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.
Sponsored by Defense Advanced Research Projects Agency, Defense Sciences Office (DSO), Program: Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE), Issued by DARPA/CMO under Contract No. HR0011-09-C-0002. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressly or implied, of the Defense Advanced Research Projects Agency or the U.S. Government. Also supported by NEI EY01500.
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Sherbondy, A.J., Dougherty, R.F., Ananthanarayanan, R., Modha, D.S., Wandell, B.A. (2009). Think Global, Act Local; Projectome Estimation with BlueMatter. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5761. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04268-3_106
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DOI: https://doi.org/10.1007/978-3-642-04268-3_106
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