73 results about "Ct reconstruction" patented technology

The invention discloses a 3DGS-based integrated visual angle synthesis and sparse visual angle CT reconstruction method, which comprises the following steps: establishing an internal and external parameter matrix through X-ray scanning parameters, constructing an initial voxel space and an initialized 3DGS radiation field by combining sparse visual angle projection data and based on an ACUI strategy, modeling anisotropic radiation intensity by utilizing a radiation intensity response function, and reconstructing a 3D visual angle CT model. And generating a virtual projection by adopting volume consistency rendering. And the optimized radiation field is converted into a voxel grid, density contribution is applied to carry out voxel space reconstruction, a reconstructed voxel space is obtained, and a new 3DGS radiation field is generated. And re-rendering the virtual visual angle projection based on the new 3DGS radiation field, performing iterative optimization by using a loss function, and outputting a new visual angle synthetic image and a CT reconstruction body until a preset number of iterations is reached. And an efficient process of one-time training and dual output is realized. The efficiency and precision of X-ray imaging are improved, the radiation dose of a patient is reduced, and meanwhile, a higher-quality imaging solution is provided for medical diagnosis.

The present application belongs to the field of CT tomographic reconstruction technology and artificial intelligence, and discloses a limited-angle CT reconstruction artifact suppression method based on a multi-domain feature fusion network. In view of the problem that the reconstruction result of traditional CT scanning under limited-angle conditions is prone to artifacts and structural distortion, thereby affecting the image quality and defect detection accuracy, the present application constructs a multi-domain feature fusion artifact suppression network, takes the limited-angle reconstruction result as input, and realizes artifact suppression and detail recovery through the synergistic effect of the encoder part, the decoder part, the feature enhancement part and the feature conversion part. The present application can obtain high-quality tomographic images under limited-angle conditions, effectively reduces the scanning angle and time of industrial CT detection, improves the imaging clarity and reliability without increasing the radiation dose, is suitable for industrial detection of complex structure workpieces, and has important industrial application value.

This invention provides a four-dimensional CT reconstruction method based on real-time kV image-driven reconstruction, including data preprocessing and four-dimensional CT reconstruction. Data preprocessing includes respiratory phase synchronization, spatial resampling, and geometric consistency registration and clipping. Four-dimensional CT reconstruction is implemented using a deep learning model based on a three-dimensional U-Net architecture. The model training adopts a supervised learning paradigm, using normalized prior three-dimensional CT volume data and corresponding two-dimensional kV projection images as joint inputs. Through multi-scale feature encoding and decoding structures, the model learns the mapping relationship between the two-dimensional projection images and the three-dimensional volume data, thereby predicting and outputting the three-dimensional CT volume data under the current respiratory phase. This application achieves an end-to-end closed loop from two-dimensional projection to four-dimensional CT to dose assessment, reducing the radiation dose required for image reconstruction, improving reconstruction speed, and ensuring spatial accuracy and temporal continuity, possessing feasibility and practical value for clinical application.

The invention discloses an X-ray and NeRF algorithm-based luneberg lens internal structure analysis method, and relates to the technical field of three-dimensional imaging of optical lenses, and the method comprises the following implementation steps: S1, configuring an RGB-X-ray combined imaging system, and carrying out data acquisition, and S2, carrying out the calibration of external parameters of a camera through a calibration plate: carrying out the imaging through a specially-made calibration plate, and carrying out the calibration of the external parameters of the camera through the calibration plate. The method comprises the following steps: acquiring a plurality of frames of RGB (Red, Green, Blue) images to obtain external parameters of an RGB camera under a calibration plate coordinate system, acquiring a plurality of frames of X-ray images to obtain an external parameter matrix of an X-ray camera about the calibration plate coordinate system, performing implicit representation on a continuous field in a lens by utilizing NeRF, mapping three-dimensional coordinates to differentiable density and radiation values, and calculating the calibration plate coordinate system. According to the method, high-fidelity reconstruction from multi-angle X-ray projection to a three-dimensional structure is achieved, detail information in the lens can be effectively reserved, meanwhile, the continuity and symmetry of the structure are guaranteed, and compared with a traditional voxelization or CT reconstruction method, the problems that noise artifacts, fractures and unstable numerical values are likely to be generated in the traditional voxelization method are solved.

