Abstract: Described herein are systems, methods, and instrumentalities associated with using an invertible neural network to complete various medical imaging tasks. Unlike traditional neural networks that may learn to map input data (e.g., a blurry reconstructed MRI image) to ground truth (e.g., a fully-sampled MRI image), the invertible neural network may be trained to learn a mapping from the ground truth to the input data, and may subsequently apply an inverse of the mapping (e.g., at an inference time) to complete a medical imaging task. The medical imaging task may include, for example, MRI image reconstruction (e.g., to increase the sharpness of a reconstructed MRI image), image denoising, image super-resolution, and/or the like.

Abstract: A mowing device includes a mower; and a camera located at a front part of the mower and oriented in a forward direction of the mower. The camera has a lens. A vertical line is defined from a center of the lens to a traveling surface of the mower. The vertical line and the traveling surface intersect at a first intersection. A horizontal axis is defined from the first intersection in the forward direction of the mower along the traveling surface as a positive direction. A central axis of the lens and the horizontal axis intersect at a second intersection. The second intersection is either in the positive direction of the horizontal axis or coincides with the first intersection.

Abstract: The present disclosure provides a system and method for fetus monitoring. The method may include obtaining ultrasound data relating to a fetus collected by an ultrasound imaging device; generating a 4D image of the fetus based on the ultrasound data; directing a display component of a virtual reality (VR) device to display the 4D image to an operator; detecting motion of the fetus based on the ultrasound data; and directing a haptic component of the VR device to provide haptic feedback with respect to the motion to the operator.

Abstract: A slurry blending tool may include a blending tank to receive and blend one or more materials into a slurry, and at least one inlet pipe connected to the blending tank and to provide the one or more materials to the blending tank. The at least one inlet pipe may vertically enter the blending tank and may not contact the blending tank. The slurry blending tool may include a blending pump partially provided within the blending tank and to blend the one or more materials into the slurry. The slurry blending tool may include an outlet pipe connected to the blending pump and to remove the slurry from the blending tank.

Abstract: Described herein are systems, methods, and instrumentalities associated with tracking groups of small objects in medical images. The tracking may be accomplished by, for each one of a sequence of medical images, determining a plurality of candidate objects captured in the medical image, grouping the plurality of candidate objects into a plurality of groups of candidate objects and dividing the medical image into a plurality of regions that each surrounds a corresponding group of candidate objects. Each of the plurality of regions may be examined to extract respective features associated with each corresponding group of candidate objects. A match between a first group of candidate objects in a first medical image and a second group of candidate objects in a second medical image may be determined based on first features associated with the first group and second features associated with the second group.

Abstract: A slurry blending tool may include a blending tank to receive and blend one or more materials into a slurry, and at least one inlet pipe connected to the blending tank and to provide the one or more materials to the blending tank. The at least one inlet pipe may vertically enter the blending tank and may not contact the blending tank. The slurry blending tool may include a blending pump partially provided within the blending tank and to blend the one or more materials into the slurry. The slurry blending tool may include an outlet pipe connected to the blending pump and to remove the slurry from the blending tank.

Abstract: A point cloud decoding method includes that: a decoder decodes a bitstream, and determines a mode parameter; in response to the mode parameter indicating to use a spatial scalability mode, the decoder determines geometric center coordinates of a current node, and determines, in the point cloud, geometric center coordinates of neighbouring nodes of the current node based on the geometric center coordinates of the current mode; the decoder determines local spatial coordinate means corresponding to the current node based on the geometric center coordinates of the current node and the geometric center coordinates of the neighbouring nodes; and the decoder determines reconstructed geometric information of the current node based on the local spatial coordinate means. Further, a decoder and a non-transitory computer-readable storage medium are also provided.

Abstract: A geometry reconstruction method performed by a decoder includes that a neighbouring node of a current node is determined based on location information of the current node; a factor of impact of the neighbouring node in direction along a preset axis on the current node is determined based on attribute information of the neighbouring node; a weight of the neighbouring node of the current node is determined; and coordinates of the current node after geometry reconstruction are determined based on the weight of the neighbouring node and the factor of impact of the neighbouring node in direction along the preset axis on the current node.

Abstract: Described herein are systems, methods, and instrumentalities associated with reconstruction of multi-contrast magnetic resonance imaging (MRI) images. The reconstruction may be performed based on under-sampled MRI data collected for the multiple contrasts using corresponding sampling patterns. The sampling patterns and the reconstruction operations for the multiple contrasts may be jointly optimized using deep learning techniques implemented through one or more neural networks. An end-to-end reconstruction optimizing framework is provided with which information collected while processing one contrast may be stored and used for another contrast. A differentiable sampler is described for obtaining the under-sampled MRI data from a k-space and a novel holistic recurrent neural network is used to reconstruct MRI images based on the under-sampled MRI data.

