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 autoencodernetwork. The trained autoencoder is then used as aprior in the image reconstruction framework. We have testedthe proposed method on free-breathing and ungated cardiacCINE data, which is acquired using a navigated golden-anglegradient-echo radial sequence. Results show the ability ofour method to better capture the manifold structure, thus providingus reduced spatial and temporal blurring as comparedto the SToRM reconstruction.
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