Diffusion tensor imaging (DTI) is an emerging and promising tool to provide information about the course of white matter fiber tracts in the human brain. Based on specific acquisition schemes, diffusion tensor data resemble local fiber orientations allowing for a reconstruction of the fiber bundles. Current techniques to calculate fascicles range from simple heuristic tracking solutions to Bayesian and differential equations approaches. Most methods are based only on local diffusion information, often resulting in bending or kinking fiber paths in voxels with reduced diffusion properties. In this article we present a new tracking approach based on linear state space models encompassing an inherent smoothness criterion to avoid too wiggly tracked fiber bundles. The new technique will be described formally and tested on simulated and real data. The performance tests are focused on the pyramidal tract, where we employed a test-retest study and a group comparison in healthy subjects. Anatomical course was confirmed in a patient with selective degeneration of the pyramidal tract. The potential of the presented technique for improved neurosurgical planning is demonstrated by visualization of a tumor-induced displacement of the motor pathways. The paper closes with a thorough discussion of perspectives and limitations of the new tracking approach.
2002 Elsevier Science (USA)