  The goal of dynamic magnetic resonance imaging (dynamic MRI) is to visualize
tissue properties and their local changes over time that are traceable in the
MR signal. We propose a new variational approach for the reconstruction of
subsampled dynamic MR data, which combines smooth, temporal regularization with
spatial total variation regularization. In particular, it furthermore uses the
infimal convolution of two total variation Bregman distances to incorporate
structural a-priori information from an anatomical MRI prescan into the
reconstruction of the dynamic image sequence. The method promotes the
reconstructed image sequence to have a high structural similarity to the
anatomical prior, while still allowing for local intensity changes which are
smooth in time. The approach is evaluated using artificial data simulating
functional magnetic resonance imaging (fMRI), and experimental dynamic
contrast-enhanced magnetic resonance data from small animal imaging using
radial golden angle sampling of the k-space.
