Joint alignment and reconstruction of multislice dynamic MRI using variational manifold learning
This work addresses the challenge of manual intervention and lack of inter-slice redundancy exploitation in free-breathing cardiac MRI for populations like pediatric patients who cannot hold their breath, offering an incremental improvement over current independent slice recovery methods.
The authors tackled the problem of reconstructing and aligning multislice dynamic MRI data from free-breathing cardiac scans, where motion patterns vary per slice, by proposing an unsupervised variational deep manifold learning scheme that jointly learns network parameters and latent vectors from k-t space data, resulting in improved alignment and reconstructions.
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.