Abstract #2518

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

Matthias Utzschneider^1,2, Sebastian Lachner¹, Nicolas G.R. Behl³, Lena V. Gast¹, Andreas Maier^2,4, 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).

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