Frontiers in Neurology (Nov 2014)
Global tractography with embedded anatomical priors for quantitative connectivity analysis
Abstract
The main assumption of fiber-tracking algorithms is that fiber trajectories are represented by paths of highest diffusion, which is usually accomplished by following the principal diffusion directions estimated in every voxel from the measured diffusion MRI data. The state-of-the-art approaches, known as global tractography, reconstruct all the fiber tracts of the whole brain simultaneously by solving a global energy minimization problem. The tractograms obtained with these algorithms outperform any previous technique but, unfortunately, the price to pay is an increased computational cost which is not suitable in many practical settings, both in terms of time and memory requirements. Furthermore, existing global tractography algorithms suffer from an important shortcoming that is crucial in the context of brain connectivity analyses. As no anatomical priors are used during in 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 does not only unnecessarily slow down the estimation procedure and potentially biases any subsequent analysis but also, most importantly, prevents the de facto quantification of brain connectivity. In this work, we propose a novel approach for global tractography that is specifically designed for connectivity analysis applications by explicitly enforcing anatomical priors of the tracts in the optimization and considering 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 aloprithms.
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