Just trying to prevent different concepts getting blurred:
mtnormalise applies to multi-tissue data, since it’s the presumption of a unity sum of tissue fractions on which it operates. So this would be the output of dwi2fod msmt_csd, regardless of what method was used to derive the response functions for such (though it would need to be a method that yields more than one response function).
With dwi2response tournier, the output is a response function to be used for deconvolution, and so isn’t something that either bias field correction or intensity normalisation is directly applicable to. Use of that response function estimation algorithm however infers that one subsequently performs single-tissue deconvolution, to which a multi-tissue-based intensity normalisation is logically not applicable.
Note that you can actually theoretically run the multi-tissue normalisation algorithm but only provide it with a single tissue ODF image. Question is whether that is detrimental vs. beneficial, and whether it is preferable to some alternative approach (e.g. dwibiascorrect & dwinormalise group). For a genuine single-tissue deconvolution, I don’t think I’d be applying mtnormalise personally.
It is true that, with single-shell DWI data, one can nevertheless perform a multi-tissue deconvolution by making use of the b=0 data and using WM and CSF response functions. However this would necessitate using something other than the dwi2response tournier algorithm.
I think we need a figure in the documentation that explains the difference between single- and multi-shell data, single-tissue vs. multi-tissue response function estimation, single-tissue vs. 2-tissue vs. 3-tissue deconvolution, different forms of intensity normalisation, and the commands that get one from point to the next…