Purpose: Compressed sensing methods with motion estimation and compensation techniques
have been proposed for the reconstruction of accelerated dynamic MRI. However, artifacts that
naturally arise in compressed sensing reconstruction procedures hinder the estimation of motion
from reconstructed images, especially at high acceleration factors. This work introduces a robust
groupwise non-rigid motion estimation technique applied to the compressed sensing reconstruction
of dynamic cardiac cine MRI sequences.
Theory and Methods: A spatio-temporal regularized, groupwise, non-rigid registration method
based on a B-splines deformation model and a least squares metric is used to estimate and to
compensate the movement of the heart in breath-hold cine acquisitions and to obtain a quasi-static
sequence with highly sparse representation in temporally transformed domains.
Results: Short axis in vivo datasets are used for validation, both original multi-coil as well as
DICOM data. Fully sampled data were retrospectively undersampled with various acceleration
factors and reconstructions were compared with the two well-known methods k-t FOCUSS and
MASTeR. The proposed method achieves higher signal to error ratio and structure similarity index
for medium to high acceleration factors.
Conclusions: Reconstruction methods based on groupwise registration show higher quality recon-
structions for cardiac cine images than the pairwise counterparts tested
