Ultrasound system with a neural network for producing images from undersampled ultrasound data

Abstract

The present disclosure describes ultrasound imaging systems and methods configured to generate ultrasound images based on undersampled ultrasound data. The ultrasound images may be generated by applying a neural network trained with samples of known fully sampled data and undersampled data derived from the known fully sampled data to a acquired sparsely sampled data. The training of the neural network may involve training adversarial generative network including a generator and a discriminator. The generator is trained with sets of known undersampled data until the generator is capable of generating estimated image data, which the classifier is incapable of differentiation as either real or fake, and the trained generator may then be applied to unknown undersampled data.