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Abstract #4451

Sparse isotropic q-space sampling distribution for Compressed Sensing in DSI

Alexandra Tobisch 1,2 , Gabriel Varela 3 , Rdiger Stirnberg 1 , Hans Knutsson 4 , Thomas Schultz 2,5 , Pablo Irarrzaval 3,6 , and Tony Stcker 1

1 German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany, 2 University of Bonn, Bonn, Germany, 3 Biomedical Imaging Center, Pontificia Universidad Catlica de Chile, Santiago, Metropolitan District, Chile, 4 Linkping University, Linkping, Sweden, 5 MPI for Intelligent Systems, Tbingen, Germany, 6 Department of Electrical Engineering, Pontificia Universidad Catlica de Chile, Santiago, Metropolitan District, Chile

The Compressed Sensing (CS) technique accelerates Diffusion Spectrum Imaging (DSI) through sub-Nyquist sampling in q-space and subsequent nonlinear reconstruction of the diffusion propagator. State-of-the-art DSI approaches that exploit CS apply Cartesian undersampling patterns. Recently, a method was proposed to generate 3D non-Cartesian sample distributions that aim for isotropic sampling of q-space. This work compares the new scheme to standard Cartesian undersampling patterns in sparse reconstruction of simulated diffusion signals. The diffusion propagator and the corresponding orientation distribution function of the reconstruction are found to deviate less from the ground truth when using an isotropic q-space sample distribution.

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