The application discloses a low-dose CT reconstruction method and device, relates to the technical field of image processing, and comprises the following steps: acquiring a low-dose CT image; adopting a trained deep learning network to process the low-dose CT image in a projection domain, a chord diagram domain and an image domain respectively; in the projection domain, the low-dose CT image is processed to obtain updated projection data; in the chord diagram domain, the updated projection data is processed to obtain updated chord diagram data; in the image domain, the updated chord diagram data is processed to obtain a reconstructed low-dose CT image; wherein the trained deep learning network takes data of a preset category as a training data set, trains an initial deep learning network, and is obtained by performing intermediate supervision in the training process and fine-tuning the deep learning network in the training. The application can realize good reconstruction of the low-dose CT image.

The application discloses a kind of simultaneously sparse angle CT reconstruction and metal artifact high-precision correction method, belong to medical imaging field, specifically: simulate the projection data under the influence of beam hardening under sparse angle sampling, obtain sparse sampling and the sinogram of containing metal trace;Interpolation processing is carried out to sparse sinogram, and the sinogram is obtained as the initial sinogram of sinogram domain;According to the additive property of artifact, preliminarily decompose the CT image containing a large number of artifacts, obtain the initial estimated image of image domain and the initial artifact image of artifact domain;A multi-domain optimization model is established, and the constructed multi-domain optimization problem is alternately iterated minimization solution;Iterative update is carried out to sinogram domain, image domain and artifact domain respectively;The high-precision CT image of reconstruction is output.The application can simulate the sinogram containing metal trace and sparse obtained by CT imaging equipment to carry out simultaneously sparse angle CT reconstruction and metal artifact correction, and the reconstruction effect is good and correction precision is high.

The invention relates to the technical field of medical image processing. The invention discloses a sparse angle CT (Computed Tomography) reconstruction method and device based on an image domain and a projection domain, which can improve the integrity of feature extraction in a CT imaging reconstruction process. The sparse angle CT reconstruction method based on the image domain and the projection domain comprises the steps that the image domain and the projection domain corresponding to original projection data in CT scanning are obtained, the image domain is a set of image data obtained after the original projection data are processed through a CT reconstruction algorithm, and the projection domain is a set of original projection data; and inputting the image domain and the projection domain into a DU-Net network model, and carrying out parallel processing on the image domain and the projection domain through the DU-Net network model to obtain a target CT image.

A calibration target for use with a radiographic image detector includes a target body securable to the image detector and a plurality of radiopaque linear markers, e.g. wires, ixed to the target body, wherein access to the image detector by incident radiation is at least partially blocked by the plurality of linear markers. By using the geometric properties of wires and advanced detection techniques, a precise calibration suitable for high-quality, volumetric three-dimensional CT reconstruction from biplanar X-ray images is achieved.

The invention discloses a fruit edible rate online detection method and device based on sparse CT reconstruction. The method comprises the following steps: carrying out multi-angle sparse X-ray projection data acquisition on a fruit; carrying out dark field and flat field correction, cutting, zooming, enhancement and normalization preprocessing on the X-ray image; complementing sparse angle data by adopting an interpolation algorithm; generating a three-dimensional CT model by using a CT reconstruction algorithm; extracting a two-dimensional slice image based on axis rotation in a CT model, and extracting pulp and whole fruit areas by adopting a deep learning semantic segmentation network; and reconstructing a segmentation result into an edible rate model, and calculating the edible rate by combining the pulp density and the whole fruit mass. The detection device comprises a radiation source, an X-ray detector, a conveyor belt, an electric rotating table, a screw rod sliding table, a clamping device and the like, and supports online automatic data acquisition. The method realizes high-precision modeling of the internal structure of the fruit and automatic measurement of the edible rate, and has the advantages of high precision, high efficiency, wide adaptability and the like.

