Hi!
Thanks everyone for your responses. Brian, I would be very much interested
in finding out more details!
Thanks
John
On 6/2/09 1:32 PM, "Brian Patenaude" <[log in to unmask]> wrote:
> 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
>>
>>
>
>
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