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

[Ariel Rokem]

Hi everyone,

Thought I would re-raise this. Anyone have any thoughts here? Would a PR
against the DTI and DKI modules be more helpful to clarify?

Cheers,

Ariel

On Sat, Mar 5, 2016 at 3:04 AM, Ariel Rokem <arokem at gmail.com> wrote:

>
> On Thu, Mar 3, 2016 at 7:28 AM, Eleftherios Garyfallidis <
> garyfallidis at gmail.com> wrote:
>
>> Sorry your suggestion is not exactly clear. Can you give show us how the
>> code will look with your proposal? Also, apart from DTI and DKI what other
>> models will be affected from this changes. Is this a change suggested only
>> for DTI and DKI or will affect all or most reconstruction models?
>>
>>
> First of all, to answer your last question: this will certainly affect DTI
> and DKI, and there will be other models to follow. For example the FWDTI
> that Rafael is currently proposing in that PR. The idea would be to also
> more tightly integrate these three models (and future extensions... !), so
> that we can remove some of the redundancies that currently exist. We could
> make this a part of the base.Reconst* methods - it might apply to other
> models as well (e.g. CSD, SFM, etc). But that's part of what I would like
> to discuss here.
>
> As for code, for now, here's a sketch of what this would look like for the
> tensor model:
>
> https://gist.github.com/arokem/508dc1b22bdbd0bdd748
>
> Note that though it changes the prediction API a bit, not much else would
> have to change. In particular, all the code that relies on there being 12
> model parameters will still be intact, because S0 doesn't go into the model
> parameters.
>
> What do you think? Am I missing something big here? Or should I go ahead
> and start working on a PR implementing this?
>
> Thanks!
>
> Ariel
>
>
>
>> On Mon, Feb 29, 2016 at 11:53 AM, Ariel Rokem <arokem at gmail.com> wrote:
>>
>>> 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
>>>
>>>
>>>
>>>
>>>
>>>
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>>>
>>
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