132 results about "Ct reconstruction" patented technology

The invention discloses a low-dose CT reconstruction method and system based on global optimization iteration deep learning, and the method comprises the steps: collecting CT cross section data of a normal dose, and generating low-dose CT data; the method comprises the following steps: establishing a dual-domain analysis compression iteration model, constructing maximum posteriori estimation based on a CT projection chordal graph data composite Poisson noise generation mechanism, subdividing CT projection data into a projection domain and a chordal graph domain, sequentially updating the projection data, the chordal graph data and image data, and constructing an iteration algorithm to realize CT global optimization modeling. Expanding an iterative algorithm into a trained reconstruction network; inputting the paired data into the expanded network for training, and storing a model with the minimum output result loss; a sub-network and an attention mechanism are added for CT data features, jump connection weights are finely adjusted, middle layer neural network parameters are finely adjusted and updated through middle supervision, and a global optimization iteration deep learning model NGACI-Net is obtained. According to the method, the problems that the low-dose CT image quality is poor, the reconstruction efficiency is low, and generalization and interpretability are lacked are solved.

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.

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 belongs to the technical field of medical image reconstruction, and particularly relates to a CT reconstruction system and method based on discrete Gaussian representation. According to the technical scheme, the method comprises the steps that a Gaussian function set is constructed, and a three-dimensional voxel space is directly represented through learnable Gaussian parameters; carrying out parallel aggregation on Gaussian function contribution values to a three-dimensional voxel grid by adopting a discretization volume reconstruction technology; establishing a geometric projection transformation model to reversely project the three-dimensional reconstruction volume to a two-dimensional projection domain, and minimizing the difference of the projection domain through joint optimization of Gaussian parameters; and introducing an adaptive density control mechanism to dynamically adjust Gaussian distribution. Compared with an existing method, the method has the advantages that an end-to-end discretization Gaussian function representation mode is adopted, global optimization of any CT geometric configuration is supported, high-quality rapid reconstruction within 5-15 minutes is achieved while the detail integrity of the anatomical structure is kept, and a high-precision and high-robustness three-dimensional reconstruction solution is provided for low-dose CT scanning and emergency treatment scenes.

The invention belongs to the technical field of physiological information processing, and particularly discloses a medical image reconstruction method, device and equipment based on a diffusion model, and the method comprises the steps: obtaining a first medical image; generating a second medical image from the first medical image according to a preset conversion rule; inputting the second medical image into a measurement diffusion model to obtain a noise measurement value of any time step; and inputting the second medical image into an image diffusion model, and obtaining a third medical image based on the constraint of the noise value of any time step. According to the method, multiple reconstruction tasks such as under-sampling MRI reconstruction, sparse view CT reconstruction and medical image super-resolution can be executed by utilizing arbitrary diffusion generation priori. In order to improve the reconstruction quality of the diffusion model, a measurement diffusion model with a determined trajectory is provided for guiding reconstruction, and a parallel trajectory constraint mode is used for enabling the image diffusion model and the measurement diffusion model to be subjected to parallel evolution, so that the reconstruction quality and the reconstruction precision are greatly improved.

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

The invention discloses a sparse view CT reconstruction method based on a conditional embedding fusion diffusion model. The sparse view CT reconstruction method comprises the following steps: constructing a data set and dividing the data set into a training set and a test set; constructing a conditional generation model and a conditional embedding fusion diffusion model, wherein the conditional embedding fusion diffusion model comprises a Fourier domain artifact removal module, a conditional attention embedding module and a self-adaptive fusion attention generation mechanism; inputting the low-quality image into the condition generation model, and obtaining a final reconstruction result in combination with a refined residual error of the condition embedding fusion diffusion model; designing a loss function used for optimizing model parameters, and adopting an Adam optimizer on a training set to realize iterative optimization and updating of the model parameters; the trained conditional generation model and conditional embedding fusion diffusion model can realize high-quality reconstruction of sparse view CT. According to the method, the quality of the reconstructed image is remarkably improved in various sparse view angle scenes, a novel and efficient solution is provided for sparse view angle CT image reconstruction, and good application potential is shown.

The invention discloses a sparse angle CT reconstruction method and model training method based on depth expansion and residual optimization, a depth expansion and residual optimization network comprises a local residual optimization module and a non-local residual optimization module, and the local residual optimization module comprises a wavelet U-Net module and a dense space attention module. The non-local residual optimization module comprises a shift window transformer module and a cavity dense channel attention module, and inputs the sparsely sampled projection data into the depth expansion and residual optimization network to obtain an initial image; residual projection data is acquired and reconstructed in combination with the projection data, and a residual image is obtained; inputting the residual image into a local residual optimization module for iteration to obtain an iterated residual image and a channel feature image, processing the residual image and a residual image obtained by last iteration, inputting the processed image into a non-local residual optimization module for iteration to obtain a channel feature image; and the local residual optimization module and the non-local residual optimization module are alternately used subsequently for iteration until a target image is obtained.