The present application belongs to the technical field of medical image processing, and particularly relates to a neural attenuation field method for iterative diffusion refinement of multi-source static CT reconstruction. In view of the reconstruction ill-conditioning problem caused by insufficient projection data under ultra-sparse view angle, the method first trains a neural attenuation field (NAF) model based on initial sparse projection; then adopts an angle prior guided strategy (APGPS) to adaptively determine a new projection view angle with the largest amount of information, and synthesizes the projection of the view angle by the NAF; then refines the synthesized projection by a double-branch conditional diffusion model (DRPR), and simultaneously predicts the structural residual and random noise; adds the refined projection as a pseudo-label to the training set, and iteratively updates the NAF model. The present application gradually expands the effective view angle coverage range through the iterative diffusion refinement mechanism, significantly suppresses the stripe artifact and restores the high-frequency details under the condition of no external labeled data, and breaks through the performance bottleneck of ultra-sparse view angle reconstruction.

The invention provides a method and system for reconstructing a three-dimensional CT according to a single two-dimensional X-ray perspective image through reinforcement learning, and belongs to the technical field of image reconstruction. Processing the obtained X-ray perspective image by using a pre-trained reconstruction model to obtain a reconstructed three-dimensional CT image; wherein the training set is used for training to obtain the reconstruction model, and each group of training data comprises a three-dimensional CT at a t1 moment, a three-dimensional CT at a t2 moment and an X-ray perspective image corresponding to the CT at the t2 moment. According to the method, the three-dimensional CT reconstruction precision and credibility are improved by utilizing strong modeling and solving capabilities of reinforcement learning; the method can further improve the reconstruction precision and speed of three-dimensional CT, and is of great significance in reducing the imaging dosage, improving the imaging quality and improving the cancer treatment level.

The invention discloses a three-dimensional CT (Computed Tomography) reconstruction method and device under an ultra-sparse view angle. The method comprises the following steps: scanning an object to be reconstructed to obtain X-ray projection data in two mutually orthogonal directions; preprocessing the X-ray projection data in the two mutually orthogonal directions, wherein the preprocessing comprises normalization, denoising and size alignment; inputting the preprocessed X-ray projection data in the two mutually orthogonal directions into a preset three-dimensional CT reconstruction model, and obtaining predicted three-dimensional CT volume data corresponding to the object to be reconstructed; wherein the preset three-dimensional CT reconstruction model is established based on a coding and decoding overall framework, CAD geometric structure information is introduced in a decoding stage when the preset three-dimensional CT reconstruction model is obtained through training, and structure loss is introduced in a preset loss function. According to the method, the precision of three-dimensional CT reconstruction can be improved under the ultra-sparse view angle, and the consistency and stability of the structure are enhanced.

The present application relates to an industrial CT beam hardening correction method, comprising: taking the jth row of pixel points in each circumferential DR image to form a projection image; CT reconstruction is carried out on the projection image to obtain a tomographic image; a homogeneous material region in the detected sample is selected in the tomographic image; a function relationship formula corresponding to the minimum standard deviation of all corrected pixel values in the region is obtained, and the obtained function relationship formula is recorded as the optimal function relationship formula; the pixel value of each pixel point in each circumferential DR image is substituted into the optimal function relationship formula to obtain the corrected pixel value of each pixel point, and then the DR image after beam hardening correction is obtained; finally, the DR image after beam hardening correction is reconstructed to obtain the corrected CT image of the detected sample. The method does not need additional processing of contrast test blocks or test samples with known structures, the process is simple, especially for homogeneous material parts, the full-automatic interactive link can be realized, and the image quality and efficiency of industrial CT are improved.

The invention relates to a target detection method based on a CT (Computed Tomography) reconstructed image, belongs to the technical field of CT reconstructed image processing, and solves the problem of low target detection precision caused by sawtooth artifacts of the CT reconstructed image. The target detection method comprises the following steps: preprocessing a CT reconstruction image to obtain a to-be-processed image; performing anti-aliasing processing on the to-be-processed image to obtain an anti-aliasing processed image; and inputting the image after anti-aliasing processing into a pre-trained target detection model to obtain a target detection result. And the target detection precision in the CT reconstruction image is improved.

