Diffusion Tensor Imaging (DTI) is a MRI-based neuroimaging technique which makes it possible to visualize the location, orientation, and anisotropy of the brain's white matter tracts.
The architecture of the axons in parallel bundles and their myelin shield facilitate the diffusion of the water molecules along their main direction. If we apply diffusion gradients in at least 6 non-collinear directions, it is possible to calculate, for each pixel, a tensor (i.e. a 3*3 matrix) that describes this diffusion anisotropy. The fiber's direction is indicated by the tensor’s main eigenvector. This vector can be color-coded, yielding a cartography of the tracts’ position, direction (red for right-left, blue for foot-head, green for anterior-posterior), and anisotropy (as indicated by the tract's brightness). In addition, the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) can be quantified.
Fiber tracking uses the diffusion tensor to track fibers along their whole length. Starting from a seed ROI, generally defined manually, the fiber tracking algorithm looks for adjacent voxels whose main diffusion direction is in the continuity of the previous one. The most tracked fiber bundle is the cortico-spinal (and associated) tract, but fiber tracking can identify most of the brain's white matter tracts.
The diffusion tensor imaging model assumes that, in each voxel, there is a unique orientation of the fibers, the direction of which is represented by the tensor’s main eigenvector (Mori and Tournier, 2013). This assumption is not valid in case of crossing fibers. The term “crossing fibers” generally refers to regions in which the fibers’ orientation is not unique, i.e. when the fibers are interdigitating, brushing past each other, curving, bending or diverging (Mori and Tournier, 2013). Higher order models, such as constrained spherical deconvolution, have been developed in order to address the issue of crossing fibers.
By Dr. Laurent Hermoye
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