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

Overview of attention for article published in Frontiers in Neuroinformatics, March 2014
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Title
Isotropic non-white matter partial volume effects in constrained spherical deconvolution
Published in
Frontiers in Neuroinformatics, March 2014
DOI 10.3389/fninf.2014.00028
Pubmed ID
Authors

Timo Roine, Ben Jeurissen, Daniele Perrone, Jan Aelterman, Alexander Leemans, Wilfried Philips, Jan Sijbers

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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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 3 5%
Belgium 2 3%
United Kingdom 1 2%
Netherlands 1 2%
Spain 1 2%
Unknown 58 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 24%
Researcher 15 23%
Student > Master 11 17%
Student > Bachelor 5 8%
Professor > Associate Professor 5 8%
Other 9 14%
Unknown 5 8%
Readers by discipline Count As %
Neuroscience 16 24%
Engineering 11 17%
Computer Science 7 11%
Medicine and Dentistry 7 11%
Physics and Astronomy 5 8%
Other 9 14%
Unknown 11 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 August 2016.
All research outputs
#6,587,685
of 23,577,654 outputs
Outputs from Frontiers in Neuroinformatics
#321
of 774 outputs
Outputs of similar age
#61,707
of 226,422 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#14
of 26 outputs
Altmetric has tracked 23,577,654 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 774 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 226,422 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.