Hello,

I have a question on whole-brain tractography. Given a "seed mask" (e.g. FA-based thresholded mask), I presume that the algorithm considers each voxel in the mask as a starting seed point and performs streamlining.

Now consider the following hypothetical scenario: Suppose we have 100 voxels (1x100 formation in 1D) with tensors/ODFs indicating a linear fiber from left to right. If the algorithm is repeated at every voxels, theoretically one should obtain 100 copies of the same fiber. In reality, we are expected to find 100 similar fibers (in terms of spatial positions, length, etc.). I think one should not say that the remaining 99 copies are spurious but I believe there is a need to resolve this "dense" representation of the same structure. I was wondering whether the Diffusion Toolkit contains a seed selection strategy to resolve this issue (and speed up the process) or a fiber similarity-driven filtering/pruning strategy after tractograpy. Does "track_merge" provide a solution by using the latter strategy?

Thanks,

Ertan