It is not immediately clear to me how to start from non-DWI inputs (if at all possible).
Suppose that I have a data-source completely unrelated to MRI. I can generate a “diffusion tensor” (something equivalent to that in any case) for the voxels in the data, and would like to do deterministic tractography.
As far as I can see, I would like to run tckgen() with the Tensor_Det algorithm, but skip the diffusion tensor fitting step. Can I do that?
Are there other issues or pitfalls that I fail to see, that will prevent me from using your impressive framework for visualization and analysis?
Thanks
\Martin
Yes, but you’ll probably be after the fact algorithm. See what form the input is expected to take for that algorithm in the tckgen command description.
Probably quite a few… It’ll depend on exactly what you want to do, best to raise issues as and when you encounter them. 
Ahh, okay, thanks. I think I just had a hard time understanding the compact description of the fact algorithm in the documentation. I’ll see how it goes 
And just to be clear. It is not immediately possible to use an input representation similar to the “simple” output representation of dwi2tensor?
No, I’m afraid not. We have discussed this in the past – there’s an open issue for it. But no action as of yet… If anyone is interested in coding this up, we’d be happy to assist! 
Just a quick update: So far I have tested the ‘basic’ functionality - generating and visualizing tracts - and everything is running beautifully
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