The application discloses a three-dimensional CT reconstruction method based on a single-view X-ray film, constructs a three-dimensional CT image reconstruction neural network model, and comprises a feature extraction network based on a deep residual convolutional neural network and an implicit neural representation network based on a multilayer full connection; the feature extraction network based on the deep residual convolutional neural network is used to extract multilevel image features from the X-ray film, and fixed low-resolution volume features are generated based on the highest layer features through three-dimensional convolution; and then the target three-dimensional image is modeled as the implicit neural representation network based on the multilayer full connection, that is, for any point in a three-dimensional space, the position code of the point, the pixel image features corresponding to the point projection and the voxel features corresponding to the point are input into the implicit neural representation network, and the corresponding voxel density value is output, so that a continuous high-resolution three-dimensional image estimation task is realized. The application can reconstruct and generate effective and reliable three-dimensional CT images.

The present application provides a multi-task non-ideal measurement CT image reconstruction method and system, device and medium, a text-visual collaborative prompt contrast learning method is designed for multi-task non-ideal measurement CT reconstruction. In order to integrate text and visual features in the network, a text-visual collaborative prompt module is designed, which combines the semantic representation of different text prompts with the fine-grained features of visual prompts, thereby enhancing the controllability, explainability and degradation adaptability of the CT image reconstruction process. The present application constructs a high-frequency enhancement module as the core component of the main network structure, which significantly improves the extraction and optimization of high-frequency information by the self-attention mechanism and adaptive filtering mechanism, thereby effectively alleviating the loss of high-frequency details. The present application introduces a new type of composite loss function, which can significantly improve the visual quality and fidelity of the reconstructed image.

Nondestructive evaluation (NDE) of objects can elucidate impacts of various process parameters and qualification of the object. Computed tomography (CT) enables rapid NDE and characterization of objects. However, CT presents challenges because of artifacts produced by standard reconstruction algorithms. Beam-hardening artifacts especially complicate and adversely impact the process of detecting defects. By leveraging computer-aided design (CAD) models, CT simulations, and a deep-neutral network high-quality CT reconstructions that are affected by noise and beam-hardening can be simulated and used to improve reconstructions. The systems and methods of the present disclosure can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art.

The invention relates to the technical field of medical images, and discloses a cascade expansion neural network for sparse view CT reconstruction. According to the method, high-throughput information transmission is established between adjacent stages through the auxiliary information multi-channel enhancement initialization module, the information bottleneck of single-channel transmission of a traditional deep expansion network is broken through, and the characterization capability of a complex structure is enhanced; all historical features are aggregated through a cross-stage memory enhancement module, and long-range memory dependence is established, so that the problem of insufficient global context capture of the existing method is solved, and organ contour and fine blood vessel reconstruction is more accurate; the optimal threshold is dynamically generated through the content perception adaptive threshold module according to the input features, the limitation of a fixed threshold is overcome, and the optimal balance of noise suppression and detail reservation is achieved. Finally, the network can reconstruct a CT image with higher quality and higher diagnostic value under extreme sparse projection data, and the consistency, reliability and clinical diagnosis precision of sparse view CT quantitative analysis are improved.

The invention belongs to the field of CT (Computed Tomography) tomography reconstruction technology and artificial intelligence, and discloses a finite angle CT reconstruction artifact suppression method based on a multi-domain feature fusion network. In order to solve the problem that image quality and defect detection precision are affected due to the fact that a reconstruction result of traditional CT scanning is prone to artifacts and structural distortion under the condition of a limited angle, a multi-domain feature fusion artifact suppression network is constructed, and a limited angle reconstruction result is used as input. Through the synergistic effect of the encoder part, the decoder part, the feature enhancement part and the feature conversion part, artifact suppression and detail recovery are realized. According to the method, high-quality cross-sectional images can be obtained under the limited angle condition, the scanning angle and time of industrial CT detection are effectively reduced, the imaging definition and reliability are improved on the premise that the radiation dosage is not increased, and the method is suitable for industrial detection of workpieces of complex structures and has important industrial application value.

Provided herein are methods, apparatuses, computer program products, and systems for generating textured surfaces using computed tomography. One method can include generating data of an X-ray detectable feature represented in volumetric data of a reference object acquired using an X-ray scanner; determining a first transformation matrix using the data of the X-ray detectable feature; obtaining a reference image of an optically detectable feature included in the reference object captured using an optical camera; calculating a second transformation matrix using the reference image of the optically detectable feature; producing a calibration transformation matrix using (i) the first transformation matrix, (ii) the second transformation matrix, and (iii) a third transformation matrix; providing the calibration transformation matrix, which is used in aligning an image of a physical object obtained using the optical camera with a CT reconstruction volume of the physical object obtained using the X-ray scanner.

