Abstract: A computer-implemented method of reducing noise in magnetic resonance (MR) images is provided. The method includes executing a neural network model of analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images. The pristine images are the corrupted images with noise reduced, and target output images of the neural network model are the pristine images. The method also includes receiving first MR signals and second MR signals, reconstructing first and second MR images based on the first MR signals and the second MR signals, and analyzing the first MR image and the second MR image using the neural network model. The method further includes deriving a denoised MR image based on the analysis, wherein the denoised MR image is a combined image based on the first MR image and the second MR image and outputting the denoised MR image.

Abstract: The present invention relates to a blade positioning system configured for positioning wind turbine blades at a hub of a nacelle of a wind turbine from an installation vessel at an offshore location, the blade positioning system comprising: the installation vessel comprising: —at least one lifting device configured for lifting wind turbine components, and —an auxiliary support tower extending upwardly from the installation vessel, the auxiliary support tower comprising: o a nacelle support for supporting the nacelle, o a root end moving assembly defining a guide path which extends over a vertical distance, the root end moving assembly comprising a movable root end support base and a root end support configured for supporting and guiding the root end of the blade, the root end support being connected to the movable root end support base, the root end support being movable along the guide path, the root end moving assembly being configured for moving the root end of the blade along the guide path from the engag

Abstract: A method for producing an image of a subject with a magnetic resonance imaging (MRI) comprises acquiring a first set of partial k-space data from the subject and generating a phase corrected image based on a phase correction factor and the first set of the partial k-space data. The method further includes transforming the phase corrected image into a second set of partial k-space data and reconstructing the image of the subject from the second set of the partial k-space data and a weighting function.

Abstract: A medical imaging system includes at least one medical imaging device to provide image data of a subject. A processing system is programmed to train a deep learning (DL) network using input image training data. The input image training data includes raw image data and at least one perturbation signal. The trained DL network is used to determine reconstructed image data from the image data of the subject and based on the reconstructed image data, a medical image of the subject is generated.

Abstract: Methods and systems are provided for independently removing streak artifacts and noise from medical images, using trained deep neural networks. In one embodiment, streak artifacts and noise may be selectively and independently removed from a medical image by receiving the medical image comprising streak artifacts and noise, mapping the medical image to a streak residual and a noise residual using the trained deep neural network, subtracting the streak residual from the medical image to a first extent, and subtracting the noise residual from the medical image to a second extent, to produce a de-noised medical image, and displaying the de-noised medical image via a display device.

Abstract: A magnetic resonance (MR) image processing system is provided. The system includes an MR image processing computing device that includes at least one processor. The processor is programmed to execute a neural network model configured to receive crude MR data as an input and output processed MR images associated with the crude MR data, the crude MR data and the processed MR images having the first number of dimensions. The processor is also programmed to receive a pair of pristine data and corrupted data both having a second number of dimensions lower than the first number of dimensions. The corrupted data are the pristine data added with primitive features. The processor is further programmed to train the neural network model using the pair of the pristine data and the corrupted data. The trained neural network model is configured to change primitive features associated with the crude MR data.

Abstract: A computer-implemented method for generating a chemical shift artifact corrected reconstructed image from magnetic resonance imaging (MRI) data includes inputting into a trained deep neural network an image generated from the MRI data acquired during a non-Cartesian MRI scan of a subject. The method also includes utilizing the trained deep neural network to generate the chemical shift artifact corrected reconstructed image from the image, wherein the trained deep neural network was trained utilizing a tissue mixing model that models interactions between different tissue types to mitigate chemical shift artifacts. The method further includes outputting from the trained deep neural network the chemical shift artifact corrected reconstructed image.

Abstract: A computer-implemented method of removing truncation artifacts in magnetic resonance (MR) images is provided. The method includes receiving a crude image that is based on partial k-space data from a partial k-space that is asymmetrically truncated in at least one k-space dimension. The method also includes analyzing the crude image using a neural network model trained with a pair of pristine images and corrupted images. The corrupted images are based on partial k-space data from partial k-spaces truncated in one or more partial sampling patterns. The pristine images are based on full k-space data corresponding to the partial k-space data of the corrupted images, and target output images of the neural network model are the pristine images. The method further includes deriving an improved image of the crude image based on the analysis, wherein the derived improved image includes reduced truncation artifacts and increased high spatial frequency data.

Abstract: A system receives information associated with an interaction with an individual in a context. Then, the system analyzes the information to extract features associated with one or more attributes of the individual. Moreover, the system generates, based at least in part on the extracted features, a group of behavioral agents in a multi-layer hierarchy that automatically mimics the one or more attributes. Next, the system calculates one or more performance metrics associated with the group of behavioral agents and the one or more attributes. Furthermore, the system determines, based at least in part on the one or more performance metrics, one or more deficiencies in the extracted features. Additionally, the system selectively acquires second information associated with additional interaction with the individual in the context based at least in part on the one or more deficiencies to at least in part correct for the one or more deficiencies.

Abstract: Tracer kinetic models are utilized as temporal constraints for highly under-sampled reconstruction of DCE-MRI data. In one embodiment, a method for improving dynamic contrast enhanced imaging. The method includes steps of administering a magnetic resonance contrast agent to a subject and then collecting magnetic resonance contrast agent from the subject. A tracer kinetic model (i.e. eTofts or Patlak) is selected to be applied to the magnetic resonance imaging data. The tracer kinetic model is applied to the magnetic resonance imaging data. Tracer kinetic maps and dynamic images are simultaneously reconstructed and a consistency constraint is applied. The proposed method allows for easy use of different tracer kinetic models in the formulation and estimation of patient-specific arterial input functions jointly with tracer kinetic maps.

