Abstract #1592
Sorted Compressed Sensing in MRI
Felipe Yanez 1,2 and Pablo Irarrazaval 1
1
Pontificia Universidad Catlica de Chile,
Santiago, Chile,
2
cole
Normale Suprieure, Paris, France
To improve the traditional Compressed Sensing (CS)
framework for image reconstruction, we propose a CS
technique with variable regularization parameter, which
penalizes the pixels of the recovered image according to
their magnitude. Herein, we present quantitative
susceptibility map (QSM) reconstructions in in-vivo
data, where the Sorted Compressed Sensing (SCS)
technique produced results that demonstrate it is
feasible to reconstruct high quality images. The
proposed method produced gains up to 5-6 dB with respect
of traditional CS.
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