2017/11/30 by Julian Rasch, Rasch, Julian, Ville Kolehmainen +11
Medicine · #Advanced MRI Techniques and Applications #Advanced Neuroimaging Techniques and Applications #FOS: Mathematics #Medical Imaging Techniques and Applications #Numerical Analysis (math.NA)
paper · pdf · doi:10.48550/arxiv.1712.00099
openalex publication_date 2017/11/30 · openalex created_date 2022/08/06 · openalex updated_date 2026/07/28
The goal of dynamic magnetic resonance imaging (dynamic MRI) is to visualize\ntissue properties and their local changes over time that are traceable in the\nMR signal. We propose a new variational approach for the reconstruction of\nsubsampled dynamic MR data, which combines smooth, temporal regularization with\nspatial total variation regularization. In particular, it furthermore uses the\ninfimal convolution of two total variation Bregman distances to incorporate\nstructural a-priori information from an anatomical MRI prescan into the\nreconstruction of the dynamic image sequence. The method promotes the\nreconstructed image sequence to have a high structural similarity to the\nanatomical prior, while still allowing for local intensity changes which are\nsmooth in time. The approach is evaluated using artificial data simulating\nfunctional magnetic resonance imaging (fMRI), and experimental dynamic\ncontrast-enhanced magnetic resonance data from small animal imaging using\nradial golden angle sampling of the k-space.\n