Abstract: A non-spectral computed tomography scanner includes a radiation source configured to emit x-ray radiation, a detector array configured to detect x-ray radiation and generate non-spectral data, and a memory configured to store a spectral image module that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

Abstract: A spectral computed tomography imaging system (102) includes a radiation source (112) configured to emit x-ray radiation and a detector array (114) configured to detect x-ray radiation and generate spectral data. The spectral imaging system further includes a memory (134) configured to store a virtual non-contrast image enhancing module (136) that includes computer executable instructions including a neural network trained to produce image quality enhanced virtual non-contrast images. The neural network is trained with training spectral data and training non-contrast-enhanced images generated from a non-contrast-enhanced scan. The spectral imaging system further includes a processor (132) configured to process the spectral data with the trained neural network to produce the image quality enhanced virtual non-contrast images.

Abstract: An imaging system includes a computed tomography (CT) imaging device (10) (optionally a spectral CT), an electronic processor (16, 50), and a non-transitory storage medium (18, 52) storing a neural network (40) trained on simulated imaging data (74) generated by Monte Carlo simulation (60) including simulation of at least one scattering mechanism (66) to convert CT imaging data to a scatter estimate in projection space or to convert an uncorrected reconstructed CT image to a scatter estimate in image space. The storage medium further stores instructions readable and executable by the electronic processor to reconstruct CT imaging data (12, 14) acquired by the CT imaging device to generate a scatter-corrected reconstructed CT image (42). This includes generating a scatter estimate (92, 112, 132, 162, 182) by applying the neural network to the acquired CT imaging data or to an uncorrected CT image (178) reconstructed from the acquired CT imaging data.

Abstract: A non-spectral computed tomography scanner includes a radiation source configured to emit x-ray radiation, a detector array configured to detect x-ray radiation and generate non-spectral data, and a memory configured to store a spectral image module that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

Abstract: The present disclosure relates to a combined treatment method for surface modification of fumed silica, which comprises the following steps: (1) two sets of modification devices are used to jointly treat fumed silica; the fumed silica is modified with a modifier in the reaction furnace of each set of modification devices to obtain two groups of modified fumed silica and exhaust gas respectively; (2) the exhaust gas obtained in step (1) is separated respectively to obtain unreacted modifier and by-products, and the obtained by-products are input into the reaction furnace of the other set of modification devices as reaction assistants to participate in the modification reaction; and the obtained unreacted modifiers are returned to the reaction furnace of the original modification device for repeated use.

Abstract: A non-spectral computed tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (114) configured to detect x-ray radiation and generate non-spectral data, and a memory (134) configured to store a spectral image module (130) that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor (126) configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

Abstract: The present disclosure provides a circuit substrate, a method for manufacturing the same, a display substrate and a tiled display device. The circuit substrate includes: a base substrate; a driving circuit on the base substrate; and conductive connection portions. A plurality of grooves is defined in a lateral side of the base substrate; each of the plurality of grooves extends through a top surface and an opposite bottom surface of the base substrate. The driving circuit includes signal lines on the top surface of the base substrate and signal-line leads on the bottom surface of the base substrate. The plurality of conductive connection portions are corresponding to the plurality of grooves in a one-to-one manner; at least one part of the conductive connection portion is in the corresponding groove. The conductive connection portion is connected with the corresponding signal line and the corresponding signal-line lead, respectively.

Abstract: A computer tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (116) configured to detect x-ray radiation and generate a signal indicative thereof, and a reconstructor (118) configured to reconstruct the signal and generate sequential spares time line perfusion volumetric image data. The computer tomography scanner further includes a processor (132) configured to process the sequential spares time line perfusion volumetric image data using a trained neural network of a perfusion data enhancing module (136) to produce sequential dense time line perfusion volumetric image data.

Abstract: A non-spectral computed tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (114) configured to detect x-ray radiation and generate non-spectral data, and a memory (134) configured to store a spectral image module (130) that includes computer executable instructions including a neural network trained to produce spectral volumetric image data. The neural network is trained with training spectral volumetric image data and training non-spectral data. The non-spectral computed tomography scanner further includes a processor (126) configured to process the non-spectral data with the trained neural network to produce spectral volumetric image data.

Abstract: An X-ray imaging device (10, 100) is configured to acquire an uncorrected X-ray image (30). An image reconstruction device comprises an electronic processor (22) and a non-transitory storage medium (24) storing instructions readable and executable by the electronic processor to perform an image correction method (26) including: applying a neural network (32) to the uncorrected X-ray image to generate a metal artifact image (34) wherein the neural network is trained to extract residual image content comprising a metal artifact; and generating a corrected X-ray image (40) by subtracting the metal artifact image from the uncorrected X-ray image.

Abstract: A spectral computed tomography imaging system (102) includes a radiation source (112) configured to emit x-ray radiation and a detector array (114) configured to detect x-ray radiation and generate spectral data. The spectral imaging system further includes a memory (134) configured to store a virtual non-contrast image enhancing module (136) that includes computer executable instructions including a neural network trained to produce image quality enhanced virtual non-contrast images. The neural network is trained with training spectral data and training non-contrast-enhanced images generated from a non-contrast-enhanced scan. The spectral imaging system further includes a processor (132) configured to process the spectral data with the trained neural network to produce the image quality enhanced virtual non-contrast images.

