Improving compressed sensing reconstructions for myocardial perfusion imaging with residual artifact learning

Compressed sensing/constrained reconstruction methods have been successfully applied to myocardial perfusion imaging for improving in-plane resolution and improving slice coverage without losing temporal resolution. However at high acceleration factors and in the presence of large inter-time frame motion image quality from the CS methods is affected. Here we propose an artifact learning neural network that aims to improve the image quality of spatio-temporal constrained reconstruction methods for gated Cartesian and ungated radial data. Promising results are shown on datasets that were not used in training the neural network.