21 results about "Ct reconstruction" patented technology

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.

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.

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 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 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.

This invention provides a low-dose CT reconstruction method assisted by pre-log domain Voronoi decomposition. The method includes obtaining a pre-log sinusoidal graph of low-dose CT, performing Voronoi decomposition on the pre-log sinusoidal graph using the K-means clustering algorithm to obtain several feature clusters, mapping each feature cluster to its corresponding latent space to obtain a multi-channel input feature map, inputting the multi-channel input feature map into a pre-trained diffusion transformer model for iterative denoising and optimization to obtain optimized cluster features, fusing and performing logarithmic transformation on the optimized cluster features to obtain a post-log sinusoidal graph, and reconstructing the post-log sinusoidal graph using a filtered back-projection algorithm to obtain a high-quality CT image. This invention solves the problems of large dynamic range, uneven gradient, and progressively amplified noise in the pre-log domain by decoupling features from the pre-log sinusoidal graph, modeling the latent space, and optimizing the diffusion transformer, thus achieving high-fidelity low-dose CT image reconstruction.

This invention relates to the field of X-ray computed tomography imaging technology, and particularly to a multi-source linear CT reconstruction method and system based on projection fusion and depth unfolding networks. First, using at least three X-ray sources in conjunction with a single area array detector, the object under examination is controlled to translate linearly, and multiple sets of projection data sequences are acquired time-divisionally. Second, a mapping model is constructed based on spatial geometric relationships, fusing and rearranging the projection data into a two-dimensional sine graph with missing angles. Next, a cascaded network of sine graph completion and image reconstruction is constructed. First, a completion submodule fills in the missing data in the sine graph while retaining the true measurement values. Then, an intelligent reconstruction submodule with a depth unfolding architecture containing multiple iteration stages, combined with a weight-sharing backprojection operator and a residual regularization module, updates the image. Finally, an end-to-end joint training is performed using a composite loss function of the sine graph domain and the image domain. This invention achieves high-fidelity, artifact-free, and rapid reconstruction of CT tomographic images, balancing imaging quality and efficiency.

This invention discloses a high-resolution dual-energy spectral CT reconstruction method based on prior information, comprising the following steps: Step 1, a high-resolution dual-energy spectral CT scanning model; Step 2, an iterative dual-energy spectral reconstruction algorithm based on BPF; Step 3, an iterative CT reconstruction algorithm based on prior information; Step 4, numerical experiments and result analysis. This high-resolution dual-energy spectral CT reconstruction method based on prior information designs a hybrid dual-detector CT scanning system, studies the combination of the material differentiation advantages of dual-energy spectral CT with the high-resolution advantages of microscopic CT, and proposes an iterative CT reconstruction algorithm based on prior information. This algorithm can perform high-resolution energy spectral reconstruction on line-to-line cards. First, the energy spectral projection data is reconstructed to obtain a low-resolution base image. Then, the base image is used as prior information and optimized and combined with the high-resolution projection data to finally obtain a high-resolution base image, thereby avoiding the missed detection of minute fractures.

This invention discloses a method for generating three-dimensional structures of rice stems using CT based on GAN networks, belonging to the field of agricultural data processing technology. The method includes the following steps: S1: collecting and processing rice stem data; S2: constructing a rice stem CT dataset based on the processed rice stem data, including multiple low-resolution three-dimensional structures, top and bottom cross-sectional CT slices, and high-resolution three-dimensional structures; S3: constructing a rice stem CT three-dimensional structure generation model, and training and testing the model based on the constructed rice stem CT dataset; S4: generating rice stem CT three-dimensional structures based on the tested model. This invention effectively solves the problem that traditional rice stem CT reconstruction methods struggle to accurately recover the fine internal structure of the stem under sparse viewpoint conditions, thus better meeting real-world application needs.

