Think Global, Act Local; Projectome Estimation with BlueMatter

Authors:
Anthony J. Sherbondy;Robert F. Dougherty;Rajagopal Ananthanarayanan;Dharmendra S. Modha;Brian A. Wandell
Affiliations:
IBM Almaden Reserach Center, Almaden, USA;Psychology Department, Stanford University, USA;IBM Almaden Reserach Center, Almaden, USA;IBM Almaden Reserach Center, Almaden, USA;Psychology Department, Stanford University, USA
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Year:
2009

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Abstract

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.