Tractography algorithms provide us with the ability to non-invasively reconstruct fiber pathways in the white matter by exploiting the directional information described with diffusion MR. These methods could be divided into two major classes, local and global. Local methods reconstruct each fiber tract iteratively by considering only directional information at the voxel level and its neighborhood. Global methods, on the other hand, reconstruct all the fiber tracts of the whole brain simultaneously by solving a global energy minimization problem.

The latter have shown improvements compared to previous techniques but these algorithms still suffer from an important shortcoming that is crucial in the context of brain connectivity analyses. As no anatomical priors are usually considered during the reconstruction process, the recovered fiber tracts are not guaranteed to connect cortical regions and, as a matter of fact, most of them stop prematurely in the white matter; this violates an important properties of neural connections, that are known to originate in the gray matter and develop in the white matter. Hence, this shortcoming poses serious limitations for the use of these techniques for the assessment of the structural connectivity between brain regions and, de facto, it can potentially bias any subsequent analysis. Moreover, the estimated tracts are not quantitative, every fiber contributes with the same weight toward the predicted diffusion signal.

In this work, we propose a novel approach for global tractography that is specifically designed for connectivity analysis applications which: (i) explicitly enforces anatomical priors of the tracts in the optimization and (ii) considers the effective contribution of each of them, i.e. volume, to the acquired diffusion MRI image. We evaluated our approach on both a realistic diffusion MRI phantom and in-vivo data, and also compared its performance to existing tractography algorithms.

Reference

Global tractography with embedded anatomical priors for quantitative connectivity analysis
Alia Lemkaddem, Didrik Skiöldebrand, Alessandro Dal Palú, Jean-Philippe Thiran and Alessandro Daducci
Frontiers in Neurology - Brain Imaging Methods

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