Gibbs tracking: A novel approach for the reconstruction of neuronal pathways

Reconstruction of neuronal fibers using diffusion-weighted (DW) MRI is an emerging method in biomedical research. Ex-isting fiber-tracking algorithms are commonly based on the “walker principle. ” Fibers are reconstructed as trajectories of “walkers, ” which are guided according to local diffusion prop-erties. In this study, a new method of fiber tracking is proposed that does not engage any “walking ” algorithm. It resolves a number of inherent problems of the “walking ” approach, in particular the reconstruction of crossing and spreading fibers. In the proposed method, the fibers are built with small line elements. Each line element contributes an anisotropic term to the simulated DW signal, which is adjusted to the measured signal. This method demonstrates good results for simulated fibers. A single in vivo result demonstrates the successful re-construction of the dominant neuronal pathways. A comparison with the diffusion tensor imaging (DTI)-based fiber assignment with continuous tracking (FACT) method and the probabilistic index of connectivity (PICo) method based on a multitensor model is performed for the callosal fibers. The result shows a strong increase in the number of reconstructed fibers. These almost fill the total white matter (WM) volume and connect a large area of the cortex. The method is very computationally expensive. Possible ways to address this problem ar