  In this work we address the problem of real-time dynamic MRI reconstruction.
There are a handful of studies on this topic; these techniques are either based
on compressed sensing or employ Kalman Filtering. These techniques cannot
achieve the reconstruction speed necessary for real-time reconstruction. In
this work, we propose a new approach to MRI reconstruction. We learn a
non-linear mapping from the unstructured aliased images to the corresponding
clean images using a stacked denoising autoencoder (SDAE). The training for
SDAE is slow, but the reconstruction is very fast - only requiring a few matrix
vector multiplications. In this work, we have shown that using SDAE one can
reconstruct the MRI frame faster than the data acquisition rate, thereby
achieving real-time reconstruction. The quality of reconstruction is of the
same order as a previous compressed sensing based online reconstruction
technique.