Abstract: The present disclosure provides a circuit substrate, a method for manufacturing the same, a display substrate and a tiled display device. The circuit substrate includes: a base substrate; a driving circuit on the base substrate; and conductive connection portions. A plurality of grooves is defined in a lateral side of the base substrate; each of the plurality of grooves extends through a top surface and an opposite bottom surface of the base substrate. The driving circuit includes signal lines on the top surface of the base substrate and signal-line leads on the bottom surface of the base substrate. The plurality of conductive connection portions are corresponding to the plurality of grooves in a one-to-one manner; at least one part of the conductive connection portion is in the corresponding groove. The conductive connection portion is connected with the corresponding signal line and the corresponding signal-line lead, respectively.

Abstract: An imaging system includes a computed tomography (CT) imaging device (10) (optionally a spectral CT), an electronic processor (16, 50), and a non-transitory storage medium (18, 52) storing a neural network (40) trained on simulated imaging data (74) generated by Monte Carlo simulation (60) including simulation of at least one scattering mechanism (66) to convert CT imaging data to a scatter estimate in projection space or to convert an uncorrected reconstructed CT image to a scatter estimate in image space. The storage medium further stores instructions readable and executable by the electronic processor to reconstruct CT imaging data (12, 14) acquired by the CT imaging device to generate a scatter-corrected reconstructed CT image (42). This includes generating a scatter estimate (92, 112, 132, 162, 182) by applying the neural network to the acquired CT imaging data or to an uncorrected CT image (178) reconstructed from the acquired CT imaging data.

Abstract: Embodiments described herein provide a method for detecting motion. At a first wireless communication device capable of wirelessly communicating with a second wireless communication device, channel state information (CSI) for the wireless channel between the first and second wireless communication devices is received. A nulling matrix is calculated based on the received CSI. The calculated nulling matrix is applied to a plurality of short packets scheduled to be periodically transmitted from the first wireless communication device to the second wireless communication device. The first wireless communication device receives, from the second wireless communication device, received signal strength information (RSSI) determined for each of the transmitted plurality of short packets. The method further includes detecting motion based on detecting a change in the RSSI received from the second wireless communication device.

Abstract: Embodiments described herein provide a method for detecting motion. At a first wireless communication device capable of wirelessly communicating with a second wireless communication device, channel state information (CSI) for the wireless channel between the first and second wireless communication devices is received. A nulling matrix is calculated based on the received CSI. The calculated nulling matrix is applied to a plurality of short packets scheduled to be periodically transmitted from the first wireless communication device to the second wireless communication device. The first wireless communication device receives, from the second wireless communication device, received signal strength information (RSSI) determined for each of the transmitted plurality of short packets. The method further includes detecting motion based on detecting a change in the RSSI received from the second wireless communication device.

Abstract: A hydrocarbon exploration method is disclosed for developing a model of at least one effective material property of a subsurface reservoir as a function of the composition and structure of the reservoir rock. In one embodiment, the method comprises: obtaining a 3D image (102) of a rock sample characteristic of a reservoir of interest (101); segmenting the 3D image into compositional classes (103) based on similarities in mineralogy, structure and spatial distribution; selecting a model (105) that relates an effective material property of interest to the volume fractions of each compositional class; and determining the parameters of the model (106). The model may be used to assess the commercial potential of the subsurface reservoir (107).

Abstract: A hydrocarbon exploration method is disclosed for generating anisotropic resistivity models of a subsurface reservoir from seismic and well data using a rock physics model. In one embodiment, the method comprises: selecting wells within a region of interest (101); obtaining a plurality of rock properties (102) and adjusting selected rock parameters (103) in the calibration of the rock physics model at the well locations; inverting porosity and shale content from seismic data (107); propagating the calibrated rock physics model to the region of interest (109) and calculating effective resistivity for the entire region of interest (109). The inventive method also provides for analyzing the uncertainty associated with the prediction of the resistivity volume.

Abstract: Method for predicting physical properties of a source rock formation wherein an inclusion-based (103) mathematical rock physics model (101) is constructed that treats organic matter as solid inclusions, solid background, or both, and relates anisotropic elastic and electric properties of source rock to in-situ rock and fluid properties (102). The model is calibrated with well log data and may be used to forward model calculate effective anisotropic elastic (104.1) and electrical (104.2) properties of the source rock formation, or by inversion (441-442) of sonic and resistivity log data to calculate total organic carbon (423) in terms of a difference (421) between elastic and electrical properties of the source rock.

Abstract: Method for modeling anisotropic elastic properties of a subsurface region comprising mixed fractured rocks and other geological bodies. P-wave and fast and slow S-wave logs are obtained, and an anisotropic rock physics model of the subsurface region is developed (21). P- and fast and slow S-wave logs at the well direction are calculated using a rock physics model capable of handling fractures and other geological factors (22). Calculated values are compared to measured values in an iterative model updating process (23). An upscaled ID model is developed by averaging elastic properties in each layer using an upscaling theory capable of handling at least orthorhombic anisotropy (24). The ID model may be used to generate synthetic seismic response for well ties or AVO modeling (25). Further, a method is disclosed for estimating anisotropy parameters from P- and fast/slow S-wave logs from one or more deviated wells.