Abstract: The present disclosure relates to a method for fabricating a touch substrate, a touch substrate and a touch device. The method includes: forming, through a splicing exposure process, a first electrode layer including a metal strip in an edge region thereof and a first metal mesh pattern connected with the metal strip; forming, on one side of the first electrode layer and through a splicing exposure process, a second electrode layer including a metal strip in an edge region thereof and a second metal mesh pattern connected with the metal strip and insulated from the first metal mesh pattern, the metal strip of the first electrode layer directly contacting the metal strip of the second electrode layer to form a metal stack; and forming a wire electrically connected with one of the first and metal mesh patterns of the first and second electrode layers by using the metal stack.

Abstract: A downhole throttling device based on wireless control includes an inlet nozzle, a throttling assembly, an electrical sealing cylinder, a gas guide cylinder, a lower adapter sleeve, an end socket, a female sleeve, and electrical components. The inlet nozzle is connected to the throttling assembly, the throttling assembly is connected to the electrical sealing cylinder and the gas guide cylinder, the electrical sealing cylinder and the gas guide cylinder are both connected to the lower adapter sleeve, the lower adapter sleeve is respectively connected to the end socket and the female sleeve, and the electrical components are arranged in the electrical sealing cylinder. A throttling effect is achieved by detecting the temperature and pressure in a tube by a temperature/pressure sensor in the electrical components and controlling a motor to rotate a movable valve in the throttling assembly by a circuit control assembly, thereby achieving wireless control over downhole throttling.

Abstract: Implementations of this specification provide prediction methods and apparatuses for adjusting computing power. One method comprises receiving a prediction request, wherein the prediction request comprises a sample to be tested, determining a computing power coefficient allocated to the prediction request, wherein the computing power coefficient indicates a proportion of hardware computing power resources allocated to the prediction request to total hardware computing power resources needed for a neural network model to run on a computing platform, determining k sub-networks inn sub-networks of the neural network model to be used for a present time based on the computing power coefficient, where n>2, and inputting the sample to be tested to the k sub-networks to obtain a prediction result.

Abstract: A driving backplane, a manufacturing method thereof, a light-emitting substrate are provided. In the driving backplane, a first conductive layer on a substrate; a first organic film layer is between the first conductive layer and second conductive layer, the first organic film layer has first via holes; a first inorganic film layer is on one side of first organic film layer away from the substrate; the second conductive layer is on one side of first inorganic film layer away from first organic film layer, the second conductive layer and the first conductive layer are connected through the first via holes; orthographic projections of the first conductive layer and second conductive layer have an overlapping region, the overlapping region includes a region including orthographic projection of the first via holes, and a first region, an orthographic projection of the first inorganic film layer covers parts of the first region.

Abstract: Systems and methods for recreating images from undersampled MRI image data includes capturing undersampled MRI data and enhancing it with multiple cascading stages, each including a data consistency block in parallel to a convolutional neural network (CNN). The data consistency block adjusts each input image by a sensitivity map and performs hard replacement of acquired lines in k-space into the image. The CNN estimates a regularizer term that attempts to minimize a difference between a true image and the output of the data consistency block. At each stage, the output of CNN and data consistency block are added to create a set of output images that feed into the next stage.

Abstract: Systems and methods for recreating images from undersampled magnetic resonance imaging (MRI) image data remove artifacts from the undersampling. Using trained transformer networks, undersampled k-space data can be acquired and the networks can predict additional projected radial MRI k-space data from the undersampled radial MRI k-space data. Images with fewer artifacts can then be generated.

Abstract: A method for acquiring magnetic resonance imaging data with respiratory motion compensation using one or more motion signals includes acquiring a plurality of gradient-delay-corrected radial readout views of a subject using a free-breathing multi-echo pulse sequence, and sampling a plurality of data points of the gradient-delay-corrected radial readout views to yield a self-gating signal. The self-gating signal is used to determine a plurality of respiratory motion states corresponding to the plurality of gradient-delay-corrected radial readout views. The respiratory motion states are used to correct respiratory motion bias in the gradient-delay-corrected radial readout views, thereby yielding gradient-delay-corrected and motion-compensated multi-echo data. One or more images are reconstructed using the gradient-delay-corrected and motion-compensated multi-echo data.