The present application relates to a kind of luggage CT reconstruction area determination method, belong to safety system detection, solve the problem of the waste of computer's computing resources caused by the large area of blank area in the CT reconstruction area in prior art.The method comprises the following steps: step 1: in the projection data used for reconstructing tomographic data M, the projection data between the rotation angle from s to s+m-1 is extracted, denoted as p1;Step 2: the projection data of p1 is preprocessed to obtain p2;Step 3: p2 is processed to determine the reconstruction area;Wherein, m is the angle number required for reconstructing a single tomographic;Wherein, s is the starting angle.The size of image reconstruction area is adaptively determined according to the actual size of the detection package.

This application provides a dual-view CT reconstruction method based on geometrically guided diffusion and frequency domain awareness. Applied to the field of computer vision technology, this method acquires anteroposterior and lateral X-ray projection data of the target object; it then inputs these data into a dual-domain residual network based on discrete wavelet transform for feature extraction, obtaining a first projection feature map and a second projection feature map; the two-dimensional features of the first and second projection feature maps are projected onto three-dimensional space to obtain aligned three-dimensional spatial features; features from three adjacent layers of three-dimensional space are extracted to form a feature set, and these extracted features are then stitched and fused along the channel dimension to obtain a contextual condition feature map; this contextual condition feature map is used as a priori guided input diffusion model for analysis and processing to generate high-resolution three-dimensional CT volume data. This method improves the accuracy of medical image reconstruction under conditions of few samples.

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 relates to the technical field of medical image processing, in particular to a CT image reconstruction method based on a progressive texture perception diffusion model, and the method comprises the steps: inputting a to-be-reconstructed low-dose CT image or a sparse view angle image and projection image data into a trained progressive texture perception diffusion model, and outputting a reconstructed CT image; the progressive texture perception diffusion model carries out preliminary reconstruction on a to-be-reconstructed low-dose CT image or a sparse view angle image to obtain a low-frequency structure of the image; based on the low-frequency structure, high-frequency details are generated through conditional Schrodinger bridge diffusion network iteration, multi-scale high-frequency features in the high-frequency details are extracted, and a reconstructed CT image is generated based on the low-frequency structure of the image and the multi-scale high-frequency features. The invention provides a CT (Computed Tomography) reconstruction method based on coarse-fine segmentation, so that artifacts are removed and more tiny tissue textures are reserved on the premise of reserving global structures such as key bones and blood vessels.

This invention relates to an X-ray-based super-resolution CT imaging method, comprising: an industrial CT system performing a first CT scan on the object under optimal detection conditions, and sequentially acquiring a projection image of the object at intervals; moving the X-ray source along a direction parallel to the detector by a distance *d*, and performing a second CT scan on the object using the same detection process as described above; translating the X-ray source upwards along the focal axis of the first and second CT scans, and performing a third CT scan on the object using the same detection process as described above; adding the projection images of the object acquired during the three CT scans to obtain a final super-resolution projection image; mapping the final super-resolution projection image to a 16-bit image, and then performing CT reconstruction on the 16-bit image to obtain a CT image. This method improves the detection spatial resolution under hardware limitations.

This invention provides a four-dimensional cone-beam CT reconstruction method based on differential Transformer motion compensation, comprising: dividing acquired continuous projection data into multiple time-phase groups according to respiratory motion signals; selecting at least one frame in each group as a base reference frame, dividing each group of images into multiple local image blocks and performing feature encoding to obtain the original feature vector of each block; calculating the difference feature vector between the original feature vector of the non-base reference frame and the original feature vector of the corresponding base reference frame; jointly encoding the original feature vector, the difference feature vector, and the angle embedding vector to form an enhanced feature vector, which is input into a Transformer network for feature modeling and motion compensation; introducing motion-aware regularization constraints to distinguish between moving and stationary regions; decoding each group of features, reconstructing the corresponding three-dimensional images of each group, and combining them to form a complete four-dimensional cone-beam CT image sequence. The method of this invention achieves high signal-to-noise ratio, clear, and morphologically accurate 4D-CBCT image reconstruction.