The invention discloses a coal density distribution and particle size distribution measuring system and method, and relates to the field of coal washing, and the system comprises a coal sample conveying device, an X-ray source, an X-ray detector, a rotating rack, an angle sensor and a computer; the X-ray source and the X-ray detector are symmetrically fixed on the rotating rack, and the rotating rack is used for driving the X-ray source and the X-ray detector to rotate; the angle sensor is arranged on the rotating rack; the coal sample conveying device conveys a to-be-detected coal sample into an imaging area of the X-ray source, the X-ray source emits X-rays to penetrate through the to-be-detected coal sample in the rotating process, the X-ray detector collects X-ray projection images at multiple angles, and the angle sensor collects the rotating angle of the rotating rack in the rotating process; and the computer generates a coal sample cross section image through a CT reconstruction algorithm according to the X-ray projection image and the rotation angle, and calculates the density distribution and the particle size distribution of the coal sample. According to the invention, the accuracy and speed of density distribution and particle size distribution measurement can be improved.

A calibration target for use with a radiographic image detector includes a target body securable to the image detector and a plurality of radiopaque linear markers, e.g. wires, fixed to the target body, wherein access to the image detector by incident radiation is at least partially blocked by the plurality of linear markers. By using the geometric properties of wires and advanced detection techniques, a precise calibration suitable for high-quality, volumetric three-dimensional CT reconstruction from biplanar X-ray images is achieved.

The invention relates to the technical field of CT (Computed Tomography) artifact correction and image processing, in particular to a CT image ring artifact correction method based on detector jitter, which comprises the following steps of: controlling a detector to jitter according to a preset periodic jitter track and acquiring a projection image during scanning; returning the image to a non-jittering state according to the track; carrying out CT reconstruction on the returned image; and performing bilateral filtering on the reconstructed CT image to obtain a corrected image. The jitter trajectory follows a triangular wave function, bilinear interpolation is adopted for return, an FDK algorithm is adopted for reconstruction, and a Gaussian weighting mode combining space and a pixel value domain is adopted for bilateral filtering. According to the method, a ring artifact generation mechanism is broken through physical jitter, edge protective filtering is combined, the image definition is kept while the ring artifacts are effectively removed, and the method has the advantages of being independent of additional correction, free of a large amount of training data and easy and convenient to implement.

The present application relates to image processing technology, and aims to provide a diffusion model guided transverse truncated CT reconstruction method. It comprises: inputting the actually collected original truncated projection data into a preprocessing network DA-UKAN for completion to obtain preliminary completed projection data; using the original truncated projection data to replace the projection data corresponding to the FOV in the preliminary completed projection data, and using a weighted fusion method to obtain the data near the edge of the FOV; using a reconstruction algorithm to process the completed and fused projection data to obtain a preprocessed reconstruction image; inputting the preprocessed reconstruction image into a diffusion model KAN-DM, skipping the early steps of the diffusion model, and only performing a reverse sampling step less than the total number of forward noise addition steps, and outputting a truncated artifact corrected and completed CT reconstruction image. The present application can generate an image with rich details, has high flexibility, can adapt to different reconstruction parameters and truncated proportion projection data completion, reduces the calculation cost, and balances the accuracy, structural consistency and noise smoothing of image recovery.

This invention proposes a single-view X-ray 3D CT reconstruction method based on a self-optimizing dual-domain cyclic diffusion probability model, aiming to solve the problem of degraded reconstruction quality caused by severe information loss in single-view CT in traditional CT. This method constructs diffusion denoising processes in both the projection domain and the CT volume domain, and establishes a bidirectional mapping relationship between forward and back projection through a perceptual transformation module. Cyclic consistency constraints are formed during the back diffusion process, achieving cross-domain structural collaborative correction. Simultaneously, a condition generation module based on structural entropy is designed to quantify and dynamically filter uncertainties in multi-view features to enhance model stability. Furthermore, a self-optimizing mechanism is introduced, performing multiple rounds of denoising and error correction within each back diffusion time step to gradually correct prediction bias and suppress error accumulation. By collaboratively optimizing the projection domain and the CT volume domain through a dual-domain joint loss function, a unity of physical consistency and structural realism is achieved, thereby realizing high-precision 3D CT reconstruction under single-view X-ray conditions.

