Diffusion tensor MR imaging (DTI) is an advanced technique, born out of conventional diffusion-weighted imaging, that is able to characterize anisotropic diffusion in the central nervous system. This chapter will focus on the physical principles underlying DTI, including its foundation in probability theory and the use of a Gaussian model of diffusion in three dimensions. A review of conventional diffusion-weighted MR imaging and the determination of apparent diffusion coefficients is provided and then extended to the case of multidirectional DTI acquisition. The procedure for filling a diffusion tensor matrix and extracting its eigenvalues and eigenvectors is explained, and the role of these parameters in shaping a Gaussian “ellipsoid” into a visual representation of anisotropic diffusion is presented. The dominant forms of tensor ellipsoids (spherical, oblate, and prolate) are introduced along with an overview of how directional DTI information is rendered as an image. Scalar metrics derived from the diffusion tensor eigenvalues, including fractional anisotropy (FA), are discussed, followed by an introduction to white matter fiber tracking (“tractography”). Inherent limitations of the tensor model are highlighted throughout the discussion, including the nonspecific nature of anisotropy measurements, awareness of non-prolate tensors in the brain, and the inability of the tensor model to resolve crossing fibers within a voxel. We conclude with a brief look at several promising techniques that move beyond the Gaussian model of diffusion.
|Original language||English (US)|
|Title of host publication||Functional Neuroradiology|
|Subtitle of host publication||Principles and Clinical Applications|
|Number of pages||21|
|State||Published - Jan 1 2012|
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)