Dynamic MRI reconstruction from undersampled data with an anatomical prescan. (4th June 2018)

• Record Type: Journal Article
• Authors: Rasch, Julian; Kolehmainen, Ville; Nivajärvi, Riikka; Kettunen, Mikko; Gröhn, Olli; Burger, Martin; Brinkmann, Eva-Maria
• Is Part Of: Inverse problems. Volume 34:Number 7(2018:Jul.)
• Publication Date: 2018-06-04
• Subjects: dynamic magnetic resonance imaging -- spatio-temporal regularization -- structural prior -- infimal convolution of Bregman distances -- total variation -- golden angle subsampling; Inverse problems (Differential equations) -- Periodicals; 515.357
• DOI: 10.1088/1361-6420/aac3af
• Languages: English
• ISSNs: 0266-5611

Abstract

Abstract: 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.