[Neuroimaging] Tractography normalization

Ariel Rokem arokem at gmail.com
Thu Aug 13 01:10:11 CEST 2015

Hi Eleftherios,

Just considered this one today:

On Tue, Aug 4, 2015 at 6:25 PM, Eleftherios Garyfallidis <
garyfallidis at gmail.com> wrote:

> Hello,
> On Tue, Aug 4, 2015 at 7:21 PM, Jorge Rudas <jrudascas at gmail.com> wrote:
>> Thanks for your answer Eleftherios
>> One questions more...
>> Be happy to ask as many questions as you need until everything is clear.
> I am sure you will need
> feedback from us to perform such an analysis. That is because although we
> are currently working on making
> easy workflows, right  now you will need write your own scripts combining
> different DIPY tutorials of the
> development version.
> Of course I am more than happy to help you with this.
>> When you say "then apply the deformation fields to the tractographies",
>> what exactly does this mean ?
>> You will generate streamlines and FA maps in the native space of every
> subject. Then you can for example register
> the FAs to an FA template. After you have performed this registrations you
> will also have saved the deformation fields
> which were applied to the FAs so that they can be registered to the FA
> template. Because the tractographies were
> in the same space (native) as the FAs the same deformation fields can be
> used to warp them to the FA template space
> and in that way your tractographies will also be normalized.

Do we have an implementation of functions that take a non-linear transform
and streamlines and warp the streamlines through this transform? Or should
we implement something like this? As it is now, the
DiffeomorphicMapping.transform method (and the functions that get called
under the hood there) take a 3D or 2D image as input, but it should be
fairly straightforward also enable these functions to take lists of
streamlines and operate on these instead. Is this something that already


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