  Free-breathing cardiac MRI schemes are emerging as competitive alternatives
to breath-held cine MRI protocols, enabling applicability to pediatric and
other population groups that cannot hold their breath. Because the data from
the slices are acquired sequentially, the cardiac/respiratory motion patterns
may be different for each slice; current free-breathing approaches perform
independent recovery of each slice. In addition to not being able to exploit
the inter-slice redundancies, manual intervention or sophisticated
post-processing methods are needed to align the images post-recovery for
quantification. To overcome these challenges, we propose an unsupervised
variational deep manifold learning scheme for the joint alignment and
reconstruction of multislice dynamic MRI. The proposed scheme jointly learns
the parameters of the deep network as well as the latent vectors for each
slice, which capture the motion-induced dynamic variations, from the k-t space
data of the specific subject. The variational framework minimizes the
non-uniqueness in the representation, thus offering improved alignment and
reconstructions.

Image and Video Processing;Computer Vision and Pattern Recognition