The invention discloses a cone beam CT full respiratory cycle image reconstruction method based on a four-dimensional neural network. The method comprises the following steps: step 1, acquiring cone beam projection data and a respiratory signal in a respiratory state; 2, a respiratory cycle is divided into N phases, multi-phase cone beam projection data Qi are obtained, i = 1, 2,..., N, the Qi is reconstructed through a filtered back projection algorithm, and a multi-phase under-sampling CT reconstructed image # imgabs0 # is obtained; 3, # imgabs1 # is input into a four-dimensional neural network, and a three-dimensional deformation field Di is obtained; 4, performing weighted summation after deformation on the # imgabs2 # by using the Di to obtain a reference phase reconstruction image Xref after motion correction; and step 5, performing inversion on the Di to obtain a three-dimensional reverse deformation field # imgabs3 #, for any breathing phase, performing image deformation on the reference phase reconstruction image Xref by using the corresponding # imgabs4 #, and performing reconstruction to obtain a motion correction image Xi. According to the method, the breathing movement correction can be quickly carried out on the cone beam CT image.

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.

The invention discloses a high-quality sparse CT reconstruction method based on implicit learning and cross-domain feature mining, and relates to the technical field of CT image reconstruction, and the method comprises the following steps: S1, fault feature expression network construction and operation; s11, position coding is carried out; s12, fault feature mining; s121, carrying out cross-domain coordinate conversion; s122, fault feature representation: context visual feature extraction and curve morphological feature extraction; s2, implicit neural representation network processing; s21, fault feature information optimization based on MLP; s22, carrying out adaptive inverse transformation; s23, image reconstruction based on MLP is carried out; s3, self-supervised learning is carried out; according to the method, CT image reconstruction is achieved through implicit neural representation learning, the high-quality CT image is reconstructed on the premise that additional training data is not needed, the problem of artifacts in sparse view angle CT reconstruction can be effectively solved, the X-ray scanning frequency is greatly reduced while the image quality is guaranteed, and radiation exposure of a patient is reduced.

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 an unsupervised low-dose photon counting CT (Computed Tomography) reconstruction method and an unsupervised low-dose photon counting CT reconstruction device based on optimal transmission. According to the method, a low-dose photon counting CT reconstruction problem is modeled as a Cantrovitch problem under a distribution consistency constraint, and the method comprises the following steps: constructing and training an optimal transmission-based unsupervised projection recovery network and constructing and training an optimal transmission-based unsupervised image noise reduction network; performing detail recovery on the photon counting CT low-dose projection by using the trained unsupervised projection recovery network to generate a denoised projection; reconstructing the denoised projection by using an image reconstruction algorithm to generate an intermediate image; and performing noise reduction on the intermediate image by using the trained unsupervised image noise reduction network to obtain a final de-noised image. According to the method, the problem that a high-quality pairing data set is difficult to obtain and a photon counting CT low-dose image is difficult to reconstruct can be solved.

The invention belongs to the technical field of medical imaging and deep learning, and discloses a mixed domain iteration sparse view CT reconstruction method based on self-attention and convolution fusion. According to the method, firstly, a multi-branch module (FSACM) fusing self-attention and convolution is provided, and interaction of global and local feature information can be effectively achieved. Based on the module, an initialization enhancement network, a projection restoration network and a residual error restoration network are designed, and the three networks are reasonably connected and construct an iteration block to jointly cooperate to complete a sparse view CT reconstruction task. A special framework is designed for the three networks, and a gradient updating module is added into an iteration block to optimize initial projection and images; meanwhile, a comprehensive loss function is introduced to balance the reconstruction precision of a projection domain and an image domain. The method can effectively eliminate stripe artifacts and reserve detail structures while maintaining iteration efficiency, realizes high-quality reconstruction, and greatly reduces radiation dose accepted by patients.

The invention provides a TKA postoperative lower limb three-dimensional force line assessment method and system based on multi-modal data fusion and a computer readable storage medium. The TKA post-operation lower limb three-dimensional force line evaluation method based on multi-modal data fusion comprises the following steps: acquiring double lower limb CT images of a patient before total knee arthroplasty; wherein the coverage range of the two lower limbs is from the upper part of the hip joint to the lower part of the ankle joint; performing three-dimensional reconstruction on the double-lower-limb CT image to clearly display each skeleton and surrounding soft tissue structure of the lower limbs; a biplane lower limb full-length X-ray acquisition system is utilized to acquire normal position and lateral position X-ray images of two lower limbs of a patient at a load-bearing position, and the space relation between skeletons and prostheses of the lower limbs in the load-bearing state is determined; and performing fusion analysis on CT reconstruction data and data acquired by a biplane lower limb full-length X-ray acquisition system to form a fusion image in a three-dimensional space, and accurately calculating angles of a thighbone prosthesis, a tibia prosthesis and a lower limb force line in the three-dimensional space after the TKA operation.

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.

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 provides a finite angle CT iterative reconstruction method and system based on tight frame wavelet transform, and relates to the technical field of image processing, and the method comprises the steps: obtaining a pre-image and finite angle CT projection data of a to-be-detected object; total variation regularization constraint is carried out based on the pre-image of the to-be-detected object, and an initial optimization model is established; solving the initial optimization model to obtain a high-quality pre-CT image, and performing transformation by adopting tight frame wavelet transform to obtain a tight frame wavelet frequency domain coefficient; based on limited angle CT projection data and a to-be-detected object before reconstruction, a dual regularization CT reconstruction optimization model is established in combination with the tight frame wavelet transform frequency domain coefficient; and solving the dual regularization CT reconstruction optimization model, and outputting a reconstructed CT image. According to the method, the double regularization CT reconstruction optimization model of the tight frame wavelet transform domain sparse characteristic finite angle is established, strip artifacts and noise appearing in the finite angle CT image can be effectively restrained, the quality of the reconstructed image is greatly improved, and the clinical diagnosis quality is improved.

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.