A sensitive and automatic white matter fiber tracts model for longitudinal analysis of diffusion tensor images in multiple sclerosis

Claudio Stamile, Gabriel Kocevar, François Cotton, Françoise Durand-Dubief, Salem Hannoun, Carole Frindel, Charles R.G. Guttmann, David Rousseau, Dominique Sappey-Marinier

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

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.

Original languageEnglish
Article numbere0156405
JournalPLoS ONE
Volume11
Issue number5
DOIs
Publication statusPublished - 2016 May

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