Abstract: An information pushing method, apparatus, device, storage medium, and computer program product are provided, relating to the technical field of Internet applications. The method includes: extracting an information feature of candidate information, the information feature including a coarse-grained feature and a fine-grained feature; obtaining a first feature of the candidate information based on an intermediate feature obtained in a process of extracting the coarse-grained feature; obtaining a second feature of the candidate information based on the information feature and the intermediate feature; obtaining target information based on the first feature and the second feature; and pushing the target information.

Abstract: The present disclosure is related to removing scatter from a SPECT scan by utilizing a radiative transfer equation (RTE) method. An attenuation map and emission map are acquired for generating scatter sources maps and scatter on detectors using the RTE method. The estimated scatter on detectors can be removed to produce an image of a SPECT scan with less scatter. Both first-order and multiple-order scatter can be estimated and removed. Additionally, scatter caused by multiple tracers can be determined and removed.

Abstract: A projection dataset from a cone beam computed tomography (CBCT) can be input into a first set of one or more neural networks trained for at least one of saturation correction, truncation correction, and scatter correction. Reconstruction can then be performed on the output projection dataset to produce an image dataset. Thereafter, this image dataset can be input into a second set of one or more neural networks trained for at least one of noise reduction and artefact reduction, thereby generating a higher quality CBCT image.

Abstract: A method for cargo counting, a computer equipment, and a storage medium are provided in the disclosure. The method includes the following. Three-dimensional (3D) point cloud data of a set of cargoes within a preset placement region is obtained based on a cargo-counting instruction. Whether the set of cargoes are in a first placement state is determined according to the 3D point cloud data. Based on a determination that the set of cargoes are in the first placement state, a quantity of the set of cargoes is calculated.

Abstract: A method and apparatus for forklift pickup, a computer device, and a storage medium are provided in the disclosure. The method includes the following. Observational data of a truck parking area is obtained by observing the truck parking area. Point cloud data of at least one truck is obtained in the observational data, and point cloud data of each of the at least one carrier is obtained in the point cloud data of the at least one truck. A relative pose of each of at least one carrier is determined based on the point cloud data of each of the at least one carrier. A pickup priority is determined based on the point cloud data of each of the at least one carrier.

Abstract: Devices, systems, and methods obtain first radiographic-image data reconstructed based on a set of projection data acquired in a radiographic scan; apply one or more trained machine-learning models to the set of projection data and the first radiographic-image data to obtain a set of parameters for a scatter kernel; input the set of parameters and the set of projection data into the scatter kernel to obtain scatter-distribution data; and perform scatter correction on the set of projection data using the scatter-distribution data, to obtain a set of corrected projection data.

Abstract: The present invention is a composition comprising an aqueous admixture of a neutralized or partially neutralized aminosilane and an associative thickener, wherein the admixture has a pH of less than 10.2. The admixture is beneficial as an easy-to-use paint additive that provides stain removal, stain blocking, and corrosion resistance for coatings from low VOC paints.

Abstract: An apparatus and method to obtain input projection data based on radiation detected at a plurality of detector elements, reconstruct plural uncorrected images in response to applying a reconstruction algorithm to the input projection data, segment the plural uncorrected images into two or more types of material-component images by applying a deep learning segmentation network, generate output projection data corresponding to the two or more types of material-component images based on a forward projection, generate corrected multi material-decomposed projection data based on the generated output projection data corresponding to the two or more types of material-component images, and reconstruct the multi material-component images from the corrected multi material-decomposed projection data to generate one or more corrected images.

Abstract: An apparatus, method, and computer-readable medium for improving image quality of a medical volume.

Abstract: The disclosure relates to a method for warehouse storage-location monitoring, a computer device, and a storage medium. The method includes the following. Video data of a warehouse storage-location area is obtained, and a target image corresponding to the warehouse storage-location area is obtained based on the video data, where the warehouse storage-location area includes an area of a storage-location and an area around the storage-location. The target image is detected based on a category detection model, to determine a category of each object appearing in the target image, where the category includes at least one of: human, vehicle, or goods. A detection result is obtained by detecting a status of each object based on the category of each object, where the detection result includes at least one of: whether the human enters the warehouse storage-location area, vehicle status information, or storage-location inventory information.

Abstract: A method for determining material-cage stacking, a computer device, and a storage medium are provided. The method includes the following. A material-cage image is obtained by photographing a first stacking apparatus of a first material cage and a second stacking apparatus of a second material cage. The stacking apparatuses of the two material cages in the material-cage image can be recognized respectively with two detection models. The first stacking result is obtained by obtaining location information of the stacking apparatuses of the two material cages with the first detection model, and the second stacking result is obtained with the second detection model.

