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AJNR - American Journal of Neuroradiology

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American Journal of Neuroradiology 27:1032-1036, May 2006
© 2006 American Society of Neuroradiology

BRAIN

A Method for Clustering White Matter Fiber Tracts

L.J. O’Donnell^a,b,e, M. Kubicki^c, M.E. Shenton^c,d, M.H. Dreusicke^c, W.E.L. Grimson^a and C.F. Westin^a,e

^a MIT Computer Science and AI Lab (CSAIL) Cambridge, Mass
^b Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass
^c Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass
^d Clinical Neuroscience Division, Laboratory of Neuroscience, VA Boston Healthcare System, Brockton, Mass
^e Laboratory of Mathematics in Imaging, MRI Division, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Mass

Please address correspondence to: Lauren J. O’Donnell, SM, MIT Computer Science and AI Lab, 32 Vassar St, D540, Cambridge, MA 02139

BACKGROUND/PURPOSE: Despite its potential for visualizing white matter fiber tracts in vivo, diffusion tensor tractography has found only limited applications in clinical research in which specific anatomic connections between distant regions need to be evaluated. We introduce a robust method for fiber clustering that guides the separation of anatomically distinct fiber tracts and enables further estimation of anatomic connectivity between distant brain regions.

METHODS: Line scanning diffusion tensor images (LSDTI) were acquired on a 1.5T magnet. Regions of interest for several anatomically distinct fiber tracts were manually drawn; then, white matter tractography was performed by using the Runge-Kutta method to interpolate paths (fiber traces) following the major directions of diffusion, in which traces were seeded only within the defined regions of interest. Next, a fully automatic procedure was applied to fiber traces, grouping them according to a pairwise similarity function that takes into account the shapes of the fibers and their spatial locations.

RESULTS: We demonstrated the ability of the clustering algorithm to separate several fiber tracts which are otherwise difficult to define (left and right fornix, uncinate fasciculus and inferior occipitofrontal fasciculus, and corpus callosum fibers).

CONCLUSION: This method successfully delineates fiber tracts that can be further analyzed for clinical research purposes. Hypotheses regarding specific fiber connections and their abnormalities in various neuropsychiatric disorders can now be tested.

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