Abstract: Systems, methods, and media for complex input data configurations for imaging applications. Complex data optimization can be provided to improve accuracy of models (e.g., neural networks) used to reconstruct medical images from raw sensor data, for example. Complex data optimization can include applying raw sensor data to an input layer of a neural network to generate an input vector ordered such that real components and imaginary components of samples in the raw sensor data are adjacent. The input vector can then be applied to convolutional layer of the neural network.

Abstract: Systems, methods, and media for patch-based medical image generation for complex input datasets. Patch-based medical image generation can include creating a training dataset with an image patch and corresponding sensor data patch and training a neural network using the training dataset. Then, sensor data acquired from a patient using a medical imaging system can be applied as input to the neural network, and a medical image of the patient can be generated based on an output of the neural network.