Magnetic resonance diffusion tensor imaging is being widely used to reconstruct brain white matter fiber tracts. To characterize structural properties of the tracts, reconstructed fibers are often grouped into bundles that correspond to coherent anatomic structures. For further group analysis of fiber bundles, it is desirable that corresponding bundles from different studies are coregistered. To address these needs simultaneously, a unified fiber bundling and registration (UFIBRE) framework is proposed in this work. The framework is based on maximizing a posteriori Bayesian probabilities using an expectation maximization algorithm. Given a set of segmented template bundles and a whole-brain target fiber set, the UFIBRE algorithm optimally bundles the target fibers and registers them with the template. The bundling component in the UFIBRE algorithm simplifies fiber-based registration into bundle-to-bundle registration, and the registration component in turn guides the bundling process to find bundles consistent with the template. Experiments with in vivo data demonstrate that the estimated bundles have an approximately 80% consistency with ground truth and the root mean square error between their bundle medial axes is less than one voxel. The proposed algorithm is highly efficient, offering potential routine use for group analysis of white matter fibers.

To improve the accuracy of tissue structural and architectural characterization with diffusion tensor imaging, an anisotropic smoothing algorithm is presented for reducing noise in diffusion tensor images efficiently and effectively. The presented algorithm is based on previous anisotropic diffusion filtering, which is implemented with a straightforward but inefficient explicit numerical scheme. The main contribution of this paper is to improve the performance of the previous method considerably by using unconditionally stable and second order time accurate semi-implicit scheme. Our new method needs only few or even one iteration to achieve better smoothed images than what is generated by tens of iterations of the previous method, which makes it more attractive to practical use. Experiments with simulated and in vivo data have demonstrated the advantage of our new algorithm for denoising diffusion tensor images in terms of efficiency and effectiveness.