Hey both,
This is a bit of a controversial topic. I’m just stating some conservative bits. My posts are otherwise moderated, so I am not speaking entirely freely by choice. But in any case, to do away with damaging for/against rhetoric, some general principles:
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dwi2response tournier is an older approach, which was at the time simply just designed for single-shell data, for the purpose of the original “single-tissue” CSD (i.e. called just “CSD”). This only uses the largest b-value, no others and no b=0 data. But note critically that this was simply only a single-shell technique, hence it’s simply not designed for multi-shell data. There’s no other reasons behind why it chooses thus naturally only a single shell (highest b-value then being the best, because superior angular contrast).
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dwi2response dhollander can work with any number of b-values. If you give it a multi-shell dataset, you’ll get 3 (WM-GM-CSF) multi-shell responses. If you give it a typical single-shell dataset (which typically also includes b=0 data), you get a “single-shell + b=0” set of 3 responses. Note that this is itself also different from `dwi2response tournier’, which doesn’t get the b=0 part, because the original CSD also doesn’t use that part by design.
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Thus, you can use dwi2response dhollander outputs for a range of stuff, going from MSMT-CSD on multi-shell data, to “2 tissue” CSD (WM-CSF) on single-shell +b=0 data using the MSMT-CSD algorithm, to (and pray this doesn’t get blocked) SS3T-CSD on single-shell +b=0 data. The latter SS3T-CSD uses an external fork of the software that is not supported by the MRtrix3 team, but that I put out there to make that method available to you.
So, that’s the possibilities. On kids all the way to adult and elderly human data, and a range of animal data, all the above is realistically possible, fits well, and leads to good results. Using different parts of data (i.e. particular shells, etc…) has different properties; there’s lots of discussion in the literature for consideration. I’m not going to comment on it here.
On fetal and neonatal data, we’re getting further into controversial areas due to our obvious involvements with such data and small or big projects, hence arguably conflicts of interest all over the place. I won’t comment on it, but very carefully I can at least suggest that different approaches are also possible, several (but not all) which have been referred to on this forum. When I say “possible”, I mean that these different approaches will all fit the full or consciously selected parts of the data quite well. So arguably, they’re all “valid signal representations”. However, when choosing response functions based on different types of definitions, this will obviously imply different interpretations of those signal representations in the end. Some might try to represent “WM-like, GM-like and CSF-like” tissue, others might try to represent “young / less developed WM-like”, “older / more developed WM-like” and “CSF-like” tissue, and yet others might represent something else entirely again. I would encourage people to play around with different options and not necessarily rely on the one or the other or yet other sets of rhetorics necessarily. We’re all very capable researchers (including you all).
This exercise doesn’t have to be daunting even, if you fall back on what all CSD approaches have in common: you model / fit your diffusion signal in function of your set of response functions. The resulting maps or FODs represent the amount of signal that respectively resembles each of the corresponding response functions. Hence why I like talking about concepts like “GM-like” tissue, etc… If the response function comes e.g. from actual GM regions, then the corresponding fitted map will tell you where in the entire brain similar diffusion signals appear, and in what amounts. So think in terms of those response functions that you happen to use in whichever approach takes your fancy. You can almost think of it as pattern matching, if that helps to make sense of it (don’t take that too literally though; just a vague intuition). That’s CSD approaches for you. You can also contrast that set of (CSD) approaches with microstructure types of modelling, like NODDI, where the assumptions of compartments are more a priori defined in the model, and might clash with the reality of the data in unusual scenarios. With CSD, you’ve got more powers and flexibility as to the definition of your model in your own hands, I would say; but you have to think wisely to make sure it makes sense in the end when you write discussion sections in papers.
Good luck & have fun.
Cheers,
Thijs (Wednesday, 27-Oct-21 08:42:45 UTC)
