As you just said in our hangout - there's a test in place that shows that
the algorithm does what it supposed to do:

https://github.com/nipy/dipy/blob/master/dipy/reconst/tests/test_dti.py#L469

I am thinking that the way to go here is to build the example to resemble
the test. It's actually quite similar to what the original paper had...

I will compare with Camino to be sure that we've got it right.



On Sun, Dec 1, 2013 at 6:32 PM, Eleftherios Garyfallidis <
notifications@github.com> wrote:

> In doc/examples/restore_dti.py:
>
> > +ax.hist(np.ravel(fa2), color='b', histtype='step', label='WLS')
> > +ax.hist(np.ravel(fa3), color='r', histtype='step', label='RESTORE')
> > +ax.hist(np.ravel(fa1), color='g', histtype='step', label='Original')
> > +ax.set_xlabel('Fractional Anisotropy')
> > +ax.set_ylabel('Count')
> > +plt.legend()
> > +fig_hist.savefig('dti_fa_distributions.png')
> > +
> > +"""
> > +
> > +.. figure:: dti_fa_distributions.png
> > +   :align: center
> > +
> > +
> > +This demonstrates that RESTORE can recover a distribution of FA that more
> > +closely resembles the baseline distribution of the noiseless signal.
>
> I think you should first convince yourself that RT does what it should be
> doing. We cannot suggest to people to use algorithms that we haven't first
> tested heavily and understand deeply. I am personally not familiar with RT
> so I cannot be much of a help here. Can you think of a plan on how to
> answer the question "Why RT does badly with these data"? Do you have anyone
> in mind who could help you with this? Have you tested against other
> software that implement the RT?
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/nipy/dipy/pull/275/files#r8016592>
> .
>