Abstract: Systems and methods for generative adversarial networks (GANs) to remove artifacts from undersampled magnetic resonance (MR) images are described. The process of training the GAN can include providing undersampled 3D MR images to the generator model, providing the generated example and a real example to the discriminator model, applying adversarial loss, L2 loss, and structural similarity index measure loss to the generator model based on a classification output by the discriminator model, and repeating until the generator model has been trained to remove the artifacts from the undersampled 3D MR images. At runtime, the trained generator model of the GAN can be generate artifact-free images or parameter maps from undersampled MRI data of a patient.

Abstract: A system and method for performing accelerated k-space shift correction calibration scans for non-Cartesian trajectories is provided. The method can include applying an MRI sequence, performing a calibration scan based on the MRI sequence using the non-Cartesian trajectory to acquire k-space shift data, wherein one or more partitions are skipped during the calibration scan, interpolating the skipped one or more partitions using the k-space shift data from adjacent partitions, and calibrating the MRI system using the k-space shift data and the interpolated k-space shift data. In some embodiments, an acceleration factor Acc can be defined and the calibration scan acquires k-space shift data for only one partition in every Acc partitions.

Abstract: A system and method for performing accelerated k-space shift correction calibration scans for non-Cartesian trajectories is provided. The method can include applying an MRI sequence, performing a calibration scan based on the MRI sequence using the non-Cartesian trajectory to acquire k-space shift data, wherein one or more partitions are skipped during the calibration scan, interpolating the skipped one or more partitions using the k-space shift data from adjacent partitions, and calibrating the MRI system using the k-space shift data and the interpolated k-space shift data. In some embodiments, an acceleration factor Acc can be defined and the calibration scan acquires k-space shift data for only one partition in every Acc partitions.

Abstract: A method for using a multi-echo magnetic resonance imaging (MRI) simultaneously quantify T1 and fat fraction in an anatomical region of interest includes performing a radial single shot multi-echo acquisition of the anatomical region of interest. The radial single shot multi-echo acquisition comprises applying a preparation pulse to invert longitudinal magnetization of the anatomical region of interest, and acquiring a plurality of radial readouts at different echo times (TE). A magnetization recovery curve is continuously sampled using the plurality of radial readouts to yield a plurality of radial spokes. The radial spokes for each TE are ground together to generate under-sampled k-space data for each TE. The under-sampled k-space data is reconstructed into a plurality of multi-echo images corresponding to the different echo times. One or more fitting algorithms are applied to the multi-echo images to generate a water-only T1 map and a proton density fat fraction (PDFF) measurement.

Abstract: A computer implemented method of processing a medical image is disclosed. The method includes receiving a medical image comprising a first plurality of pixels each having an initial pixel value. For each of the first plurality of pixels, a filtering operation is applied to the pixel to generate a filtered pixel value for the pixel based on the initial pixel values of pixels that surround the pixel in the medical image. For each of the first plurality of pixels, a comparison of the initial pixel value with the filtered pixel value is performed. The method comprises, for each of the first plurality of pixels, determining, based on the comparison, whether or not to categorize the pixel as an erroneous pixel; and for each of the first plurality of pixels for which it is determined to categorize the pixel as an erroneous pixel, categorizing the pixel as an erroneous pixel.

Abstract: A method of generating biomarker parameters includes acquiring imaging data depicting a patient using a MRI system. The imaging data is acquired for a plurality of contrasts resulting from application of a pulse on the patient's anatomy. A process is executed to generate a MoCoAve image for each contrast. This process includes dividing the imaging data for the contrast into bins corresponding to one of a plurality of respiratory motion phases, and reconstructing the imaging data in each bin to yield bin images. The process further includes selecting a reference bin image from the bin images, and warping the bin images based on the reference bin image. The warped bin images and the reference bin image are averaged to generate the MoCoAve image for the contrast. One or more biomarker parameter maps are calculated based on the MoCoAve images generated for the contrasts.

Abstract: A computer-implemented method for using machine learning to suppress fat in acquired MR images includes receiving multi-echo images from an anatomical area of interest acquired using an MRI system. A first subset of the multi-echo images is acquired prior to application of contrast to the anatomical area of interest and a second subset of the multi-echo images is acquired after application of contrast to the anatomical area of interest. Next, data is generated including water images, fat images, and effective R*2 maps from the multi-echo images. The water images, the fat images, and the effective R*2 maps are used to create synthetic fat suppressed images. A neural network is trained to use the multi-echo images as input and the synthetic fat suppressed images as ground truth. A plurality of components of the neural network are saved to allow later deployment of the neural network on a computing system.

