Hi,
Provided that the diffusion data has been register to the T1, you could
potentially use the subcortical masks or surfaces from FIRST to register
specific subcortical areas. I could provide more details if you're
interested in pursuing this.
Cheers,
Brian
> Dear John,
>
>> We are looking at subcortical areas in a diffusion study.
>> Registration
>> has so far proved challenging and;
>>
>> 1- I was wondering if someone could explain how FNIRT could use
>> nearest
>> neighbor interpolation during the registration process. I noticed that
>> FLIRT can do this, but I don't quite understand how this could apply
>> to
>> FNIRT. Does nearest neighbor interpolation make sense with a non-
>> linear
>> registration method?
>
> There is no option to use nearest neighbor (nn) when estimating the
> warps (though you can use nn when resampling your data once the warps
> have been calculated).
>
> It would be very difficult to use nn in an estimation scheme since it
> would make the derivatives highly non-linear.
>
>> 2- Additionally, how else can I control registration so we are not
>> risking losing small structures that could be wiped out by the
>> smoothing
>> process? Can anyone give us advice on how to boost our sensitivity?
>
> I don't quite understand what you mean here. There shouldn't really by
> any smoothing going on here, except for a small amount of smoothing
> introduced by the interpolation. And I would be very surprised if that
> was enough to wipe out structures.
>
> Could it be that you have data with higher resolution than your "--ref
> space" (as defined by your template)? In that case you may end up not
> sampling all points in your original image when creating your
> resampled image. The solution to that is to use the --super switch
> with applywarp.
>
> Good Luck Jesper
>
>
|