[ITK-users] Super-resolution resampling

[Dženan Zukić]

Hi Gavin,

your plan sounds good! There is no 1:N registration, so you should proceed
with N 1:1 registrations. Pick one as a reference (#0 is good), register
all the other time points to it. You can initialize the k+1-st iteration by
the resulting transform of k-th registration to speed things up.

And yes, you can do super-resolution by resampling all these images onto a
higher resolution grid, e.g. same origin and direction, 2x higher size and
2x smaller spacing.

ITK has all the required classes for this process. Will you let us know how
satisfactory the result was? Ideally with some images :)

Regards,
Dženan Zukić, PhD, Senior R&D Engineer, Kitware (Carrboro, N.C.)

On Tue, Jun 6, 2017 at 4:00 AM, Gavin Baker <gavinb+itk at antonym.org> wrote:

> Hello!
>
> I have a time series of 3D data (relatively low resolution), captured in
> sequence, with small positional changes (eg. translation). I would like
> to perform a super-resolution resampling by first co-registering each
> volumetric dataset (using rigid registration) in order to reduce noise
> and improve detail.
>
> Is there a registration process that is 1:N (fixed:moving)?
>
> Or is the recommended method to pick a fixed image (ie. #0) and register
> each 1..N individually to it?
>
> Given a set of transforms that map each of the 1..N moving images back
> to the fixed image for registration, is it possible to then resample the
> volume at a higher spatial resolution, combining all image data? IOW
> super-resolution resampling?
>
> I tried searching for the above and didn't have much luck finding
> relevant info.
>
> Thanks -
>
>   :: Gavin
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