  Compressed sensing for magnetic resonance imaging (CS-MRI) exploits image
sparsity properties to reconstruct MRI from very few Fourier k-space
measurements. The goal is to minimize any structural errors in the
reconstruction that could have a negative impact on its diagnostic quality. To
this end, we propose a deep error correction network (DECN) for CS-MRI. The
DECN model consists of three parts, which we refer to as modules: a guide, or
template, module, an error correction module, and a data fidelity module.
Existing CS-MRI algorithms can serve as the template module for guiding the
reconstruction. Using this template as a guide, the error correction module
learns a convolutional neural network (CNN) to map the k-space data in a way
that adjusts for the reconstruction error of the template image. Our
experimental results show the proposed DECN CS-MRI reconstruction framework can
considerably improve upon existing inversion algorithms by supplementing with
an error-correcting CNN.
