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D-BRAIN: anatomically accurate simulated diffusion MRI brain data

Daniele Perrone, Ben Jeurissen, Jan Aelterman UGent, Timo Roine, Jan Sijbers, Aleksandra Pizurica UGent, Alexander Leemans and Wilfried Philips UGent (2016) PLOS ONE. 11(3).
abstract
Diffusion Weighted (DW) MRI allows for the non-invasive study of water diffusion inside living tissues. As such, it is useful for the investigation of human brain white matter (WM) connectivity in vivo through fiber tractography (FT) algorithms. Many DW-MRI tailored restoration techniques and FT algorithms have been developed. However, it is not clear how accurately these methods reproduce the WM bundle characteristics in real-world conditions, such as in the presence of noise, partial volume effect, and a limited spatial and angular resolution. The difficulty lies in the lack of a realistic brain phantom on the one hand, and a sufficiently accurate way of modeling the acquisition-related degradation on the other. This paper proposes a software phantom that approximates a human brain to a high degree of realism and that can incorporate complex brain-like structural features. We refer to it as a Diffusion BRAIN (D-BRAIN) phantom. Also, we propose an accurate model of a (DW) MRI acquisition protocol to allow for validation of methods in realistic conditions with data imperfections. The phantom model simulates anatomical and diffusion properties for multiple brain tissue components, and can serve as a ground-truth to evaluate FT algorithms, among others. The simulation of the acquisition process allows one to include noise, partial volume effects, and limited spatial and angular resolution in the images. In this way, the effect of image artifacts on, for instance, fiber tractography can be investigated with great detail. The proposed framework enables reliable and quantitative evaluation of DW-MR image processing and FT algorithms at the level of large-scale WM structures. The effect of noise levels and other data characteristics on cortico-cortical connectivity and tractographybased grey matter parcellation can be investigated as well.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
FIBER ORIENTATION DISTRIBUTIONS, CONSTRAINED SPHERICAL DECONVOLUTION, STRUCTURAL CORTICAL NETWORKS, MAGNETIC-RESONANCE IMAGES, WHITE-MATTER, TENSOR TRACTOGRAPHY, WEIGHTED MRI, TOPOLOGICAL ORGANIZATION, ALZHEIMERS-DISEASE, HUMAN CONNECTOME
journal title
PLOS ONE
volume
11
issue
3
article number
e0149778
Web of Science type
Article
Web of Science id
000371434500056
JCR category
MULTIDISCIPLINARY SCIENCES
JCR impact factor
2.806 (2016)
JCR rank
15/64 (2016)
JCR quartile
1 (2016)
ISSN
1932-6203
DOI
10.1371/journal.pone.0149778
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
7125693
handle
http://hdl.handle.net/1854/LU-7125693
date created
2016-03-02 11:48:12
date last changed
2017-09-15 14:03:49
@article{7125693,
  abstract     = {Diffusion Weighted (DW) MRI allows for the non-invasive study of water diffusion inside living tissues. As such, it is useful for the investigation of human brain white matter (WM) connectivity in vivo through fiber tractography (FT) algorithms. Many DW-MRI tailored restoration techniques and FT algorithms have been developed. However, it is not clear how accurately these methods reproduce the WM bundle characteristics in real-world conditions, such as in the presence of noise, partial volume effect, and a limited spatial and angular resolution. The difficulty lies in the lack of a realistic brain phantom on the one hand, and a sufficiently accurate way of modeling the acquisition-related degradation on the other. This paper proposes a software phantom that approximates a human brain to a high degree of realism and that can incorporate complex brain-like structural features. We refer to it as a Diffusion BRAIN (D-BRAIN) phantom. Also, we propose an accurate model of a (DW) MRI acquisition protocol to allow for validation of methods in realistic conditions with data imperfections. The phantom model simulates anatomical and diffusion properties for multiple brain tissue components, and can serve as a ground-truth to evaluate FT algorithms, among others. The simulation of the acquisition process allows one to include noise, partial volume effects, and limited spatial and angular resolution in the images. In this way, the effect of image artifacts on, for instance, fiber tractography can be investigated with great detail. The proposed framework enables reliable and quantitative evaluation of DW-MR image processing and FT algorithms at the level of large-scale WM structures. The effect of noise levels and other data characteristics on cortico-cortical connectivity and tractographybased grey matter parcellation can be investigated as well.},
  articleno    = {e0149778},
  author       = {Perrone, Daniele and Jeurissen, Ben and Aelterman, Jan and Roine, Timo and Sijbers, Jan and Pizurica, Aleksandra and Leemans, Alexander and Philips, Wilfried},
  issn         = {1932-6203},
  journal      = {PLOS ONE},
  keyword      = {FIBER ORIENTATION DISTRIBUTIONS,CONSTRAINED SPHERICAL DECONVOLUTION,STRUCTURAL CORTICAL NETWORKS,MAGNETIC-RESONANCE IMAGES,WHITE-MATTER,TENSOR TRACTOGRAPHY,WEIGHTED MRI,TOPOLOGICAL ORGANIZATION,ALZHEIMERS-DISEASE,HUMAN CONNECTOME},
  language     = {eng},
  number       = {3},
  title        = {D-BRAIN: anatomically accurate simulated diffusion MRI brain data},
  url          = {http://dx.doi.org/10.1371/journal.pone.0149778},
  volume       = {11},
  year         = {2016},
}

Chicago
Perrone, Daniele, Ben Jeurissen, Jan Aelterman, Timo Roine, Jan Sijbers, Aleksandra Pizurica, Alexander Leemans, and Wilfried Philips. 2016. “D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data.” Plos One 11 (3).
APA
Perrone, D., Jeurissen, B., Aelterman, J., Roine, T., Sijbers, J., Pizurica, A., Leemans, A., et al. (2016). D-BRAIN: anatomically accurate simulated diffusion MRI brain data. PLOS ONE, 11(3).
Vancouver
1.
Perrone D, Jeurissen B, Aelterman J, Roine T, Sijbers J, Pizurica A, et al. D-BRAIN: anatomically accurate simulated diffusion MRI brain data. PLOS ONE. 2016;11(3).
MLA
Perrone, Daniele, Ben Jeurissen, Jan Aelterman, et al. “D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data.” PLOS ONE 11.3 (2016): n. pag. Print.