Abstract #2578
CURVELETS, A NEW SPARSE DOMAIN FOR DIFFUSION SPECTRUM IMAGING
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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