5 results about "Filtered backprojection" patented technology

The application discloses a kind of based on the sparse helical CT reconstruction method, system, equipment, medium and program product of back projection tensor interpolation, belong to computer tomography (CT) imaging technical field, specifically includes the following steps: generating the back projection tensor data under sparse angle, the back projection tensor data indicates the intermediate data of back projection process in filter back projection algorithm;Interpolation is carried out to the back projection tensor data of the sparse angle by depth learning network, generates the back projection tensor data of full angle;Image reconstruction is carried out based on the back projection tensor data of full angle, and the final CT image is obtained.The reconstruction method of the application suppresses the generation of strip artifact from the source and directly repairs the key intermediate data for reconstruction, which can better retain and restore the true anatomical structure information than post-processing in the image domain, avoids excessive smoothing or filtering, and has generalization ability, suitable for different CT scanning geometry.

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 PCB blind via defect detection technology and discloses an X-ray-based method for detecting PCB blind via defects. The method includes the following steps: first, the PCB is irradiated with X-rays; then, data is acquired and an initial image is established using CT projection; subsequently, the initial image is reconstructed using filtered back projection to obtain a reconstructed image; then, the blind via region is extracted using the reconstructed image; next, a two-stream neural network architecture is constructed based on a deep residual network; finally, the blind via defect is detected and classified using the two-stream neural network architecture. This invention, through a two-stream neural network architecture constructed based on a deep residual network, comprehensively utilizes geometric and material features, enabling comprehensive and accurate detection and classification of various defects in PCB blind vias, such as cracks, wrinkles, dents, and voids. This improves the efficiency and accuracy of defect detection, helps to promptly identify quality problems in the PCB manufacturing process, and ensures product quality.

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.

The application discloses a zooming static CT system and an image reconstruction method, wherein the system comprises a cylindrical detector array arranged in an arc and an X-ray source array arranged in a preset shape; the X-ray source array and the detector array remain static during scanning; projection data is acquired at different angles by sequentially emitting beams of different X-ray sources; the distance from the X-ray source to the detector is variable; a data acquisition system is used for controlling the detector to acquire data; a control system controls the emission of the X-ray source and the acquisition of data by the detector; and an image processing system processes acquired image projection data by using an image reconstruction algorithm. The image reconstruction algorithm in the embodiment of the application adopts a weighted filter back-projection reconstruction algorithm, which can optimize the system geometry, improve the efficiency and accuracy of the reconstructed image, and improve the applicability and reliability of the system.