TY - JOUR
T1 - A sensitive and automatic white matter fiber tracts model for longitudinal analysis of diffusion tensor images in multiple sclerosis
AU - Stamile, Claudio
AU - Kocevar, Gabriel
AU - Cotton, François
AU - Durand-Dubief, Françoise
AU - Hannoun, Salem
AU - Frindel, Carole
AU - Guttmann, Charles R.G.
AU - Rousseau, David
AU - Sappey-Marinier, Dominique
N1 - Publisher Copyright:
© 2016 Stamile et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2016/5
Y1 - 2016/5
N2 - Diffusion tensor imaging (DTI) is a sensitive tool for the assessment of microstructural alterations in brain white matter (WM). We propose a new processing technique to detect, local and global longitudinal changes of diffusivity metrics, in homologous regions along WM fiber-bundles. To this end, a reliable and automatic processing pipeline was developed in three steps: 1) co-registration and diffusion metrics computation, 2) tractography, bundle extraction and processing, and 3) longitudinal fiber-bundle analysis. The last step was based on an original Gaussian mixture model providing a fine analysis of fiber-bundle cross-sections, and allowing a sensitive detection of longitudinal changes along fibers. This method was tested on simulated and clinical data. High levels of F-Measure were obtained on simulated data. Experiments on cortico-spinal tract and inferior fronto-occipital fasciculi of five patients with Multiple Sclerosis (MS) included in a weekly follow-up protocol highlighted the greater sensitivity of this fiber scale approach to detect small longitudinal alterations.
AB - Diffusion tensor imaging (DTI) is a sensitive tool for the assessment of microstructural alterations in brain white matter (WM). We propose a new processing technique to detect, local and global longitudinal changes of diffusivity metrics, in homologous regions along WM fiber-bundles. To this end, a reliable and automatic processing pipeline was developed in three steps: 1) co-registration and diffusion metrics computation, 2) tractography, bundle extraction and processing, and 3) longitudinal fiber-bundle analysis. The last step was based on an original Gaussian mixture model providing a fine analysis of fiber-bundle cross-sections, and allowing a sensitive detection of longitudinal changes along fibers. This method was tested on simulated and clinical data. High levels of F-Measure were obtained on simulated data. Experiments on cortico-spinal tract and inferior fronto-occipital fasciculi of five patients with Multiple Sclerosis (MS) included in a weekly follow-up protocol highlighted the greater sensitivity of this fiber scale approach to detect small longitudinal alterations.
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U2 - 10.1371/journal.pone.0156405
DO - 10.1371/journal.pone.0156405
M3 - Article
C2 - 27224308
AN - SCOPUS:84971474844
VL - 11
JO - PLoS One
JF - PLoS One
SN - 1932-6203
IS - 5
M1 - e0156405
ER -