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: The present disclosure relates to a natural water-soluble plant compound with antidepressant and sleep-promoting effects, and a preparation method and use thereof. In the present disclosure, the preparation method includes: immersing Fructus lycii, green tea, spina date seed, lily, sugarcane, peanut stem, Euglena, and Chlorella in water, and conducting ultrasonication and first solid-liquid separation to obtain a plant extract; mixing the plant extract with an organic extractant to extract an extracted aqueous phase; removing a solvent of the extracted aqueous phase to obtain a crude extract; dispersing the crude extract into water, and conducting second solid-liquid separation to obtain a purified water extract; and drying the purified water extract to obtain the natural water-soluble plant compound.

Abstract: This application describes a method and system for medical 3D imaging reconstruction based on limited-angle 2D image acquisition, which only requires 30˜50% of the data required by traditional scanner-based 3D reconstruction. This approach significantly reduces the radiation dose requirement and improves clinical efficiency. An example method includes: capturing a plurality of 2-dimensional (2D) projections of a target that cover a limited angle of the target; calibrating the plurality of 2D projections of the target to obtain a plurality of calibrated 2D projections; generating a sinogram based on the plurality of calibrated 2D projections; inputting the sinogram into a dual-domain neural network to obtain a 3D volumetric image of the target that is geometrically corrected and calibrated.

Abstract: Disclosed herein are methods and dose metrics for evaluating the performance and/or quality of a radiotherapy treatment plan and/or radiotherapy system. Dose metrics may be used in a quality assurance (QA) procedure to evaluate (e.g., test, confirm, validate, etc.) the quality of a treatment plan and/or radiotherapy system before radiation is delivered to a patient. One example of a dose metric is a coverage margin metric. The coverage margin metric may provide a way to quantify how tumor motion affects dose coverage and facilitate the determination of whether the tumor contour defined in the treatment plan has a margin that is sufficient to compensate for tumor motion and/or positional changes during radiation therapy.

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).