Application of diffusion tensor MRI to neurological segmentation

We consider the application of segmentation based on cluster classification techniques to a series of images derived from the diffusion tensor. The ex tension of a sophisticated cluster simulation tool used for optimizing data acquisition for such segmentation methods to diffusion-based images is des cribed. The characteristics of a variety of diffusion-based images includin g fractional anisotropy images, diffusion tensor trace images, and isotropi cally diffusion-weighted images are considered and their application to neu rological image segmentation is investigated. The critical effect of the si gnal-to-noise ratio on fractional anisotropy is analyzed and limitations of current echo planar-based strategies are discussed. Segmentation is shown to be possible using only images derived from the diffusion tensor, and suc h images are shown to offer exciting new avenues for neurological segmentat ion. (C) 1999 John Wiley & Sons, Inc.