Meeting Banner
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.

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

Join Here