13 results about "Scatter correction" patented technology

The application discloses a scattering correction method, a PET imaging method, a device, equipment and a storage medium. The scattering correction method comprises the following steps: obtaining TOF-PET data and an attenuation image of a target object during scanning; obtaining a back projection image of TOF information based on the TOF-PET data; obtaining a scattering estimation of TOF-PET imaging based on the back projection image and the attenuation image; and performing scattering correction on the TOF-PET data based on the scattering estimation of TOF-PET imaging. The application solves the technical problem of long time consumption of scattering correction in TOF-PET imaging in the prior art.

This invention discloses a non-destructive method for detecting strawberry freshness based on hyperspectral imaging and machine learning, belonging to the field of agricultural product quality testing technology. The method includes: acquiring hyperspectral images of strawberries in the 900-1700 nm wavelength range and extracting the average spectrum of the region of interest; performing multivariate scattering correction and Savitzky-Golay second-order derivative filtering preprocessing on the spectrum; using a genetic algorithm to screen a subset of characteristic wavelengths; and inputting the screened characteristic wavelengths into a pre-trained backpropagation neural network classification model to output the freshness grade determination result. This invention, through the technical path of "MSC and SG2 preprocessing—GA feature screening—BPNN classification," achieves rapid, non-destructive, and high-precision detection of strawberry freshness. The model's recognition accuracy exceeds 99% under storage conditions of 4℃ and 20℃, and can be widely applied to post-harvest grading, cold chain monitoring, and online sorting equipment for strawberries.

A multi-energy CT scatter correction method and apparatus based on a Boltzmann transport equation. The method comprises: preprocessing initial multi-energy projection data to be corrected of an object obtained after multi-energy CT scanning, to acquire initial corrected multi-energy projection data; performing material density conversion on the initial corrected multi-energy projection data to obtain material density distribution data; discretizing and labeling an actual scanning energy spectrum of the object during the multi-energy CT scanning, and determining an energy spectrum matrix in a pre-constructed labeled Boltzmann transport equation; calculating multi-energy scatter signal data of the object during the multi-energy CT scanning; and using the multi-energy scatter signal data to perform scatter correction on the initial multi-energy projection data to obtain actual multi-energy projection data.

The application discloses a soil heavy metal pollution rapid detection method based on hyperspectral imaging, relates to the field of soil pollution detection hyperspectral technology, and comprises the following steps: obtaining original hyperspectral data of soil in a target region, obtaining standard hyperspectral data through noise removal, light path scattering correction and spectral smoothing denoising pretreatment, extracting a heavy metal characteristic spectral response interval and dividing the heavy metal characteristic spectral response interval into multiple sub-bands, calculating spectral absorption depth, absorption peak symmetry and first-order differential characteristic values of each sub-band to form a multi-dimensional spectral feature set, constructing and training a nonlinear mapping model to obtain a heavy metal content prediction model, inversely calculating the multi-dimensional features of each pixel point by point, and outputting a soil heavy metal content spatial distribution map. The method refines the spectral feature extraction dimension, realizes pixel-level inversion and spatial distribution visualization, and improves the accuracy and spatialization efficiency of soil heavy metal pollution detection.

A method for correcting scatter in a reconstructed medical image comprises inputting the reconstructed volume 302 into a trained machine learning model 304 to output a correction volume 306. The corre

The application provides a diamond wear-resistant floor construction process optimization method, and belongs to the technical field of wind tunnel construction.The application analyzes stress distribution by establishing a three-dimensional finite element model, removes base layer floating slurry by using a vacuum water suction device, sprays a penetrating interface agent to improve interface bonding, monitors interface bonding quality by using an ultrasonic detection device, realizes quantitative diamond scattering by using a variable frequency scattering device equipped with machine vision and proportional-integral-derivative closed-loop control, generates a distribution density three-dimensional cloud map and identifies abnormal areas by using a laser radar scanning device, carries out supplementary scattering correction and screeding treatment on the abnormal areas, and realizes quality determination by constructing a Voronoi diagram to calculate uniformity evaluation index, thereby solving the technical problem that local area wear-resistant performance is not up to standard due to the difficulty in accurately controlling diamond wear-resistant floor scattering uniformity.

This application relates to an intelligent X-ray image detection method for defects in solid-state battery electrodes, comprising: S1, acquiring an X-ray transmission image of the solid-state battery electrode to be detected; establishing an X-ray attenuation physical model; using the X-ray attenuation physical model to perform scattering correction calculation and thickness non-uniformity compensation calculation on the X-ray transmission image to obtain a structural correction image; S2, performing layered structural segmentation calculation on the structural correction image to obtain structural feature maps corresponding to the electrode material layer, solid electrolyte layer, and interface transition region, respectively; S3, calculating interface continuity parameters, grayscale statistical parameters, and texture feature parameters from the structural feature maps to form a multi-dimensional numerical feature vector; converting the multi-dimensional numerical feature vector into numerical defect characterization parameters; S4, inputting the numerical defect characterization parameters into a pre-trained defect recognition model for classification and localization calculation, and outputting defect type and defect spatial location information; S5, outputting the electrode defect detection result.

