Abstract: Described herein are systems, methods, and instrumentalities associated with using a multi-layer perceptron (MLP) neural network to process medical images of an anatomical structure. The processing may include padding an input image in accordance with the training of the MLP neural network, splitting the input image (e.g., the padded input image) into patches of a same size, and processing the patches through the MLP neural network over one or more iterations. During an iteration of the processing, the patches may be processed separately and re-combined into an intermediate image before the intermediate image is shifted to concatenate portions of the image that are derived from different patches. This way, global features of the anatomical structure may be learned and used to improve the quality of the image generated by the MLP neural network, without incurring significant computation or memory costs.

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: Mammography may provide multi-view information about the healthy state of a person's breasts, and described herein are deep learning based techniques for obtaining mammographic images associated with multiple views, extracting cross-view features from the images, and automatically determining the existence or non-existence of a medical abnormality based on the extracted features. The cross-view features may be determined using an encoding module of an artificial neural network (ANN) and the encoded features may be decoded using a decoding module of the ANN to generate a prediction (e.g., a classification label, a bounding shape, a segmentation mask, a probability map, etc.) about the medical abnormality.

Abstract: A method includes using fully sampled retro cine data to train an algorithm, and applying the trained algorithm to real time MR cine data to yield reconstructed MR images.

Abstract: Deblurring and denoising a medical image such as X-ray fluoroscopy images may be challenging, and deep-learning based techniques may be employed to meet the challenge. An artificial neural network (ANN) may be trained using training images with synthetic noise and as well as training images with real noise. The parameters of the ANN may be adjusted during the training based on at least a first loss designed to maintain continuity between consecutive medical images generated by the ANN and a second loss designed to maintain similarity of patches inside a medical image generated by the ANN. The parameters of the ANN may be further adjusted based on a third loss that may be calculated from ground truth associated with the synthetic training images. Transfer learning between the synthetic images and the real images may be accomplished using these techniques.

Abstract: Real-time cardiac MRI images may be captured continuously across multiple cardiac phases and multiple slices. Machine learning-based techniques may be used to determine spatial (e.g., slices and/or views) and temporal (e.g., cardiac cycles and/or cardiac phases) properties of the cardiac images such that the images may be arranged into groups based on the spatial and temporal properties of the images and the requirements of a cardiac analysis task. Different groups of the cardiac MRI images may also be aligned with each other based on the timestamps of the images and/or by synthesizing additional images to fill in gaps.

Abstract: A neural network system implements a model for generating an output image based on a received input image. The model is learned through a training process during which parameters associated with the model are adjusted so as to maximize a difference between a first image predicted using first parameter values of the model and a second image predicted using second parameter values of the model, and to minimize a difference between the second image and a ground truth image. During a first iteration of the training process the first image is predicted and during a second iteration the second image is predicted. The first parameter values are obtained during the first iteration by minimizing a difference between the first image and the ground truth image, and the second parameter values are obtained during the second iteration by maximizing the difference between the first image and the second image.

Abstract: Disclosed herein are systems, methods, and instrumentalities associated with reconstructing magnetic resonance (MR) images based on under-sampled MR data. The MR data include 2D or 3D information, and may encompass multiple contrasts and multiple coils. The MR images are reconstructed using deep learning (DL) methods, which may accelerate the scan and/or image generation process. Challenges imposed by the large quantity of the MR data and hardware limitations are overcome by separately reconstructing MR images based on respective subsets of contrasts, coils, and/or readout segments, and then combining the reconstructed MR images to obtain desired multi-contrast results.

Abstract: In Multiplex MRI image reconstruction, a hardware processor acquires sub-sampled Multiplex MRI data and reconstructs parametric images from the sub-sampled Multiplex MRI data. A machine learning model or deep learning model uses the subsampled Multiplex MRI data as the input and parametric maps calculated from the fully sampled data, or reconstructed fully sample data, as the ground truth. The model learns to reconstruct the parametric maps directly from the subsampled Multiplex MRI data.

