Dynamic MRI Reconstruction from Undersampled Data with an Anatomical Prescan
For dynamic MRI reconstruction, this method leverages an anatomical prescan to improve reconstruction quality, but is evaluated only on limited datasets and lacks quantitative comparison to state-of-the-art methods.
The paper proposes a variational reconstruction method for undersampled dynamic MRI that incorporates anatomical prior information via infimal convolution of Bregman distances, achieving improved structural similarity and temporal smoothness. Evaluations on simulated fMRI and experimental DCE-MRI data show effective reconstruction.
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.