Dynamic MRI using deep manifold self-learning

TL;DR

This paper introduces a deep self-learning approach that leverages autoencoders to learn the manifold structure of free-breathing cardiac MRI data, enabling improved image reconstruction from highly undersampled measurements.

Contribution

It presents a novel deep manifold self-learning algorithm that enhances cardiac MRI reconstruction by learning data structure directly from navigators, outperforming existing methods.

Findings

Better capture of manifold structure

Reduced spatial and temporal blurring

Improved reconstruction quality

Abstract

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.