# Registration Ct and MRI with multiresolution

**URL:** <https://discourse.itk.org/t/registration-ct-and-mri-with-multiresolution/2585>\
**Category:** Algorithms\
**Tags:** simpleitk\
**Created:** [January 6, 2020, 3:16pm UTC](https://discourse.itk.org/t/registration-ct-and-mri-with-multiresolution/2585 "2020-01-06T15:16:26Z")\
**Posts on this page:** 1\
**Showing post:** 8

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**Author:** ![lassoan](https://discourse.itk.org/user_avatar/discourse.itk.org/lassoan/32/27_2.png) [@lassoan](https://discourse.itk.org/u/lassoan)\
**Post date:** [January 14, 2020, 10:38pm UTC](https://discourse.itk.org/t/registration-ct-and-mri-with-multiresolution/2585/8 "2020-01-14T22:38:10Z")

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> [@zivy](#):
>
> If you try it out and find that cropping both volumes gives you more robust and accurate results you have empirical results from the world. All that remains is to identify the theoretical reason underlying your observation, what makes this specific data behave in such a way that generic data isn’t expected to do.

I have experienced many times that registration fails (converges to incorrect position) if one region covers a different region compared to the other but succeeds if the images are cropped to the common region (even if their initial alignment was good, regardless of which one was the fixed image). I suspect that it might be do to insufficient sampling but I did not investigate it much.

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