In one aspect, the disclosure relates to a method for correcting distortions or aberrations in a multi-photon microscopy image, the method including at least: (a) illuminating the sample; (b) imaging the illuminated sample with an imaging device having one or more detection elements that receive a signal from the sample; and (c) processing the image using a scattering correction and a phase correction. The phase correction can be a low-order deformable mirror adaptive optics correction with a hardware component and a software component. In some aspects, a spatial light modulator can be employed as a low-order phase correcting device. The scattering correction can be intensity scattering correction, phase scattering correction, or both, and can be performed with a high-order digital micromirror device with binary intensity modulation or a phase or intensity high-order spatial light modulator device. Also disclosed are methods of imaging complex tissue in living subjects.

This invention discloses a PET scattering correction and image reconstruction method based on time-of-flight (TOF) and GPU, belonging to the field of medical imaging technology. This invention uses a TOF histogram image as the initial radiation source distribution for scattering estimation for scattering correction. The computational overhead of the TOF histogram image is extremely low, and the initial scattering estimation can be executed in parallel with the sensitivity image calculation process, thereby effectively improving the computational efficiency of the overall reconstruction process. This invention has strong flexibility; it can be modularly integrated into EM-type reconstruction algorithms without requiring significant modifications to existing reconstruction frameworks, facilitating implementation. An atomically driven multi-GPU parallel MC computing framework solves the load balancing problem in multi-GPU computing, fully leveraging the performance advantages of multi-GPU computing resources. While meeting clinical needs in terms of computational efficiency, it can accurately estimate the scattering distribution, including multiple scattering coincidence events. The method of this invention is simple and easy to understand. This invention is applied to PET scattering correction and image reconstruction.

The application provides a contrast enhancement method for improving X-ray imaging quality of thick-wall GIS components, and belongs to the technical field of X-ray inspection of GIS components. After a unified three-dimensional imaging coordinate system is established, a Compton scattering point spread function is calculated by using a Monte Carlo scattering estimator, and a scattering correction image is obtained by frequency domain separation through a scattering deconvolution network. The scattering correction image is input into a pyramid attention fusion network to perform multi-scale feature extraction and defect saliency enhancement. A thermal diffusion contrast adjustment algorithm based on a local gradient structure tensor is performed on a preliminary enhanced image. A graph cut multi-region segmentation algorithm is used to independently optimize the contrast parameters for homogeneous regions. A closed-loop optimization is formed by adjusting the pyramid feature fusion weight coefficient according to the global contrast evaluation value feedback, thereby solving the technical problems of low X-ray imaging contrast of thick-wall GIS components and the inability to adaptively enhance different regions.

The application discloses a kind of oral cavity CBCT projection domain scattering suppression method and system based on double-flow deep learning network, it is related to oral cavity CBCT scattering suppression technical field, including creating double-flow parallel network architecture, inference process is carried out based on double-flow parallel process;Design high-fidelity training data preparation subsystem.The application improves the precision and image fidelity of scattering correction;Innovation uses size FOV physical scanning to obtain high-fidelity paired training data, ensures that model learns real scattering physical law, greatly enhances generalization and clinical robustness;The whole scheme does not need any hardware modification, only through software deployment can realize the soft upgrade of existing CBCT equipment, with very low implementation cost and wide popularization potential;The high-quality projection data finally output effectively improves bone density quantitative accuracy, tissue contrast and microstructure display capability, improves the diagnostic value and clinical reliability of CBCT in implantation, periodontal disease and apical lesion and other key diagnosis and treatment scenarios.

A system for the automated detection of lung anomalies using deep learning-based computed tomography image reconstruction, comprising: a computed tomography unit configured to generate raw projection data corresponding to a thoracic region of a subject; a preprocessing unit operationally coupled to the computed tomography acquisition unit and configured to convert the raw projection data into normalized projection representations through logarithmic transformation, scatter correction, and geometric calibration;a reconstruction processor that is communicatively linked to the preprocessing unit and configured to reconstruct volumetric image data from the normalized projection representations using a trained deep neural network architecture consisting of a multitude of convolutional layers arranged for feature extraction in projection space, domain transformation, and image space refinement; a segmentation processor that is operationally coupled to the reconstruction processor and configured to segment the reconstructed volumetric image data into lung regions and subregions based on learned spatial features; a feature extraction processor configured to derive spatial, morphological, and textual features at various scales from the segmented lung regions;a classification processor that is operationally coupled to the feature extraction processor and configured to identify and classify lung anomalies based on the extracted features using a trained neural network; and a visualization unit configured to display detected anomalies and generate diagnostic outputs, wherein the reconstruction processor and the classification processor are further configured to work together so that the classification results influence the refinement of the reconstruction.