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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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