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Informed constrained spherical deconvolution (iCSD)

Timo Roine, Ben Jeurissen, Daniele Perrone, Jan Aelterman UGent, Wilfried Philips UGent, Alexander Leemans and Jan Sijbers (2015) MEDICAL IMAGE ANALYSIS. 24(1). p.269-281
abstract
Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE). These PVEs can be caused both by complex (e.g. crossing) WM fiber configurations and non-WM tissue, such as gray matter (GM) and cerebrospinal fluid. High angular resolution diffusion imaging methods have been developed to correctly characterize complex WM fiber configurations, but significant non-WM PVEs are also present in a large proportion of WM voxels. In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice, the RF is modified based on tissue fractions estimated from high-resolution anatomical data. Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
DIFFUSION-TENSOR MRI, FIBER ORIENTATION DISTRIBUTIONS, Tractography, Constrained spherical deconvolution, Partial volume effect, Fiber orientation, Diffusion MRI, TRACTOGRAPHY, B-MATRIX, SPIN-ECHO, WEIGHTED MRI, CROSSING FIBERS, HUMAN CONNECTOME PROJECT, WHITE-MATTER, LIVING HUMAN BRAIN
journal title
MEDICAL IMAGE ANALYSIS
volume
24
issue
1
pages
269 - 281
Web of Science type
Article
Web of Science id
000360252700020
JCR category
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
JCR impact factor
4.565 (2015)
JCR rank
4/104 (2015)
JCR quartile
1 (2015)
ISSN
1361-8415
DOI
10.1016/j.media.2015.01.001
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
5794925
handle
http://hdl.handle.net/1854/LU-5794925
date created
2015-01-06 10:38:01
date last changed
2016-12-19 15:43:32
@article{5794925,
  abstract     = {Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE). These PVEs can be caused both by complex (e.g. crossing) WM fiber configurations and non-WM tissue, such as gray matter (GM) and cerebrospinal fluid. High angular resolution diffusion imaging methods have been developed to correctly characterize complex WM fiber configurations, but significant non-WM PVEs are also present in a large proportion of WM voxels.
In constrained spherical deconvolution (CSD), the full fiber orientation distribution function (fODF) is deconvolved from clinically feasible DW data using a response function (RF) representing the signal of a single coherently oriented population of fibers. Non-WM PVEs cause a loss of precision in the detected fiber orientations and an emergence of false peaks in CSD, more prominently in voxels with GM PVEs. We propose a method, informed CSD (iCSD), to improve the estimation of fODFs under non-WM PVEs by modifying the RF to account for non-WM PVEs locally. In practice, the RF is modified based on tissue fractions estimated from high-resolution anatomical data.
Results from simulation and in-vivo bootstrapping experiments demonstrate a significant improvement in the precision of the identified fiber orientations and in the number of false peaks detected under GM PVEs. Probabilistic whole brain tractography shows fiber density is increased in the major WM tracts and decreased in subcortical GM regions. The iCSD method significantly improves the fiber orientation estimation at the WM-GM interface, which is especially important in connectomics, where the connectivity between GM regions is analyzed.},
  author       = {Roine, Timo and Jeurissen, Ben and Perrone, Daniele and Aelterman, Jan and Philips, Wilfried and Leemans, Alexander and Sijbers, Jan},
  issn         = {1361-8415},
  journal      = {MEDICAL IMAGE ANALYSIS},
  keyword      = {DIFFUSION-TENSOR MRI,FIBER ORIENTATION DISTRIBUTIONS,Tractography,Constrained spherical deconvolution,Partial volume effect,Fiber orientation,Diffusion MRI,TRACTOGRAPHY,B-MATRIX,SPIN-ECHO,WEIGHTED MRI,CROSSING FIBERS,HUMAN CONNECTOME PROJECT,WHITE-MATTER,LIVING HUMAN BRAIN},
  language     = {eng},
  number       = {1},
  pages        = {269--281},
  title        = {Informed constrained spherical deconvolution (iCSD)},
  url          = {http://dx.doi.org/10.1016/j.media.2015.01.001},
  volume       = {24},
  year         = {2015},
}

Chicago
Roine, Timo, Ben Jeurissen, Daniele Perrone, Jan Aelterman, Wilfried Philips, Alexander Leemans, and Jan Sijbers. 2015. “Informed Constrained Spherical Deconvolution (iCSD).” Medical Image Analysis 24 (1): 269–281.
APA
Roine, T., Jeurissen, B., Perrone, D., Aelterman, J., Philips, W., Leemans, A., & Sijbers, J. (2015). Informed constrained spherical deconvolution (iCSD). MEDICAL IMAGE ANALYSIS, 24(1), 269–281.
Vancouver
1.
Roine T, Jeurissen B, Perrone D, Aelterman J, Philips W, Leemans A, et al. Informed constrained spherical deconvolution (iCSD). MEDICAL IMAGE ANALYSIS. 2015;24(1):269–81.
MLA
Roine, Timo, Ben Jeurissen, Daniele Perrone, et al. “Informed Constrained Spherical Deconvolution (iCSD).” MEDICAL IMAGE ANALYSIS 24.1 (2015): 269–281. Print.