[ITK-users] [ITK] Super-resolution resampling

[Gavin Baker]

Thanks, Samuel,

Great point - using the N/2 sample makes a lot of sense.  I'll start
with that example and see how I go. Any thoughts on my followup question
about the super-resolution resampling the N images together would be
most appreciated.
Regards -

  :: Gavin

On Tue, 6 Jun 2017, at 11:33 PM, Samuel Gerber wrote:
> Hi Gavin,
> 
> One small addition, I would probably take the N/2 image to register
> everybody else to, in order to minimize the maximal transformation
> (might not matter in your case since it is only small transformations
> but it could minimize errors due to resampling).> 
> This example has all the required classes you should need:
> https://itk.org/Wiki/ITK/Examples/Registration/ImageRegistrationMethod> 
> You will most likely want to use a different optimizer and you can see
> in the code how to set the size etc of the output image in the
> resampler.> 
> 
> On Tue, Jun 6, 2017 at 9:16 AM, Dženan Zukić
> <dzenanz at gmail.com> wrote:>> 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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>> 
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> 
> 
> 
> -- 
> Samuel Gerber
> R&D Engineer
> Kitware, Inc.

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