The present application relates to the technical field of computer tomography, in particular to a limited-angle CT image optimization reconstruction method and system based on noise-reduction diffusion regularization, a diffusion model is pre-trained on a given CT image dataset and used as an implicit image prior; a weighted noise predicted by the diffusion model is deducted from a reconstruction image of current iteration to obtain an estimated image; based on observed projection data and CT imaging physical process, a separable quadratic surrogate algorithm is used to update the estimated image for data consistency to obtain a physical update term; a first-order diffusion probability model solver is used to weight sample the physical update term and a predicted noise term output by the diffusion model to obtain an updated CT image, completing one iteration; the process is repeated until a limited-angle CT reconstruction image meeting accuracy requirements is obtained. The present application greatly improves the reconstruction accuracy of limited-angle low-dose CT images and effectively suppresses artifacts and retains details.

The application relates to a casting internal defect diagnosis method and device, a storage medium and an electronic equipment, and relates to the technical field of defect diagnosis. The method comprises the following steps: acquiring geometric feature information of a three-dimensional digital model corresponding to a to-be-diagnosed casting; determining a CT scanning path corresponding to the to-be-diagnosed casting according to the geometric feature information; performing CT scanning on the to-be-diagnosed casting according to the CT scanning path to obtain X-ray projection data; inputting the X-ray projection data into a preset defect identification model to obtain at least one actual internal defect of the to-be-diagnosed casting; determining initial positions of the actual internal defects by using a CT reconstruction algorithm; and adjusting and optimizing the initial positions according to a historical internal defect that has occurred in a historical casting and a historical region where the historical internal defect exists when a single casting parameter dimension is abnormal to obtain corresponding final positions. The application has the effect of improving the positioning accuracy of the internal defects of the casting.

The application provides a low-dose CT image reconstruction method and system based on flow matching, and the method comprises the following steps: estimating a signal-related noise parameter of a low-dose CT image through a physical noise encoder, and constructing a noise prior distribution conforming to the physical law of CT imaging; embedding an anatomical transport module in a U-Net coding path, estimating an anatomical structure displacement field by using adjacent slice features, and realizing cross-slice feature alignment; introducing an interlayer staggered attention mechanism at a bottleneck layer, and fusing global anatomical information of adjacent slices; and generating a reconstructed image from the noise prior by ordinary differential equation integration based on a flow matching framework. Through the introduction of a physical noise model and a three-dimensional anatomical continuity constraint, high fidelity and high efficiency are realized in low-dose CT reconstruction, and the method can be widely applied to clinical CT imaging systems.

The application provides a pre-log domain Voronoi decomposition assisted low-dose CT reconstruction method, which comprises the following steps: obtaining a pre-log sinogram of low-dose CT; performing Voronoi decomposition on the pre-log sinogram by using a K-means clustering algorithm to obtain a plurality of feature clusters; mapping each feature cluster to a corresponding hidden space to obtain a multi-channel input feature map; inputting the multi-channel input feature map into a pre-trained diffusion transformer model for iterative denoising and optimization to obtain optimized clustering features; fusing and logarithmically transforming the optimized clustering features to obtain a post-log sinogram; and reconstructing the post-log sinogram by using a filtered back-projection algorithm to obtain a high-quality CT image. The application solves the problems of large dynamic range, uneven gradient and noise amplification at each stage in the pre-log domain by performing feature decoupling, hidden space modeling and diffusion transformer optimization on the pre-log sinogram, and realizes high-fidelity low-dose CT image reconstruction.

This invention discloses a high spatial and spectral resolution dual-mode imaging system and method for energy-spectral CT and optical imaging. The system uses an X-ray energy-spectral detector to acquire multi-level X-ray projection data; a hyperspectral camera is used to acquire multi-band fluorescence data and fluorescence images. The method includes correcting the X-ray projection at each energy level, performing three-dimensional reconstruction on the corrected multi-level projection data, volume rendering of the reconstructed three-dimensional data to obtain the three-dimensional contour of the object, mapping the fluorescence data onto the three-dimensional contour, and for each spectral band, constructing and reconstructing an accurate photon transport forward model based on the accurate material decomposition results obtained from the multi-level CT reconstruction, referencing the scattering and absorption coefficients of different materials, and based on the third-order simplified spherical harmonic equation, performing three-dimensional registration and image fusion of the dual-mode images to obtain a dual-mode fused image. The dual-mode fused image obtained by this invention has high spatial and spectral resolution.