  We propose a deep self-learning algorithm to learn the manifold structure of
free-breathing and ungated cardiac data and to recover the cardiac CINE MRI
from highly undersampled measurements. Our method learns the manifold structure
in the dynamic data from navigators using autoencoder network. The trained
autoencoder is then used as a prior in the image reconstruction framework. We
have tested the proposed method on free-breathing and ungated cardiac CINE
data, which is acquired using a navigated golden-angle gradient-echo radial
sequence. Results show the ability of our method to better capture the manifold
structure, thus providing us reduced spatial and temporal blurring as compared
to the SToRM reconstruction.