Abstract: A location of a client in a private network where the client has a plurality of interfaces for using a plurality of internet gateways for coupling with the public internet can be determined by sending, from the client, a plurality of STUN BINDING requests to a STUN server connected to the public internet that each exit through an individual internet gateway, receiving, at the client, a plurality of STUN BINDING responses from the STUN server that each include public IP address mapped to the internet gateway through which the STUN BINDING request has exited the private network, sending, from the client, a plurality of location requests that each include a mapped public IP address and the corresponding private IP address, to a location server for looking up location data for the respective private IP addresses so the client can receive location data relating to the public IP addresses.

Abstract: A computer-implemented method of reducing noise in magnetic resonance (MR) images is provided. The method includes executing a neural network model of analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images. The pristine images are the corrupted images with noise reduced, and target output images of the neural network model are the pristine images. The method also includes receiving first MR signals and second MR signals, reconstructing first and second MR images based on the first MR signals and the second MR signals, and analyzing the first MR image and the second MR image using the neural network model. The method further includes deriving a denoised MR image based on the analysis, wherein the denoised MR image is a combined image based on the first MR image and the second MR image and outputting the denoised MR image.

Abstract: Methods and systems are provided for de-noising medical images using deep neural networks. In one embodiment, a method comprises receiving a medical image acquired by an imaging system, wherein the medical image comprises colored noise; mapping the medical image to a de-noised medical image using a trained convolutional neural network (CNN); and displaying the de-noised medical image via a display device. The deep neural network may thereby reduce colored noise in the acquired noisy medical image, increasing a clarity and diagnostic quality of the image.

Abstract: The present invention relates to a blade positioning system configured for positioning wind turbine blades at a hub of a nacelle of a wind turbine from an installation vessel at an offshore location, the blade positioning system comprising: the installation vessel comprising: — at least one lifting device configured for lifting wind turbine components, and — an auxiliary support tower extending upwardly from the installation vessel, the auxiliary support tower comprising: o a nacelle support for supporting the nacelle, o a root end moving assembly defining a guide path which extends over a vertical distance, the root end moving assembly comprising a movable root end support base and a root end support configured for supporting and guiding the root end of the blade, the root end support being connected to the movable root end support base, the root end support being movable along the guide path, the root end moving assembly being configured for moving the root end of the blade along the guide path from the eng

Abstract: A magnetic resonance (MR) image processing system is provided. The system includes an MR image processing computing device that includes at least one processor. The processor is programmed to execute a neural network model configured to receive crude MR data as an input and output processed MR images associated with the crude MR data, the crude MR data and the processed MR images having the first number of dimensions. The processor is also programmed to receive a pair of pristine data and corrupted data both having a second number of dimensions lower than the first number of dimensions. The corrupted data are the pristine data added with primitive features. The processor is further programmed to train the neural network model using the pair of the pristine data and the corrupted data. The trained neural network model is configured to change primitive features associated with the crude MR data.

Abstract: A location of a client in a private network where the client has a plurality of interfaces for using a plurality of internet gateways for coupling with the public internet can be determined by sending, from the client, a plurality of STUN BINDING requests to a STUN server connected to the public internet that each exit through an individual internet gateway, receiving, at the client, a plurality of STUN BINDING responses from the STUN server that each include public IP address mapped to the internet gateway through which the STUN BINDING request has exited the private network, sending, from the client, a plurality of location requests that each include a mapped public IP address and the corresponding private IP address, to a location server for looking up location data for the respective private IP addresses so the client can receive location data relating to the public IP addresses.

Abstract: A computer that simulates a real-world showroom experience for a wholesale buyer is described. During operation, the computer may receive information corresponding to first user-interface activity associated with an electronic device, where the user-interface activity indicates selections of the wholesale buyer of fashion items in a first user interface. Then, the computer may generate instructions for a second user interface based at least in part on the first user-interface activity, where the second user interface corresponds to a virtual showroom of the wholesale buyer, the second user interface includes the selected fashion items and a clothes rack with available hangers, and wherein the second user interface allows the wholesale buyer to dynamically assemble one or more groups of the selected fashion items on one or more of the available hangers. Next, the computer may provide the instructions for the second user interface addressed to the electronic device.

Abstract: The present invention relates to a method for continuously manufacturing UHMWPE products comprising:—providing a counter-rotating twin-screw extruder;—feeding UHMWPE powder into a hopper of said counter-rotating twin-screw extruder;—transporting said UHMWPE powder from said hopper through said counter-rotating twin-screw extruder to an outlet of said counter-rotating twin-screw extruder;—further transporting said UHMWPE powder from said outlet of said counter-rotating twin-screw extruder to an entrance of a heat-controlled tooling system for defining the shape of UHMWPE products;—withdrawing said UHMWPE products from an outlet of said heat-controlled tooling system.

Abstract: This invention allows the players of multiplayer games like poker, chess, backgammon, go, board games, and video games to play the game online while commenting on the game and explaining their strategies to an online audience without sharing that information with their opponents.

Abstract: Tracer kinetic models are utilized as temporal constraints for highly under-sampled reconstruction of DCE-MRI data. The method is flexible in handling any TK model, does not rely on tuning of regularization parameters, and in comparison to existing compressed sensing approaches, provides robust mapping of TK parameters at high under-sampling rates. In summary, the method greatly improves the robustness and ease-of-use while providing better quality of TK parameter maps than existing methods. In another embodiment, TK parameter maps are directly reconstructed from highly under-sampled DCE-MRI data. This method provides more accurate TK parameter values and higher under-sampling rates. It does not require tuning parameters and there are not additional intermediate steps. The proposed method greatly improves the robustness and ease-of-use while providing better quality of TK parameter maps than conventional indirect methods.