The application discloses an integrated view synthesis and sparse view CT reconstruction method based on 3DGS, which comprises the following steps: establishing internal and external parameter matrices through X-ray scanning parameters, combining sparse view projection data, and constructing an initial voxel space and initializing a 3DGS radiation field based on an ACUI strategy; modeling anisotropic radiation intensity by using a radiation intensity response function; generating virtual projection by using volume consistency rendering. The optimized radiation field is converted into a voxel grid, and density contribution is applied for voxel space reconstruction to obtain a reconstructed voxel space and generate a new 3DGS radiation field. The new 3DGS radiation field is used to re-render virtual view projection, and iterative optimization is carried out by using a loss function until a preset iteration number is reached to output a new view synthesis image and a CT reconstruction body. An efficient process of one-time training and double-output is realized. The efficiency and precision of X-ray imaging are improved, the radiation dose of patients is reduced, and a higher-quality imaging solution is provided for medical diagnosis.

The application discloses a deep learning-based limited-angle projection phase contrast CT image reconstruction method and device, which comprises the following steps: acquiring limited-angle projection data of X-ray phase contrast CT scanning through an equal-angle increment strategy; performing flat-field correction and dark-field correction preprocessing on the limited-angle projection data to obtain limited-angle projection data before phase recovery; performing preprocessing on the limited-angle projection data through a phase recovery strategy to obtain limited-angle projection data after phase recovery; performing CT reconstruction on the limited-angle projection data before and after phase recovery through a filtered back projection strategy to obtain CT images of the limited-angle projection before and after phase recovery; constructing a phase contrast CT intelligent image reconstruction network model and training; and performing reconstruction processing on the CT images of the limited-angle projection before and after phase recovery through the trained phase contrast CT intelligent image reconstruction network model to obtain reconstructed phase contrast CT images. The application can quickly and efficiently perform phase contrast CT image reconstruction, effectively suppress limited-angle reconstruction artifacts and improve image quality.

A computer-implemented method and system of CT reconstruction can include receiving one or more CT projection images; performing smooth filtering on the one or more CT projection images to generate one or more smooth boundary filtered images; and back-projecting the one or more smooth boundary filtered images.

The application discloses a kind of low-dose CT reconstruction methods of prior image and convolution sparse network in combination, belong to computer tomography technical field.The present application includes the following steps: first, using distinctive feature representation method obtains high-quality prior image;Second, design the convolution sparse network of feature fusion;Next, the error of reconstruction image and prior image is calculated, and iterative reconstruction gradient is estimated according to error;Then, the reconstructed image is fine-tuned using total variation constraint;Finally, the iterative reconstruction of low-dose CT is realized by module cascade form.The present application algorithm comprehensively utilizes the advantage of prior image of plan scanning and convolution sparse network, has explainable strength, reconstructed image quality is high, and has the advantages such as less noise artifact, has greater advantage in clinical tumor radiotherapy, can improve examination efficiency, reduce the radiation injury of patient non-target organ.

The invention discloses a CT implicit reconstruction method based on a single X-ray image. The method comprises the following steps: training each sample in a CT data set into an implicit expression; constructing a weight generation network, recombining the implicitly expressed network weights into a data set, and training the weight generation network; predicting a weight generation network parameter and a camera parameter corresponding to the input X-ray image through an image encoder; according to the predicted weight generation network parameters and camera parameters, performing end-to-end optimization on the encoder in combination with the generated implicitly expressed miniaturized body rendering result; in the reasoning stage, an X-ray image is input for implicit expression prediction of the corresponding CT, and the task of CT reconstruction is achieved. According to the CT implicit reconstruction method based on the single X-ray image, through learning in an implicit space, implicit expression prediction can be conducted on input of the single X-ray image at low cost, and then high-resolution CT reconstruction is achieved.