Dynamic MRI Reconstruction from Undersampled Data with an Anatomical Prescan

TL;DR

This paper introduces a variational method for dynamic MRI reconstruction that integrates anatomical priors from prescans, improving structural fidelity while allowing temporal intensity variations.

Contribution

It presents a novel regularization technique combining total variation and anatomical priors via infimal convolution for dynamic MRI reconstruction.

Findings

Effective in preserving structural similarity to anatomical priors.

Capable of handling local intensity changes over time.

Validated on simulated and experimental MRI data.

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.