Yeah. I agree - RESTORE seems to do pretty badly with this data, but I am
not sure why. I was considering simply using the simulation data that is
used to test RESTORE test_dti.test_restore, because it seems to work great
for the scenario described in that test.

What do you think?





On Fri, Nov 29, 2013 at 1:33 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.
>
> What do you mean by more closely resembles? Both WLS and RESTORE look like
> they are far from the original.
>
> —
> Reply to this email directly or view it on GitHub<https://github.com/nipy/dipy/pull/275/files#r8008350>
> .
>