Abstract: A method for acquiring magnetic resonance imaging data with respiratory motion compensation using one or more motion signals includes acquiring a plurality of gradient-delay-corrected radial readout views of a subject using a free-breathing multi-echo pulse sequence, and sampling a plurality of data points of the gradient-delay-corrected radial readout views to yield a self-gating signal. The self-gating signal is used to determine a plurality of respiratory motion states corresponding to the plurality of gradient-delay-corrected radial readout views. The respiratory motion states are used to correct respiratory motion bias in the gradient-delay-corrected radial readout views, thereby yielding gradient-delay-corrected and motion-compensated multi-echo data. One or more images are reconstructed using the gradient-delay-corrected and motion-compensated multi-echo data.

Abstract: A method for acquiring magnetic resonance imaging data with respiratory motion compensation using one or more motion signals includes acquiring a plurality of gradient-delay-corrected radial readout views of a subject using a free-breathing multi-echo pulse sequence, and sampling a plurality of data points of the gradient-delay-corrected radial readout views to yield a self-gating signal. The self-gating signal is used to determine a plurality of respiratory motion states corresponding to the plurality of gradient-delay-corrected radial readout views. The respiratory motion states are used to correct respiratory motion bias in the gradient-delay-corrected radial readout views, thereby yielding gradient-delay-corrected and motion-compensated multi-echo data. One or more images are reconstructed using the gradient-delay-corrected and motion-compensated multi-echo data.

Abstract: A method of generating biomarker parameters includes acquiring imaging data depicting a patient using a MRI system. The imaging data is acquired for a plurality of contrasts resulting from application of a pulse on the patient's anatomy. A process is executed to generate a MoCoAve image for each contrast. This process includes dividing the imaging data for the contrast into bins corresponding to one of a plurality of respiratory motion phases, and reconstructing the imaging data in each bin to yield bin images. The process further includes selecting a reference bin image from the bin images, and warping the bin images based on the reference bin image. The warped bin images and the reference bin image are averaged to generate the MoCoAve image for the contrast. One or more biomarker parameter maps are calculated based on the MoCoAve images generated for the contrasts.

Abstract: A method for using a multi-echo magnetic resonance imaging (MRI) simultaneously quantify T1 and fat fraction in an anatomical region of interest includes performing a radial single shot multi-echo acquisition of the anatomical region of interest. The radial single shot multi-echo acquisition comprises applying a preparation pulse to invert longitudinal magnetization of the anatomical region of interest, and acquiring a plurality of radial readouts at different echo times (TE). A magnetization recovery curve is continuously sampled using the plurality of radial readouts to yield a plurality of radial spokes. The radial spokes for each TE are ground together to generate under-sampled k-space data for each TE. The under-sampled k-space data is reconstructed into a plurality of multi-echo images corresponding to the different echo times. One or more fitting algorithms are applied to the multi-echo images to generate a water-only T1 map and a proton density fat fraction (PDFF) measurement.

Abstract: A computer-implemented method for using machine learning to suppress fat in acquired MR images includes receiving multi-echo images from an anatomical area of interest acquired using an MRI system. A first subset of the multi-echo images is acquired prior to application of contrast to the anatomical area of interest and a second subset of the multi-echo images is acquired after application of contrast to the anatomical area of interest. Next, data is generated including water images, fat images, and effective R*2 maps from the multi-echo images. The water images, the fat images, and the effective R*2 maps are used to create synthetic fat suppressed images. A neural network is trained to use the multi-echo images as input and the synthetic fat suppressed images as ground truth. A plurality of components of the neural network are saved to allow later deployment of the neural network on a computing system.

Abstract: A system for performing magnetic resonance imaging (MRI) of a subject has a pulse sequence system that generates a pulse sequence and has a gradient system, a plurality of gradient coils, a radio-frequency system, and a plurality of RF coils. The pulse sequence system causes the subject to emit MR signals which are captured as k-space data. The system also has a k-space ordering processor that collects first k-space data and second k-space data, an MR signal modeler that generates a signal variation model, and a compensation module that applies the signal variation model to the second k-space data collected to produce compensated k-space data. A display processor reconstructs the compensated k-space data into an image of the subject. The compensated data accounts for variation in magnetization during the pulse sequence and k-space data collection to reduce artifacts in the images.