Abstract: The present disclosure is related to removing scatter from a SPECT scan by utilizing a radiative transfer equation (RTE) method. An attenuation map and emission map are acquired for generating scatter sources maps and scatter on detectors using the RTE method. The estimated scatter on detectors can be removed to produce an image of a SPECT scan with less scatter. Both first-order and multiple-order scatter can be estimated and removed. Additionally, scatter caused by multiple tracers can be determined and removed.

Abstract: An information processing method of an embodiment is a processing method of information acquired by imaging performed by a medical image diagnostic apparatus, the information processing method includes the steps of: acquiring noise data by imaging a phantom using a medical imaging apparatus; based on first subject projection data acquired by the imaging performed by a medical image diagnostic modality of a same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noise based on the noise data is added to the first subject projection data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and second subject projection data acquired by the imaging performed by the medical image diagnostic modality.

Abstract: The present disclosure relates to an apparatus for estimating scatter in positron emission tomography, comprising processing circuitry configured to acquire an emission map and an attenuation map, each representing an initial image reconstruction of a positron emission tomography scan, calculate, using a radiative transfer equation (RTE) method, a scatter source map of a subject of the positron emission tomography scan based on the emission map and the attenuation map, estimate, using the RTE method and based on the emission map, the attenuation map, and the scatter source map, scatter, and perform an iterative image reconstruction of the positron emission tomography scan based on the estimated scatter and raw data from the positron emission tomography scan of the subject.

Abstract: A method and apparatus is provided to perform dead-time correction in a positron emission tomography (PET) by estimating a full singles spectrum using a scatter model. The scatter model can use a Monte Carlo method, a radiation transfer equation method, an artificial neural network, or an analytical expression. The scatter model simulates scatter based on an emission image/map and an attenuation image/map to estimate Compton scattering. In the full singles spectrum, the singles counts with energies less than 511 keV are determined from the simulated scatter. The attenuation image can be generated based on X-ray computed tomography or based on applying a joint-estimation to PET data.

Abstract: The present disclosure relates to an apparatus for estimating scatter in positron emission tomography, comprising processing circuitry configured to acquire an emission map and an attenuation map, each representing an initial image reconstruction of a positron emission tomography scan, calculate, using a radiative transfer equation (RTE) method, a scatter source map of a subject of the positron emission tomography scan based on the emission map and the attenuation map, estimate, using the RTE method and based on the emission map, the attenuation map, and the scatter source map, scatter, and perform an iterative image reconstruction of the positron emission tomography scan based on the estimated scatter and raw data from the positron emission tomography scan of the subject.

Abstract: A method and apparatus is provided to perform dead-time correction in a positron emission tomography (PET) by estimating a full singles spectrum using a scatter model. The scatter model can use a Monte Carlo method, a radiation transfer equation method, an artificial neural network, or an analytical expression. The scatter model simulates scatter based on an emission image/map and an attenuation image/map to estimate Compton scattering. In the full singles spectrum, the singles counts with energies less than 511 keV are determined from the simulated scatter. The attenuation image can be generated based on X-ray computed tomography or based on applying a joint-estimation to PET data.

Abstract: X-ray scatter simulations to correct computed tomography (CT) data can be accelerated using a non-uniform discretization of the RTE, reducing the number of computations without sacrificing precision. For example, a coarser discretization can be used for higher-order/multiple-scatter flux, than for first-order-scatter flux. Similarly, precision is preserved when coarser angular resolution is used to simulate scatter within a patient, and finer angular resolution used for the scatter flux incident on detectors. Finer energy resolution is more beneficial at lower X-ray energies, and coarser spatial resolution can be applied to regions exhibiting less X-ray scatter (e.g., air and regions with low radiodensity). Further, predefined non-uniform discretization can be learned from scatter simulations on training data (e.g., a priori compressed grids learned from non-uniform grids generated by adaptive mesh methods).

Abstract: A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.

Abstract: A method and apparatus is provided that uses a deep learning (DL) network to improve the image quality of computed tomography (CT) images, which were reconstructed using an analytical reconstruction method. The DL network is trained to use physical-model information in addition to the analytical reconstructed images to generate the improved images. The physical-model information can be generated, e.g., by estimating a gradient of the objective function (or just the data-fidelity term) of a model-based iterative reconstruction (MBIR) method (e.g., by performing one or more iterations of the MBIR method). The MBIR method can incorporate physical models for X-ray scatter, detector resolution/noise/non-linearities, beam-hardening, attenuation, and/or the system geometry. The DL network can be trained using input data comprising images reconstructed using the analytical reconstruction method and target data comprising images reconstructed using the MBIR method.