Anthony J. Sherbondy1 , Robert F. Dougherty2 , Rajagopal Ananthanarayanan1, Dharmendra S. Modha1 , and Brian A. Wandell2
IBM Almaden Reserach Center, Almaden, USA firstname.lastname@example.org Psychology Department, Stanford University, USA
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 globalprojectome 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.
The white matter of the human brain comprises more than 150km of long-range myelinated connections . Understanding the architecture of these long-range projections (the projectome) is important for understanding brain function . Diﬀusion weighted imaging ﬁber tractography (DWI-FT) is the only non-invasive method for studying the humanbrain projectome. While there has been great progress in developing ﬁber tracking techniques [3,4,5], there is wide agreement that current methods fail in many speciﬁc cases [6,7,4]. A limitation is that current algorithms ﬁnd pathways using greedy techniques; that is, the algorithms make decisions based on individual tracts without considering the entire projectome. Further, current algorithmsdo not optimize
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, eitherexpressly or implied, of the Defense Advanced Research Projects Agency or the U.S. Government. Also supported by NEI EY01500.
G.-Z. Yang et al. (Eds.): MICCAI 2009, Part I, LNCS 5761, pp. 861–868, 2009. c Springer-Verlag Berlin Heidelberg 2009
A.J. Sherbondy et al.
the projectome to ﬁt the original diﬀusion data. Finally, they ignore important physical constraints, such as the volumeoccupied by the estimated fascicles. We introduce the BlueMatter algorithm to address these limitations. BlueMatter takes as input fascicles derived from multiple tractography algorithms. It searches for an optimal combination of these fascicles (the projectome) subject to two error terms. The ﬁrst term compares the predicted and measured diﬀusion-weighted images. We refer to this constraint asdiﬀusion-ﬁtting (see also [8,9]). The second term accounts for an upper limit on the fascicle volume. We refer to this constraint as volume regularization; this term is an important physical constraint that helps resolve the ill-posed inverse problem of ﬁnding a projectome that minimizes diﬀusion-ﬁtting error. Accounting for diﬀusion-ﬁtting and ﬁber count was discussed by Zhang and Laidlaw ; however,their technique did not address fascicle volume estimation and was limited to fascicles derived from deterministic algorithms. BlueMatter is the ﬁrst algorithm to produce a human brain projectome that combines diﬀusion-ﬁtting and volume regularization. BlueMatter combines fascicle estimates from deterministic and stochastic DWI-FT algorithms; uniquely integrating algorithms with diﬀerent...