Abstract: Medical image data representing a medical image is received at a first machine learning (ML)-based system. The first ML-based system generates, based on the received medical image data, a plurality of image embedding vectors corresponding to a respective plurality of medical image features, each of the plurality of image embedding vectors relating to a different respective medical image feature and comprising medical image feature data indicative of the presence or absence of the respective medical image feature at each of a plurality of locations in the medical image. An indication of a first medical image feature included in the medical image is received at a second ML-based system. The second ML-based system generates a feature vector based on the indication. A comparison of the feature vector with the plurality of image embedding vectors is performed and a first image embedding vector is identified based on the comparison.

Abstract: A framework for anatomical positioning. In accordance with one aspect, input text is mapped into normalized coordinates using an artificial neural network. A location in a target image that corresponds to the normalized coordinates is determined and presented. In accordance with another aspect, a user selection of a point-of-interest in a medical image is received. A context set of points nearest to the point-of-interest is determined. A prompt containing the point-of-interest and context set of points is constructed.

Abstract: A framework for generating natural language text describing a change between first medical imaging data and second medical imaging data is disclosed. Imaging data representative of the first medical imaging data and the second medical imaging data, or of a difference between the first medical imaging data and the second medical imaging data, is obtained. The imaging data is input into a first trained machine learning model to generate an image feature vector representative of the difference between the first medical imaging data and the second medical imaging data. Data representative of the image feature vector is input into a second trained machine learning model to generate the natural language text describing the change between the first medical imaging data and the second medical imaging data.

Abstract: A computer implemented framework for determining data representing a change between first medical imaging data and second medical imaging data is disclosed. Imaging data representative of a difference between the first medical imaging data and the second medical imaging data is obtained. The imaging data is input into a trained image processing machine learning model to generate an image feature vector. A plurality of text feature vectors is obtained, each text feature vector being representative of natural language text describing a respective change in medical imaging data. For each of the plurality of text feature vectors, a similarity measure indicating a degree of similarity to the image feature vector determined. A text feature vector is selected. The data representing the change between the first medical imaging data and the second medical imaging data is determined based on the selected text feature vector.

Abstract: Suppressing artifacts in MRI image acquisition data includes alternatives to phase cycling by using a Convolutional Neural Network to suppress the artifact-generating echos. A U-NET CNN is trained using phase-cycled artifact-free images for ground truth comparison with received displacement encoded stimulated echo (DENSE) images. The DENSE images include data from a single acquisition with both stimulated (STE) and T1-relaxation echoes. The systems and methods of this disclosure are explained as generating artifact-free images in the ultimate output and avoiding the additional data acquisition needed for phase cycling and shortens the scan time in DENSE MRI.

Abstract: Suppressing artifacts in MRI image acquisition data includes alternatives to phase cycling by using a Convolutional Neural Network to suppress the artifact-generating echos. A U-NET CNN is trained using phase-cycled artifact-free images for ground truth comparison with received displacement encoded stimulated echo (DENSE) images. The DENSE images include data from a single acquisition with both stimulated (STE) and T1-relaxation echoes. The systems and methods of this disclosure are explained as generating artifact-free images in the ultimate output and avoiding the additional data acquisition needed for phase cycling and shortens the scan time in DENSE MRI.