Abstract: Described herein are systems, methods, and instrumentalities associated with processing complex-valued MRI data using a machine learning (ML) model. The ML model may be learned based on synthetically generated MRI training data and by applying one or more meta-learning techniques. The MRI training data may be generated by adding phase information to real-valued MRI data and/or by converting single-coil MRI data into multi-coil MRI data based on coil sensitivity maps. The meta-learning process may include using portions of the training data to conduct a first round of learning to determine updated model parameters and using remaining portions of the training data to test the updated model parameters. Losses associated with the testing may then be determined and used to refine the model parameters. The ML model learned using these techniques may be adopted for a variety of tasks including, for example, MRI image reconstruction and/or de-noising.

Abstract: An unsupervised machine learning method with self-supervision losses improves a slice-wise spatial resolution of 3D medical images with thick slices, and does not require high resolution images as the ground truth for training. The method utilizes information from high-resolution dimensions to increase a resolution of another desired dimension.

Abstract: Described herein are systems, methods, and instrumentalities associated with medical image enhancement. The medical image may include an object of interest and the techniques disclosed herein may be used to identify the object and enhance a contrast between the object and its surrounding area by adjusting at least the pixels associated with the object. The object identification may be performed using an image filter, a segmentation mask, and/or a deep neural network trained to separate the medical image into multiple layers that respectively include the object of interest and the surrounding area. Once identified, the pixels of the object may be manipulated in various ways to increase the visibility of the object. These may include, for example, adding a constant value to the pixels of the object, applying a sharpening filter to those pixels, increasing the weight of those pixels, and/or smoothing the edge areas surrounding the object of interest.

Abstract: A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

Abstract: A universal mounting mechanism including a mounting bracket configured to mount a liquid-cooling heat exchange apparatus to a substrate or an electronic component and at least one universal fastener assembly is provided. The at least one universal fastener assembly is orthogonally assembled through the mounting bracket. The liquid-cooling heat exchange apparatus includes a first liquid-cooling heat exchange apparatus having a first waterblock set or a second liquid-cooling heat exchange apparatus having a second waterblock set. The first waterblock set has a first block thickness, and the second waterblock set has a second block thickness. The substrate or the electronic component includes receiving portions having a fastened thickness. A thickness of the second block thickness and the fastened thickness is greater than a thickness of the first block thickness and the fastened thickness.

Abstract: A system for image reconstruction in magnetic resonance imaging (MRI) is provided. The system may obtain undersampled k-space data associated with an object, wherein the undersampled K-space data may be generated based on magnetic resonance (MR) signals collected by an MR scanner that scans the object. The system may construct an ordinary differential equation (ODE) that formulates a reconstruction of an MR image based on the undersampled k-space data. The system may further generate the MR image of the object by solving the ODE based on the undersampled k-space data using an ODE solver.

Abstract: A heat dissipation module is provided and includes a cold plate having a housing, and a frame body disposed on the housing and having two sidewalls and at least one first rib, where the two sidewalls are positioned at two sides of the housing, respectively, and the first rib is used to provide a deformation resistance so that the heat dissipation module will not be seriously deformed when secured.

Abstract: A standalone image reconstruction device is configured to reconstruct the raw signals received from a radiology scanner device into a reconstructed output signal. The image reconstruction device is a vendor neutral interface between the radiology scanner device and the post processing imaging device. The reconstructed output signal is a user readable domain that can be used to generate a medical image or a three-dimensional (3D) volume. The apparatus is configured to reconstruct signals from different types of radiology scanner devices using any suitable image reconstruction protocol.

Abstract: A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.

Abstract: An apparatus for stent visualization includes a hardware processor that is configured to input one or more stent images from a sequence of X-ray images and corresponding balloon marker location data to a cascaded spatial transform network. The background is separated from the one or more stent images using the cascaded spatial transform network and a transformed stent image with a clear background and a non-stent background image is generated. The stent layer and non-stent layer are generated using a neural network without online optimization. A mapping function f maps the inputs, the sequence images and marker coordinates, into the two single image outputs.

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with estimating the motion of an anatomical structure. The motion estimation may be performed utilizing pre-learned knowledge of the anatomy of the anatomical structure. The anatomical knowledge may be learned via a variational autoencoder, which may then be used to optimize the parameters of a motion estimation neural network system such that, when performing motion estimation for the anatomical structure, the motion estimation neural network system may produce results that conform with the underlying anatomy of anatomical structure.