Quantitative white matter fiber analysis: a short history (Part III)

Part III: Quantitative tract-based analysis

This is the third post in this three part series. Parts I and II are here
and here.


It is useful to classify white matter data analysis in the three decades that followed the introduction of the first MRI scans as volume-based or tract-based. The preprocessing path flows and possibilities for data interpretation differ in these two approaches.

Volume-based Anatomical structures or regions of interest are treated as volumes
and quantitative information is smoothed or averaged in such a way that the local variation
in individual tracts is not preserved. The white matter structures are usually segmented by thresholding FA maps though fiber tracts have also been used. Group-wise registration for population studies may be done at the voxel level or across individual volumes. A voxel-based coordinate system is used in the first case and a structure-based coordinate system in the second.

Tract-based The emphasis is on fiber tracts and parameters that vary along a fiber or anatomically defined bundle. Diffusion indices such as FA and physical descriptors such as shape are typically studied to assess fiber integrity or changes due to disease.



Tract-based image analysis was made possible only after the first tractography algorithms were introduced. In 1999, Mori et al. ushered in tract-based image analysis by reconstructing fiber pathways in a rat brain. Improvements to the basic tractography algorithm and work in clustering paved the way for data analysis. These three stages of what is a developing field are summarized below.

I) Tractography
Tractography or fiber tracking, as the name suggests, is a way to follow the direction of the local white matter diffusion from voxel to voxel. For DTI, the simplest algorithms follow the direction of the principal diffusion tensor eigenvector in a deterministic fashion. The reconstruction process, which includes curvature thresholds and other termination criteria, generates a tract or streamline. More sophisticated approaches include interpolations for smoother pathways, the use of anatomical and topological constraints to guide the tracking and ways to deal with the uncertainty at each voxel due to noise and registration errors.
Streamline tractography is also used in conjunction with high angular resolution (HARDI) methods.

II) Clustering
The mass of DTI fibers rendered was not immediately available for analysis. To organize and pare them down into meaningful fiber tracts, clustering was used and this led to a study of these methods.

III) Mathematical Frameworks
(Due to the length of this post, I will cover the data analysis frameworks in my next post.)

Bibliography
Mori, S., Crain, B. J., Chacko, V. P. and Van Zijl, P. C. M. (1999), Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Annals of Neurology, 45:265–269.