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: Methods and systems are provided for processing medical images using deep neural networks. In one embodiment, a medical image processing method comprises receiving a first medical image having a first characteristic and one or more acquisition parameters corresponding to acquisition of the first medical image, incorporating the one or more acquisition parameters into a trained deep neural network, and mapping, by the trained deep neural network, the first medical image to a second medical image having a second characteristic. The deep neural network may thereby receive at least partial information regarding the type, extent, and/or spatial distribution of the first characteristic in a first medical image, enabling the trained deep neural network to selectively convert the received first medical image.

Abstract: A target assembly that is configured to withstand high-velocity impact from a projectile. These configurations may comprise elastic members, or “bungee” cords, that generate elastic forces to return the target assembly to its orientation prior to impact. The bungee cords are less likely to undergo inelastic deformation; so these components afford the target assembly with longer life or longevity under heavy duty cycles.

Abstract: Methods and systems are provided for deblurring medical images using deep neural networks. In one embodiment, a method for deblurring a medical image comprises receiving a blurred medical image and one or more acquisition parameters corresponding to acquisition of the blurred medical image, incorporating the one or more acquisition parameters into a trained deep neural network, and mapping, by the trained deep neural network, the blurred medical image to a deblurred medical image. The deep neural network may thereby receive at least partial information regarding the type, extent, and/or spatial distribution of blurring in a blurred medical image, enabling the trained deep neural network to selectively deblur the received blurred medical image.

Abstract: A computer-implemented method of correcting phase and reducing noise in magnetic resonance (MR) phase images is provided. The method includes executing a neural network model for analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images, wherein the corrupted images include corrupted phase information, the pristine images are the corrupted images with the corrupted phase information reduced, and target output images of the neural network model are the pristine images. The method further includes receiving MR images including corrupted phase information, and analyzing the received MR images using the neural network model. The method also includes deriving pristine phase images of the received MR images based on the analysis, wherein the derived pristine phase images include reduced corrupted phase information, compared to the received MR images, and outputting MR images based on the derived pristine phase images.

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: 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: A method for requesting connection in a blue-green deployment which is switched over from a first server (blue) to which at least two clients are connected to a second server (green). The method can include determining, upon receipt of the maintenance notification or detection of a connection failure, an activity score at each one of the at least two clients; and, upon switchover to the second server or detection of the connection failure, transmitting, together with a respective reconnection request, the respective activity score from each one of the at least two clients to the second server for the second server to evaluate a prioritization of the reconnection request based on the activity score. Embodiments of a communication system, server system, and clients (e.g. user devices) can utilize embodiments of the method.

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: Methods and systems are provided for deblurring medical images using deep neural networks. In one embodiment, a method for deblurring a medical image comprises receiving a blurred medical image and one or more acquisition parameters corresponding to acquisition of the blurred medical image, incorporating the one or more acquisition parameters into a trained deep neural network, and mapping, by the trained deep neural network, the blurred medical image to a deblurred medical image. The deep neural network may thereby receive at least partial information regarding the type, extent, and/or spatial distribution of blurring in a blurred medical image, enabling the trained deep neural network to selectively deblur the received blurred medical image.

Abstract: A system provides, based at least in part on predetermined parameters, configuration information, and a group of behavioral agents, a dynamic virtual representation that includes a multi-dimensional puppet having one or more attributes of an individual, where the dynamic virtual representation automatically mimics one or more attributes of the individual in a context, the providing of the dynamic virtual representation that includes the multi-dimensional puppet involves rendering of the multi-dimensional puppet, and the multi-dimensional puppet includes stereopsis information, and has photorealistic movement corresponding to movement behaviors of the individual. Then, the system receives an input corresponding to user spatial manipulation of or interaction with the multi-dimensional puppet.

Abstract: Methods and systems are provided for selectively denoising medical images. In an exemplary method, one or more deep learning networks are trained to map corrupted images onto a first type and a second type of artifacts present in corresponding corrupted images. Then the one or more trained learning networks are used to single out the first and second types of artifacts from a particular medical image. The first type of artifacts is removed to a first extent and the second type of artifacts is removed to a second extent. The first and second extents may be different. For example, one type of artifacts can be fully suppressed while the other can be partially removed form the medical image.

Abstract: Methods and systems are provided for hybrid slice encoding. In one embodiment, a method for magnetic resonance imaging comprises, during a scan with a pulse sequence, sampling k-space linearly for a predetermined number of echoes, and sampling k-space centrically for remaining echoes of the pulse sequence. In this way, blurriness along the slice direction may be reduced for 3D fast spin echo imaging.

Abstract: A target assembly that is configured to withstand high-velocity impact from a projectile. These configurations may comprise elastic members, or “bungee” cords, that generate elastic forces to return the target assembly to its orientation prior to impact. The bungee cords are less likely to undergo inelastic deformation; so these components afford the target assembly with longer life or longevity under heavy duty cycles.

Abstract: Methods and systems are provided for reducing noise in medical images with deep neural networks. In one embodiment, a method for training a neural network comprises transforming each of a plurality of initial image data sets not acquired by a medical imaging modality into a target image data set, wherein each target image data set is in a format specific to the medical imaging modality, corrupting each target image data set to generate a corrupted image data set, and training the neural network to map each corrupted image data set to the corresponding target image data set. In this way, the high-resolution of digital non-medical photographs or images can be leveraged for the enhancement or correction of medical images, and the trained neural network can be used to reduce noise and image artifacts in medical images acquired by the medical imaging modality.

Abstract: Methods and systems are provided for selectively denoising medical images. In an exemplary method, one or more deep learning networks are trained to map corrupted images onto a first type and a second type of artifacts present in corresponding corrupted images. Then the one or more trained learning networks are used to single out the first and second types of artifacts from a particular medical image. The first type of artifacts is removed to a first extent and the second type of artifacts is removed to a second extent. The first and second extents may be different. For example, one type of artifacts can be fully suppressed while the other can be partially removed form the medical image.

Abstract: Methods and systems are provided for hybrid slice encoding. In one embodiment, a method for magnetic resonance imaging comprises, during a scan with a pulse sequence, sampling k-space linearly for a predetermined number of echoes, and sampling k-space centrically for remaining echoes of the pulse sequence. In this way, blurriness along the slice direction may be reduced for 3D fast spin echo imaging.

Abstract: A system that is configured to coordinate movement of athletes during drills and exercises. The system may be configured for receiving position data, in real-time, that relates to a location of a player on a venue, comparing the position data with registration data for tracked locations on the venue found in an instruction listing, selecting an instruction from the instruction listing indicating that the real-time position data corresponds with the registration data for the tracked locations, and generating an output that conveys the instruction to the player to perform an action. In on implementation, the system 100 may also process ball-related data, in conjunction with the real-time position data, where the ball-related data relates to the presence or absence of the ball at, near, or in proximity to portable equipment (e.g., a lacrosse stick) carried by the athletes during game play.

Abstract: A sports device configured to generate thermal energy to warm surfaces. In one implementation, the sports device embodies a lacrosse stick with a shaft and head. The shaft includes a thermal core with a phase change material that can retain and dissipate heat over an extended period of time.

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.