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Abstract #3539

On the importance of adapting compressed sensing for images with significant spatial phase variations

Jérémie P. Fouquet1, Michael Paquette2, Réjean Lebel1, Maxime Descoteaux2, and Martin Lepage1

1Centre d'imagerie moléculaire de Sherbrooke, department of Médecine nucléaire et radiobiologie, Université de Sherbrooke, Sherbrooke, QC, Canada, 2Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada

We explore the limits of compressed sensing (CS) in the practical setting of T2*-weighted imaging of the brain. Surprisingly, those limits are rapidly reached due in part to the presence of spatial phase variations. While conventional CS performs well for synthetic phase-free images, it is equally performant to or even outperformed by a simple zero-padding of the k-space center for acquired complex-valued images. Clearly, CS must be adapted to images including spatial phase variations, and our results point toward new solutions to achieve this adaptation.

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