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      Isotropic non-white matter partial volume effects in constrained spherical deconvolution

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          Abstract

          Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a non-invasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. Significant partial volume effects (PVEs) are present in the DW signal due to relatively large voxel sizes. These PVEs can be caused by both non-WM tissue, such as gray matter (GM) and cerebrospinal fluid (CSF), and by multiple non-parallel WM fiber populations. High angular resolution diffusion imaging (HARDI) methods have been developed to correctly characterize complex WM fiber configurations, but to date, many of the HARDI methods do not account for non-WM PVEs. In this work, we investigated the isotropic PVEs caused by non-WM tissue in WM voxels on fiber orientations extracted with constrained spherical deconvolution (CSD). Experiments were performed on simulated and real DW-MRI data. In particular, simulations were performed to demonstrate the effects of varying the diffusion weightings, signal-to-noise ratios (SNRs), fiber configurations, and tissue fractions. Our results show that the presence of non-WM tissue signal causes a decrease in the precision of the detected fiber orientations and an increase in the detection of false peaks in CSD. We estimated 35–50% of WM voxels to be affected by non-WM PVEs. For HARDI sequences, which typically have a relatively high degree of diffusion weighting, these adverse effects are most pronounced in voxels with GM PVEs. The non-WM PVEs become severe with 50% GM volume for maximum spherical harmonics orders of 8 and below, and already with 25% GM volume for higher orders. In addition, a low diffusion weighting or SNR increases the effects. The non-WM PVEs may cause problems in connectomics, where reliable fiber tracking at the WM–GM interface is especially important. We suggest acquiring data with high diffusion-weighting 2500–3000 s/mm 2, reasonable SNR (~30) and using lower SH orders in GM contaminated regions to minimize the non-WM PVEs in CSD.

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          Most cited references28

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          Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient

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            Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution.

            Diffusion-weighted (DW) MR images contain information about the orientation of brain white matter fibres that potentially can be used to study human brain connectivity in vivo using tractography techniques. Currently, the diffusion tensor model is widely used to extract fibre directions from DW-MRI data, but fails in regions containing multiple fibre orientations. The spherical deconvolution technique has recently been proposed to address this limitation. It provides an estimate of the fibre orientation distribution (FOD) by assuming the DW signal measured from any fibre bundle is adequately described by a single response function. However, the deconvolution is ill-conditioned and susceptible to noise contamination. This tends to introduce artefactual negative regions in the FOD, which are clearly physically impossible. In this study, the introduction of a constraint on such negative regions is proposed to improve the conditioning of the spherical deconvolution. This approach is shown to provide FOD estimates that are robust to noise whilst preserving angular resolution. The approach also permits the use of super-resolution, whereby more FOD parameters are estimated than were actually measured, improving the angular resolution of the results. The method provides much better defined fibre orientation estimates, and allows orientations to be resolved that are separated by smaller angles than previously possible. This should allow tractography algorithms to be designed that are able to track reliably through crossing fibre regions.
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              MRtrix: Diffusion tractography in crossing fiber regions

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                Author and article information

                Contributors
                Journal
                Front Neuroinform
                Front Neuroinform
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Media S.A.
                1662-5196
                28 March 2014
                2014
                : 8
                : 28
                Affiliations
                [1] 1iMinds-Vision Lab, Department of Physics, University of Antwerp Antwerp, Belgium
                [2] 2Ghent University-iMinds/Image Processing and Interpretation Ghent, Belgium
                [3] 3Image Sciences Institute, University Medical Center Utrecht Utrecht, Netherlands
                Author notes

                Edited by: Daniele Marinazzo, University of Gent, Belgium

                Reviewed by: Fang-Cheng Yeh, Carnegie Mellon University, USA; Shawna Farquharson, The Florey Institute of Neuroscience and Mental Health, Australia

                *Correspondence: Timo Roine, iMinds-Vision Lab, Department of Physics, University of Antwerp, Universiteitsplein 1, Building N, 2610 Wilrijk, Antwerp, Belgium e-mail: timo.roine@ 123456uantwerpen.be

                This article was submitted to the journal Frontiers in Neuroinformatics.

                Article
                10.3389/fninf.2014.00028
                3975100
                24734018
                934aa991-4e10-4a10-a08d-3ada3da1f2d7
                Copyright © 2014 Roine, Jeurissen, Perrone, Aelterman, Leemans, Philips and Sijbers.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 06 December 2013
                : 02 March 2014
                Page count
                Figures: 8, Tables: 0, Equations: 1, References: 60, Pages: 9, Words: 0
                Categories
                Neuroscience
                Original Research Article

                Neurosciences
                diffusion mri,fiber orientation,partial volume effect,constrained spherical deconvolution,gray matter

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