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NF - Particle Filtering Tractography #1340
This PR add a new tissue classifier (CMC) and a new tractography algorithm (PFT). Both methods are described in [Girard et al., NImg, 2014].
@nilgoyette found potential optimisation for the cumsum(...) function, called several times in
@MrBago I would be very glad to get your feedback on the way I actually structured the implementation of PFT in dipy. PFT is the first tracking algorithm in dipy that is not a different
@Garyfallidis, @arokem, @skoudoro, @jchoude, I would be very much interested to hear your thoughts on this implementation. On the CC exemple, PFT is ~4.5x slower than ProbabilisticDirectionGetter LocalTracking, but outputs 3x more streamlines connecting the GM (i.e. not stopping in the WM or CSF). PFT has a seed to streamlines ratio of ~93% while the same algorithm With localTracking has a ratio of ~29% (for CC seeding of the example).
Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. Towards quantitative connectivity analysis: reducing tractography biases. NeuroImage, 98, 266-278, 2014.