Fiber-to-Bundle Registration of White Matter Tracts

Magnetic resonance diffusion tensor imaging (DTI) is being widely used to reconstruct brain white matter (WM) fiber tracts. For further characterization of the tracts, the fibers with similar courses often need to be grouped into a fiber bundle that corresponds to certain underlying WM anatomic structure. In addition, the alignments of fibers from different studies are often desirable for bundle comparisons and group analysis. In this work, a novel registration algorithm based on fiber-to-bundle matching was proposed to address the above two needs. Using an Expectation Maximization (EM) algorithm, the proposed method is capable of estimating a Thin-Plate-Spline transformation that optimally aligns whole-brain target fiber sets with a reference bundle model. Based on the resulting transformations, the fibers from different target datasets can all be warped into the reference coordinate system for comparisons and group analysis. The fibers can be further automatically labeled according to their similarity to the reference model. The algorithm was evaluated with eight human brain DTI data volumes acquired in vivo at 3T. After registration, the warped target bundles exhibit good similarity to the reference bundles. Quantitative experiments further demonstrated that the detected target bundles agree with ground truth obtained by manual segmentation at a sub-voxel accuracy.