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
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