Abstract: Relevant portions of broadcasts of live events, such as replays, may be marked or framed by patterns of one or more images. A model is trained with pairs of images and text descriptors to generate image embeddings and text embeddings representative of the images and text descriptors, respectively. Where a pattern of images includes one or more images, a user may specify one or more text descriptors representative of images of the pattern and identify portions of a media file with respect to such images. The model generates text embeddings of the text descriptors, as well as image embeddings of image frames of a video file broadcasted to viewers, including a relevant portion marked or framed by the pattern. Where an image embedding is sufficiently similar to one of the text embeddings, the pattern is detected, and the relevant portion is identified based on the pattern.

Abstract: Systems and techniques are described for providing optimal play route prediction during sports broadcasts. In various examples, a first frame of tracking data indicating a first respective location of each player of a first plurality of players on a field plane may be received at a first time. A first predicted direction of motion may be determined for the first frame based at least in part on a first objective. A second frame of tracking data indicating a second respective location of each player of the first plurality of players on the field plane may be received at a second time. A second predicted direction of motion for the second frame may be determined based at least in part on the first objective. A first graphical overlay may be displayed on a live video feed based on one or more of the first predicted direction and the second predicted direction.

Abstract: K-space data obtained from a magnetic resonance imaging scan where motion was detected is split into two parts in accordance with the timing of the motion to produce first and second sets of k-space data corresponding to different poses. Sub-images are reconstructed from the k first and second sets of k-space data, which are used as inputs to a deep neural network which transforms them into a motion-corrected image.

Abstract: A magnetic resonance imaging (MRI) system includes control and analysis circuitry having programming to acquire magnetic resonance (MR) data using coil elements of the MRI system, analyze the MR data, and reconstruct the MR data into MR sub-images. The system also includes a trained neural network associated with the control and analysis circuitry to transform the MR sub-images into a prediction relating to a presence and extent of motion corruption in the MR sub-images. The programming of the control and analysis circuitry includes instructions to control operations of the MRI system based at least in part on the prediction of the trained neural network.

Abstract: K-space data obtained from a magnetic resonance imaging scan where motion was detected is split into two parts in accordance with the timing of the motion to produce first and second sets of k-space data corresponding to different poses. Sub-images are reconstructed from the k first and second sets of k-space data, which are used as inputs to a deep neural network which transforms them into a motion-corrected image.

Abstract: A magnetic resonance imaging (MRI) system includes control and analysis circuitry having programming to acquire magnetic resonance (MR) data using coil elements of the MRI system, analyze the MR data, and reconstruct the MR data into MR sub-images. The system also includes a trained neural network associated with the control and analysis circuitry to transform the MR sub-images into a prediction relating to a presence and extent of motion corruption in the MR sub-images. The programming of the control and analysis circuitry includes instructions to control operations of the MRI system based at least in part on the prediction of the trained neural network.

Abstract: A system and method for detecting, timing, and adapting to patient motion during an MR scan includes using the inconsistencies between calculated images from different coil-array elements to detect the presence of patient motion and, together with the k-space scan-order information, determine the timing of the motion during the scan. Once the timing is known, various actions may be taken, including restarting the scan, reacquiring those portions of k-space acquired before the movement, or correcting for the motion using the existing data and reconstructing a motion-corrected image from the data.

Abstract: A system and method for detecting, timing, and adapting to patient motion during an MR scan includes using the inconsistencies between calculated images from different coil-array elements to detect the presence of patient motion and, together with the k-space scan-order information, determine the timing of the motion during the scan. Once the timing is known, various actions may be taken, including restarting the scan, reacquiring those portions of k-space acquired before the movement, or correcting for the motion using the existing data and reconstructing a motion-corrected image from the data.