Meeting Banner
Abstract #2518

Optimized 3D Dictionary-Learning Compressed-Sensing Reconstruction for Quantitative Sodium Imaging in the Skeletal Muscle

Matthias Utzschneider1,2, Sebastian Lachner1, Nicolas G.R. Behl3, Lena V. Gast1, Andreas Maier2,4, Michael Uder1, and Armin Nagel1

1Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 2Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 3Division of Medical Physics in Radiology, German Cancer Research Centre (DKFZ), Heidelberg, Germany, 4Erlangen Graduate School in Advanced Optical Technologies, Erlangen, Germany

Quantitative sodium MRI could be a sensitive tool for therapy monitoring in muscular diseases. However, sodium MRI suffers a low signal-to-noise ratio (SNR). 3D dictionary-learning compressed-sensing (3D-DLCS) enables SNR improvement and acceleration of sodium MRI, but it is dependent on parameterization. In this work a simulation based optimization method for 3D-DLCS is presented, which finds the most suitable parameters for 3D-DLCS in the context of sodium quantification. The method is applied in an in vivo study to quantify sodium in the skeletal muscle. The optimized 3D-DLCS yields a lower quantification error than the reference reconstruction method (Nonuniform FFT).

This abstract and the presentation materials are available to members only; a login is required.

Join Here