Abstract #1029
Comparing Fourier to SHORE Basis Functions for Sparse DSI Reconstruction
Alexandra Tobisch 1,2 , Thomas Schultz 2 , Rdiger Stirnberg 1 , Gabriel Varela 3 , Hans Knutsson 4 , Pablo Irarrzaval 3,5 , and Tony Stcker 1,6
1
German Center for Neurodegenerative
Diseases, Bonn, Germany,
2
Department
of Computer Science, University of Bonn, Bonn, Germany,
3
Biomedical
Imaging Center, Pontificia Universidad Catlica de
Chile, Santiago, Chile,
4
Linkping
University, Linkping, Sweden,
5
Department
of Electrical Engineering, Pontificia Universidad
Catlica de Chile, Santiago, Chile,
6
Department
of Physics and Astronomy, University of Bonn, Bonn,
Germany
Compressed Sensing (CS) theory accelerates Diffusion
Spectrum Imaging (DSI) acquisition, while still
providing high angular and radial resolution of
intra-voxel microstructure. Several groups have proposed
to reconstruct the diffusion propagator from sparse
q-space samples by fitting continuous basis functions.
Among these, the SHORE basis has recently been found to
perform best. This work compares the SHORE-based
approach to traditional CS recovery that combines the
discrete Fourier transform with a sparsity term. For
simulated diffusion signals, the CS reconstruction is
found to deviate less from the ground truth when using
Fourier basis functions for sparse DSI reconstruction.
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