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PLoS One. 2016 May 25;11(5):e0156405. doi: 10.1371/journal.pone.0156405. eCollection 2016.

A Sensitive and Automatic White Matter Fiber Tracts Model for Longitudinal Analysis of Diffusion Tensor Images in Multiple Sclerosis.

Author information

1
CREATIS CNRS UMR5220 & INSERM U1044, Université de Lyon, Université Claude Bernard-Lyon 1, INSA-Lyon, Villeurbanne, France.
2
Service de Radiologie, Centre Hospitalier Lyon-Sud, Hospices Civils de Lyon, Pierre-Bénite, France.
3
Service de Neurologie A, Hôpital Neurologique, Hospices Civils de Lyon, Bron, France.
4
Center for Neurological Imaging, Departments of Radiology and Neurology, Brigham and Women's Hospital, Boston, MA, United States of America.
5
CERMEP - Imagerie du Vivant, Université de Lyon, Bron, France.

Abstract

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.

PMID:
27224308
PMCID:
PMC4880200
DOI:
10.1371/journal.pone.0156405
[Indexed for MEDLINE]
Free PMC Article

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