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Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Elekta Instrument AB, Kungstensgatan 18, Box 7593, SE-103 93 Stockholm, Sweden.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning. Linköping University, Faculty of Arts and Sciences.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-9091-4724
2017 (English)In: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, 778-782 p.Conference paper, Published paper (Refereed)
Abstract [en]

We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in qspace. By estimating the signal on a grid, we can do synthetic diffusion spectrum imaging: reconstructing the ensemble averaged propagator (EAP) by an inverse Fourier transform. We also propose an alternative reconstruction method guaranteeing a nonnegative EAP that integrates to unity. The reconstruction is validated on data simulated from two Gaussians at various crossing angles. Moreover, we demonstrate on nonuniformly sampled in vivo data that the method is far superior to linear interpolation, and allows a drastic undersampling of the data with only a minor loss of accuracy. We envision the method as a potential replacement for standard diffusion spectrum imaging, in particular when acquistion time is limited.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. 778-782 p.
Series
International Symposium on Biomedical Imaging. Proceedings, ISSN 1945-8452
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:liu:diva-138632DOI: 10.1109/ISBI.2017.7950634ISI: 000414283200181ISBN: 978-1-5090-1172-8 (electronic)ISBN: 978-1-5090-1173-5 (print)OAI: oai:DiVA.org:liu-138632DiVA: diva2:1112410
Conference
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia, 18-21 April 2017
Note

Funding agencies: Swedish Research Council (VR) [2012-4281, 2013-5229, 2015-05356]; Swedish Foundation for Strategic Research (SSF) [AM13-0090]; EUREKA ITEA BENEFIT [2014-00593]; Linneaus center CADICS; NIDCR; NIMH; NINDS

Available from: 2017-06-20 Created: 2017-06-20 Last updated: 2017-12-15Bibliographically approved

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Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging(665 kB)44 downloads
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Özarslan, Evren

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Sjölund, JensEklund, AndersÖzarslan, EvrenKnutsson, Hans
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