Automated determination of axonal orientation in the deep white matter of the human brain.

The wide-spread utilization of diffusion-weighted imaging in the clinical neurosciences to assess white-matter (WM) integrity and architecture calls for robust validation strategies applied to the data that are acquired with noninvasive imaging. However, the pathology and detailed fiber architecture of WM tissue can only be observed postmortem. With these considerations in mind, we designed an automated method for the determination of axonal orientation in high-resolution microscope images. The algorithm was tested on tissue that was stained using a silver impregnation technique that was optimized to resolve axonal fibers against very low levels of background. The orientation of individual nerve fibers was detected using spatial filtering and a template-matching algorithm, and the results are displayed as color-coded overlays. Quantitative models of WM fiber architecture at the microscopic level can lead to improved interpretation of low-resolution neuroimaging data and to more accurate mapping of fiber pathways in the human brain.