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Abstract #2578


Gabriel Varela 1 , Alexandra Tobisch 2,3 , Tony Stoecker 2 , and Pablo Irarrazaval 1,4

1 Biomedical Imaging Center - Pontificia Universidad Catolica de Chile, Santiago, Metropolitan District, Chile, 2 German Center of Neurological Diseases, North Rhine-Westphalia, Germany, 3 University of Bonn, North Rhine-Westphalia, Germany, 4 Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Metropolitan District, Chile

Compressed Sensing allows accelerating Diffusion Spectrum Imaging (DSI) acquisitions by reconstructing the Ensemble Average Propagator from a significantly reduced number of q-space samples. Nevertheless, the reconstruction performance is highly dependent on the sparse domain, which has not been fully studied for the specific DSI application. In this work we propose a new sparse domain based on Curvelets, a multi-resolution geometric analysis that incorporates explicitly an angular decomposition with parabolic scaling and location to characterize bounded curve-singularities in a sparse matter. We show that this domain allows even higher accelerating factors for DSI and thus significantly shortening the scan time.

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