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  Real diffusion-weighted MRI enabling true signal averaging and increased diffusion contrast

Eichner, C., Cauley, S. F., Cohen-Adad, J., Möller, H. E., Turner, R., Stesompop, K., et al. (2015). Real diffusion-weighted MRI enabling true signal averaging and increased diffusion contrast. NeuroImage, 122, 373-384. doi:10.1016/j.neuroimage.2015.07.074.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0028-DDCF-D Version Permalink: http://hdl.handle.net/21.11116/0000-0003-14A8-9
Genre: Journal Article

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 Creators:
Eichner, Cornelius1, 2, Author              
Cauley, Stephen F.2, Author
Cohen-Adad, Julien3, Author
Möller, Harald E.1, Author              
Turner, Robert4, Author              
Stesompop, Kawim2, Author
Wald, Lawrence L.2, Author
Affiliations:
1Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
2Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA, USA, ou_persistent22              
3École Polytechnique de Montréal, University of Montréal, QC, Canada, ou_persistent22              
4Department Neurophysics, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634550              

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 Abstract: This project aims to characterize the impact of underlying noise distributions on diffusion-weighted imaging. The noise floor is a well-known problem for traditional magnitude-based diffusion-weighted MRI (dMRI) data, leading to biased diffusion model fits and inaccurate signal averaging. Here, we introduce a total-variation-based algorithm to eliminate shot-to-shot phase variations of complex-valued diffusion data with the intention to extract real-valued dMRI datasets. The obtained real-valued diffusion data are no longer superimposed by a noise floor but instead by a zero-mean Gaussian noise distribution, yielding dMRI data without signal bias. We acquired high-resolution dMRI data with strong diffusion weighting and, thus, low signal-to-noise ratio. Both the extracted real-valued and traditional magnitude data were compared regarding signal averaging, diffusion model fitting and accuracy in resolving crossing fibers. Our results clearly indicate that real-valued diffusion data enables idealized conditions for signal averaging. Furthermore, the proposed method enables unbiased use of widely employed linear least squares estimators for model fitting and demonstrates an increased sensitivity to detect secondary fiber directions with reduced angular error. The use of phase-corrected, real-valued data for dMRI will therefore help to clear the way for more detailed and accurate studies of white matter microstructure and structural connectivity on a fine scale.

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Language(s): eng - English
 Dates: 2014-10-272015-07-242015-08-012015-11-15
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2015.07.074
PMID: 26241680
PMC: PMC4651971
Other: Epub 2015
 Degree: -

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Title: NeuroImage
Source Genre: Journal
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Pages: - Volume / Issue: 122 Sequence Number: - Start / End Page: 373 - 384 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166