Sparse isotropic q-space sampling distribution for Compressed Sensing in DSI
2014 (English)In: ISMRM-ESMRMB 2014, 2014Conference paper, Poster (Other academic)
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.
Place, publisher, year, edition, pages
Medical Image Processing
IdentifiersURN: urn:nbn:se:liu:diva-110429OAI: oai:DiVA.org:liu-110429DiVA: diva2:745892
Joint Annual Meeting ISMRM-ESMRMB 2014, 10-17 May 2014, Milan, Italy
FunderSwedish Research Council