A system for generating ultrasound images, the system comprising: at least one storage device; and at least one processor operatively coupled to the storage device; and at least one non-transitory computer readable storage medium storing instructions thereon, that, when executed by the at least one processor, cause the processor to: receive undersampled ultrasound data; produce modified ultrasound data by modifying, using a neural network, the undersampled ultrasound data to represent sufficiently-sampled ultrasound data; and generate an ultrasound image based on the modified ultrasound data, characterized in that the neural network is trained by: providing a first ultrasound data set (302, 506) comprising sufficiently-sampled ultrasound data; reducing sampling of the first ultrasound data set to produce a second ultrasound data set (304, 508) with missing data; coupling the second ultrasound data set to one or more neural networks (310, 504) including the neural network to generate new data estimated to represent the missing data; producing a third ultrasound data set (306, 510) by modifying the second ultrasound data set to include the new data; classifying, by the one or more neural networks (320, 502), the third ultrasound data set as either real or fake; and adjusting activation rules for one or more nodes of the one or more neural networks based on an accuracy of the classifying. at least one storage device; and at least one processor operatively coupled to the storage device; and at least one non-transitory computer readable storage medium storing instructions thereon, that, when executed by the at least one processor, cause the processor to: receive undersampled ultrasound data; produce modified ultrasound data by modifying, using a neural network, the undersampled ultrasound data to represent sufficiently-sampled ultrasound data; and generate an ultrasound image based on the modified ultrasound data, characterized in that the neural network is trained by: providing a first ultrasound data set (302, 506) comprising sufficiently-sampled ultrasound data; reducing sampling of the first ultrasound data set to produce a second ultrasound data set (304, 508) with missing data; coupling the second ultrasound data set to one or more neural networks (310, 504) including the neural network to generate new data estimated to represent the missing data; producing a third ultrasound data set (306, 510) by modifying the second ultrasound data set to include the new data; classifying, by the one or more neural networks (320, 502), the third ultrasound data set as either real or fake; and adjusting activation rules for one or more nodes of the one or more neural networks based on an accuracy of the classifying. receive undersampled ultrasound data; produce modified ultrasound data by modifying, using a neural network, the undersampled ultrasound data to represent sufficiently-sampled ultrasound data; and generate an ultrasound image based on the modified ultrasound data, characterized in that the neural network is trained by: providing a first ultrasound data set (302, 506) comprising sufficiently-sampled ultrasound data; reducing sampling of the first ultrasound data set to produce a second ultrasound data set (304, 508) with missing data; coupling the second ultrasound data set to one or more neural networks (310, 504) including the neural network to generate new data estimated to represent the missing data; producing a third ultrasound data set (306, 510) by modifying the second ultrasound data set to include the new data; classifying, by the one or more neural networks (320, 502), the third ultrasound data set as either real or fake; and adjusting activation rules for one or more nodes of the one or more neural networks based on an accuracy of the classifying. providing a first ultrasound data set (302, 506) comprising sufficiently-sampled ultrasound data; reducing sampling of the first ultrasound data set to produce a second ultrasound data set (304, 508) with missing data; coupling the second ultrasound data set to one or more neural networks (310, 504) including the neural network to generate new data estimated to represent the missing data; producing a third ultrasound data set (306, 510) by modifying the second ultrasound data set to include the new data; classifying, by the one or more neural networks (320, 502), the third ultrasound data set as either real or fake; and adjusting activation rules for one or more nodes of the one or more neural networks based on an accuracy of the classifying.