the auxiliary puncture equipment comprises a display module, a processor and a memory, the processor is used for executing the following steps when calling the calculation program in the memory: acquiring a plurality of cross-sectional images obtained by scanning a puncture object by the medical imaging equipment; acquiring puncture state information of a puncture needle in the surgical robot; according to the puncture state information, position information of a needle point of the puncture needle and position information of a prediction target point are determined; according to the position information of the needle point and the position information of the prediction target, the needle point and the prediction target are marked on the first cross-sectional image, and the marked first cross-sectional image is obtained; and displaying the marked first cross-sectional image through a display module. By marking the needle point and the prediction target on the cross-sectional image for display, the dependency on doctor experience can be reduced.