Estimating the complete set of white matter fascicles (the projectome) from diffusion data requires evaluating an enormous number of potential pathways; consequently, most algorithms use computationally efficient greedy methods to search for pathways. The limitation of this approach is that critical global parameters - such as data prediction error and white matter volume conservation - are not taken into account. We describe BlueMatter, a parallel algorithm for global projectome evaluation, which uniquely accounts for global prediction error and volume conservation. Leveraging the BlueGene/L supercomputing architecture, BlueMatter explores a massive database of 180 billion candidate fascicles. The candidates are derived from several sources, including atlases and mutliple tractography algorithms. Using BlueMatter we created the highest resolution, volume-conserved projectome of the human brain.
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