97 results about "Ct reconstruction" patented technology

The invention provides a CT guided liver puncture training method and system based on virtual reality, and relates to the technical field of virtual reality. A deformable liver model and a virtual CT reconstruction engine under respiration driving are constructed, needle body posture mapping and image fusion display are achieved by fusing an inertia-electromagnetic dual-mode sensor, path interaction control, tissue dynamic response and score feedback are supported, the scene difficulty is automatically adjusted based on a training result, and the accuracy and the reliability of the system are improved. Progressive puncture skill training of static breath-holding, shallow breath and free breath scenes is achieved, the sense of reality of training, operation feedback and teaching efficiency are improved, and the system is suitable for development and clinical teaching application of an interventional therapy training system under the guidance of medical images.

The invention belongs to the field of CT (Computed Tomography) tomography reconstruction technology and artificial intelligence, and discloses a finite angle CT reconstruction method based on combination of a three-dimensional conditional diffusion model and synchronous iteration. The CT is an imaging technology which utilizes X-rays to irradiate a target from different angles and acquire projection, and obtains an internal three-dimensional structure through reconstruction. Different from traditional CT depending on nearly full-angle scanning, the method only collects limited-angle projection, and achieves fault reconstruction under the limited-angle condition through a three-dimensional condition diffusion model of space domain-frequency domain two-way decoding and by means of structural generality priori of a workpiece. Meanwhile, projection and fault data consistency correction is carried out in combination with a synchronous iteration reconstruction technology, the advantages of an iteration method in the aspect of physical mechanism characterization are exerted, interpretable physical constraints are provided for a deep learning network, and therefore the reliability and precision of a reconstruction result are improved.

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 invention discloses a low-dose CT reconstruction method and device, and relates to the technical field of image processing, and the method comprises the steps: obtaining a low-dose CT image; the trained deep learning network is adopted to process the low-dose CT image in a projection domain, a chordal graph domain and an image domain; in the projection domain, processing the low-dose CT image to obtain updated projection data, in the chordal graph domain, processing the updated projection data to obtain updated chordal graph data, and in the image domain, processing the updated chordal graph data 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, the initial deep learning network is trained, intermediate supervision is performed in the training process, and the trained deep learning network is finely adjusted to obtain the deep learning network. According to the invention, good reconstruction of the low-dose CT image can be realized.

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 provides a multi-task non-ideal measurement CT image reconstruction method and system, equipment and a medium, and designs a text-vision collaborative prompt contrast learning method which is used 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, and the module combines semantic representations of different text prompts with fine-grained features of visual prompts, so that the controllability, interpretability and degradation adaptability of a CT image reconstruction process are enhanced. According to the invention, the high-frequency enhancement module is constructed as a core component of a main network structure, and the module significantly improves the extraction and optimization of the network on high-frequency information through a self-attention mechanism and an adaptive filtering mechanism, thereby effectively relieving the loss problem of high-frequency details. According to the method, a novel composite loss function is introduced, and the visual quality and fidelity of a reconstructed image can be remarkably improved.

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 present invention discloses a sparse view CT reconstruction method based on a conditional embedding fusion diffusion model, comprising: constructing a data set and dividing it into a training set and a test set; constructing a conditional generation model and a conditional embedding fusion diffusion model, including a Fourier domain artifact removal module, a conditional attention embedding module, and an adaptive fusion attention generation mechanism. A low-quality image is input into the conditional generation model, and the final reconstruction result is obtained by combining the refined residual of the conditional embedding fusion diffusion model; a loss function is designed to optimize the model parameters, and the Adam optimizer is used on the training set to achieve iterative optimization and update of the model parameters; the trained conditional generation model and conditional embedding fusion diffusion model can achieve high-quality reconstruction of sparse view CT. This method significantly improves the quality of reconstructed images in a variety of sparse view scenarios, provides a novel and efficient solution for sparse view CT image reconstruction, and shows good application potential.

Data processing and reconstruction techniques that enhance the practical utility of Ross spectrometers, by addressing the issues of long acquisition times and / or high degrees of noise associated with Ross spectrometers. By applying these techniques to noisy data, it is possible to generate low-noise spectral images, which can significantly increase the practical utility of the spectrometers.

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

The 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 present invention discloses a low-dose CT reconstruction method based on a residual domain iterative optimization network, which belongs to the field of computer tomography. The low-dose CT reconstruction method of the present invention first establishes a multi-objective optimization function for low-dose CT reconstruction by adding image domain and projection domain residuals; then, the multi-objective optimization function is decomposed and solved, wherein the directly optimized part adopts iterative solution calculation, and the residual domain update part adopts a convolutional sparse coding network to achieve; finally, the iterative reconstruction of low-dose CT is achieved through module cascade. In addition, in order to improve the initial optimization value, the present invention adopts a convolutional sparse coding network in the image domain to obtain the update of the first reconstructed image. The method of the present invention can improve the reconstruction effect, convergence and generalization ability of the network, reduce noise artifacts in the low-dose reconstructed image, and improve the imaging effect.

The application discloses a three-dimensional light-thermal parameter reconstruction method and system for a combustion process, and belongs to the field of combustion diagnosis.The application solves the problems of low measurement accuracy in a high-temperature environment and poor accuracy of extracting effective signals in a complex background existing in the prior art light field parameter measurement.The application adopts a polarization spectrum camera and a light field camera to synchronously perform multi-angle data acquisition on a combustion area, and acquires polarization spectrum data and light field data of the combustion area;light field depth information is extracted from the light field data, a three-dimensional structure of the combustion area is constructed by combining the light field depth information with a light field reconstruction algorithm, spectral analysis is performed on the polarization spectrum data, and polarization degree and polarization angle information of the polarization spectrum are acquired;the three-dimensional structure is combined with a CT reconstruction algorithm; a three-dimensional distribution field of particulate matters in the combustion process is acquired; and based on the polarization spectrum information and the preprocessed light field data, the three-dimensional temperature field and the concentration field distribution of gases in the combustion area are calculated by using the optimized three-dimensional structure.The application is mainly used for combustion product parameter acquisition.

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 invention discloses a method for removing ring artifacts in CT imaging. The method comprises the following steps: correcting a projection sinogram of a central channel area of a projection domain detector based on a projection domain correction table; performing CT reconstruction on the corrected projection sinogram to obtain a reconstructed original CT image; extracting an image domain ring artifact of the original CT image; and subtracting the image domain ring artifact from the original CT image to obtain a ring-removed CT image. Therefore, by introducing the central channel selection window to carry out projection domain ring removal on the central channel area and carrying out integral ring removal in the image domain, the problem of black and white centers can be well inhibited under the condition that CT image distortion is not caused, the quality of the CT image, especially the photon counting CT image, is improved to a great extent, and the method can be widely applied to CT image processing.

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.