Abstract: Methods and systems are provided for removing visual artifacts from a medical image acquired during a scan of an object, such as a patient. In one example, a method for an image processing system comprises receiving a medical image; performing a wavelet decomposition on image data of the medical image; performing one or more 2-D Fourier transforms on wavelet coefficients resulting from the wavelet decomposition; removing image artifacts from the Fourier coefficients determined from the 2-D Fourier transforms using a filter; reconstructing the medical image using the filtered Fourier coefficients; and displaying the reconstructed medical image on a display device of the image processing system.

Abstract: Various methods and systems are provided for generating super-resolution images. In one embodiment, a method comprises: progressively up-sampling an input image to generate a super-resolution output image by: generating N intermediate images based on the input image, where N is equal to at least one, including a first intermediate image by providing the input image to a deep neural network, where a resolution of the first intermediate image is a multiple of a resolution of the input image, higher than the resolution of the input image, and can be any positive real value and not necessarily an integer value; generating the super-resolution output image based on the N intermediate images, the super-resolution output image having a resolution higher than a respective resolution of each intermediate image of the N intermediate images and the resolution of the input image; and displaying the super-resolution output image via a display device.

Abstract: Systems/techniques that facilitate improved uncertainty scoring for neural networks via stochastic weight perturbations are provided. In various embodiments, a system can access a trained neural network and/or a data candidate on which the trained neural network is to be executed. In various aspects, the system can generate an uncertainty indicator representing how confidently executable or how unconfidently executable the trained neural network is with respect to the data candidate, based on a set of perturbed instantiations of the trained neural network.

Abstract: Methods and systems are provided for predicting cardiac arrhythmias based on multi-modal patient monitoring data via deep learning. In an example, a method may include predicting an imminent onset of a cardiac arrhythmia in a patient, before the cardiac arrhythmia occurs, by analyzing patient monitoring data via a multi-arm deep learning model, outputting an arrhythmia event in response to the prediction, and outputting a report indicating features of the patient monitoring data contributing to the prediction. In this way, the multi-arm deep learning model may predict cardiac arrhythmias before their onset.

Abstract: A method and ultrasound imaging system includes generating a cine including a plurality of cardiac views based on the cardiac ultrasound data, segmenting a plurality of cardiac chambers from each of the plurality of cardiac images, and automatically determining a cardiac chamber area for each of the plurality of cardiac chambers. The method and ultrasound imaging system includes displaying the cine on a display device and displaying a plurality of single trace curves on the display device at the same time as the cine to provide feedback regarding an acquisition quality of the cine, wherein each of the single trace curves represents the cardiac chamber area for a different one of the plurality of cardiac chambers over the plurality of cardiac cycles.

Abstract: A method and ultrasound imaging system includes generating a cine including a plurality of cardiac views based on the cardiac ultrasound data, segmenting a plurality of cardiac chambers from each of the plurality of cardiac images, and automatically determining a cardiac chamber area for each of the plurality of cardiac chambers. The method and ultrasound imaging system includes displaying the cine on a display device and displaying a plurality of single trace curves on the display device at the same time as the cine to provide feedback regarding an acquisition quality of the cine, wherein each of the single trace curves represents the cardiac chamber area for a different one of the plurality of cardiac chambers over the plurality of cardiac cycles.