A graph-based approach for automatic cardiac tractography

A new automatic algorithm for assessing fiber‐bundle organization in the human heart using diffusion‐tensor magnetic resonance imaging is presented. The proposed approach distinguishes from the locally “greedy” paradigm, which uses voxel‐wise seed initialization intrinsic to conventional tracking algorithms. It formulates the fiber tracking problem as the global problem of computing paths in a boolean‐weighted undirected graph, where each voxel is a vertex and each pair of neighboring voxels is connected with an edge. This leads to a global optimization task that can be solved by iterated conditional modes‐like algorithms or Metropolis‐type annealing. A new deterministic optimization strategy, namely iterated conditional modes with α‐relaxation using (t2)‐ and (t4)‐moves, is also proposed; it has similar performance to annealing but offers a substantial computational gain. This approach offers some important benefits. The global nature of our tractography method reduces sensitivity to noise and modeling errors. The discrete framework allows an optimal balance between the density of fiber bundles and the amount of available data. Besides, seed points are no longer needed; fibers are predicted in one shot for the whole diffusion‐tensor magnetic resonance imaging volume, in a completely automatic way. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.