Directional correlation in white matter tracks of the human brain

To describe a technique for the detection of distinct brain fibers in sets of magnetic resonance (MR) diffusion tensor imaging (DTI) data.

MR-DTI can be used for a tractography of brain fibers presuming a data set of high spatial resolution and high signal to noise. A less demanding technique for the visualization of discrete brain fiber bundles involves segmentation. By using a region-growing algorithm, those voxels that have a direction similar to that of the major eigenvector in neighboring voxels of a data set can be marked. It has been shown recently by Mori et al (1) that this technique can be successfully applied to data from a single slice of a mouse brain. In this study, the segmentation technique was applied with modifications to multislice DTI data from the human brain.

A distinct segmentation of various brain fiber bundles could be achieved by the use of a two-step algorithm. In the first step, voxels within large fiber tracts-such as corticofugal tracts (e.g., corticospinal tract) and the optic radiation-were segmented by starting the region-growing algorithm in the corpus callosum (CC) and erasing this major structure from the data set. In the second step, remaining voxels were segmented by the same algorithm; this revealed a good assignment of the similarly oriented fibers derived by segmentation to the anatomically given brain lobes. This two-step procedure was successfully applied to DTI data of six healthy volunteers.

The segmentation technique for DTI data proposed by Mori et al (1) for data from mouse brains can be applied to multislice data from the human brain by using a two-step algorithm including a masking of the major fiber tracts.