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Sparse isotropic q-space sampling distribution for Compressed Sensing in DSI
German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile.
German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
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2014 (English)In: ISMRM-ESMRMB 2014, 2014Conference paper, Poster (Other academic)
Abstract [en]

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
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Medical Image Processing
URN: urn:nbn:se:liu:diva-110429OAI: diva2:745892
Joint Annual Meeting ISMRM-ESMRMB 2014, 10-17 May 2014, Milan, Italy
Swedish Research Council
Available from: 2014-09-11 Created: 2014-09-11 Last updated: 2014-10-08Bibliographically approved

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Knutsson, Hans
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Medical InformaticsThe Institute of TechnologyCenter for Medical Image Science and Visualization (CMIV)
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ReferencesLink to record
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