Abstract: Described herein are systems, methods, and instrumentalities associated with landmark detection. The detection may be accomplished by determining a graph representation of a plurality of hypothetical landmarks detected in one or more medical images. The graph representation may include nodes that represent the hypothetical landmarks and edges that represent the relationships between paired hypothetical landmarks. The graph representation may be processed using a graph neural network such a message passing graph neural network, by which the landmark detection problem may be converted and solved as a graph node labeling problem.

Abstract: Motion contaminated magnetic resonance (MR) images for training an artificial neural network to remove motion artifacts from the MR images are difficult to obtain. Described herein are systems, methods, and instrumentalities for injecting motion artifacts into clean MR images and using the artificially contaminated images for machine learning and neural network training. The motion contaminated MR images may be created based on clean source MR images that are associated with multiple physiological cycles of a scanned object, and by deriving MR data segments for the multiple physiological cycles based on the source MR images. The MR data segments thus derived may be combined to obtain a simulated MR data set, from which one or more target MR images may be generated to exhibit a motion artifact. The motion artifact may be created by manipulating the source MR images and/or controlling the manner in which the MR data set or the target MR images are generated.

Abstract: Disclosed herein are systems, methods, and instrumentalities associated with reconstructing magnetic resonance (MR) images based on multi-slice, under-sampled MRI data (e.g., k-space data). The multi-slice MRI data may be acquired using a simultaneous multi-slice (SMS) technique and MRI information associated with multiple MRI slices may be entangled in the multi-slice MRI data. A neural network may be trained and used to disentangle the MRI information and reconstruct MRI images for the different slices. A data consistency component may be used to estimate k-space data based on estimates made by the neural network, from which respective MRI images associated with multiple MRI slices may be obtained by applying a Fourier transform to the k-space data.

Abstract: A 3D anatomical model of one or more blood vessels of a patient may be obtained using CT angiography, while a 2D image of the blood vessels may be obtained based on fluoroscopy. The 3D model may be registered with the 2D image based on a contrast injection site identified on the 3D model and/or in the 2D image. A fused image may then be created to depict the overlaid 3D model and 2D image, for example, on a monitor or through a virtual reality headset. The injection site may be determined automatically or based on a user input that may include a bounding box drawn around the injection site on the 3D model, a selection of an automatically segmented area in the 3D model, etc.

Abstract: Digital breast tomosynthesis (DBT) may provide richer information than full-field digital mammography (FFDM). DBT data such as DBT slices may be processed based on deep learning techniques such as using a neural network, and the DBT slices may be divided into groups and a pre-determined number of representative images may be derived based on the grouping. The neural network may be configured to process the representative images to predict the presence or non-presence of a breast disease such as breast cancer.

Abstract: Mammography data such as DBT and/or FFDM images may be processed using deep learning based techniques, but labeled training data that may facilitate the learning may be difficult to obtained. Described herein are systems, methods, and instrumentalities associated with automatically generating and/or augmenting labeled mammography training data, and training a deep learn model based on the auto-generated/augmented data for detecting a breast disease (e.g., breast cancer) in a mammography image.

Abstract: A shielding structure is disposed around a camera of a mower, in a front of a forward direction of the mower. The shielding structure includes a first shielding plate, a second shielding plate, and a third shielding plate. The shielding plates from a lens surface of the camera. The first shielding plate is disposed above the camera to shield sunlight from a top of the camera. The second and third shielding plates are disposed on two sides of the camera to shield sunlight from the two sides of the camera. The shielding plates may be formed unitarily or separately, and they may be removably and/or retractably attached to the mower. The shielding plates may be arranged so that the shielding structure flares outwardly in the downward direction.

Abstract: Deep learning-based systems, methods, and instrumentalities are described herein for MRI reconstruction and/or refinement. An MRI image may be reconstructed based on under-sampled MRI information and a generative model may be trained to refine the reconstructed image, for example, by increasing the sharpness of the MRI image without introducing artifacts into the image. The generative model may be implemented using various types of artificial neural networks including a generative adversarial network. The model may be trained based on an adversarial loss and a pixel-wise image loss, and once trained, the model may be used to improve the quality of a wide range of 2D or 3D MRI images including those of a knee, brain, or heart.

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.