[Neuroimaging] [dipy]Fitting diffusion models in the absence of S0 signal

[Ariel Rokem]

Hi everyone,

In Rafael's recent PR implementing free-water-eliminated DTI (
https://github.com/nipy/dipy/pull/835), we had a little bit of a discussion
about the use of the non-diffusion weighted signal (S0). As pointed out by
Rafael, in the absence of an S0 in the measured data, for some models, that
can be derived from the model fit (
https://github.com/nipy/dipy/pull/835#issuecomment-183060855).

I think that we would like to support using data both with and without S0.
On the other hand, I don't think that we should treat the derived S0 as a
model parameter, because in some cases, we want to provide S0 as an input
(for example, when predicting back the signal for another measurement, with
a different ). In addition, it would be hard to incorporate that into the
 model_params variable of the TensorFit object, while maintaining backwards
compatibility of the TensorModel/TensorFit and derived classes (e.g., DKI).

My proposal is to have an S0 property for ReconstFit objects. When this is
calculated from the model (e.g. in DTI), it gets set by the `fit` method of
the ReconstModel object. When it isn't, it can be set from the data. Either
way, it can be over-ridden by the user (e.g., for the purpose of predicting
on a new data-set). This might change the behavior of the prediction code
slightly, but maybe that is something we can live with?

Happy to hear what everyone thinks, before we move ahead with this.

Cheers,

Ariel
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/neuroimaging/attachments/20160229/1afc17e2/attachment.html>