Information-Theoretic Approach for Automated White Matter Fiber Tracts Reconstruction

Abstract

Fiber tracking is the most popular technique for creating white matter connectivity maps from diffusion tensor imaging (DTI). This approach requires a seeding process which is challenging because it is not clear how and where the seeds have to be placed. On the other hand, to enhance the interpretation of fiber maps, segmentation and clustering techniques are applied to organize fibers into anatomical structures. In this paper, we propose a new approach to automatically obtain bundles of fibers grouped into anatomical regions. This method applies an information-theoretic split-and-merge algorithm that considers fractional anisotropy and fiber orientation information to automatically segment white matter into volumes of interest (VOIs) of similar FA and eigenvector orientation. For each VOI, a number of planes and seeds is automatically placed in order to create the fiber bundles. The proposed approach avoids the need for the user to define seeding or selection regions. The whole process requires less than a minute and minimal user interaction. The agreement between the automated and manual approaches has been measured for 10 tracts in a DTI brain atlas and found to be almost perfect (kappa > 0.8) and substantial (kappa > 0.6). This method has also been evaluated on real DTI data considering 5tracts. Agreement was substantial (kappa > 0.6) in most of the cases.