4D MR imaging

Overview

The method aims to provide a clinical feasible setup and is therefore set up in the Gadgetron [Hansen and Sørensen, MRM 2013] framework.

The overall acquistion and reconstruction pipeline is illustrated in the following Figure:

Acquisition

A Cartesian 3D random Gaussian ESPReSSo subsampling is proposed which acquires a random combination of k-space lines k_y and k_z in each repetition, as illustrated in the Figure below. Periodically the central k-space lines are acquired which serve as a self-navigation signal in the reconstruction.

Reconstruction

After navigator signal extraction from the self-navigation data, the samples are sorted in a gating procedure into their respective motion states. The resulting subsampled k-space is reconstructed via a Compressed Sensing reconstruction.

Download

Source codes

• [current version] https://github.com/thomaskuestner/CS_MoCo_LAB
• [MRM paper version] https://github.com/thomaskuestner/4DMRImaging

Acquisition

The acquisition sequence, termed CS_Retro (Siemens, VB20P), can be downloaded here: https://github.com/thomaskuestner/4DMRImaging/tree/master/acquisition

The incorporated subsampling is based on: https://github.com/thomaskuestner/CS_LAB/tree/master/acquisition/subsampling_class

Reconstruction

Matlab/Gadgetron: https://github.com/thomaskuestner/4DMRImaging/tree/master/reconstruction

Please cite one of the papers, if you use it in a scientific publication.

References