Thanks Bruce and Martin.
Martin, I have a followup question:
Let's say I run mri_robust_register with --iscale flag and also use --halfmov and --halfdst
flags to create two outputs hm.mgz and hd.mgz
1) You mentioned that --iscale "adjusts the intensities of both input images to better match". Are the image intensities of the outputs hm.mgz and hd.mgz also adjusted to match each other ?
Also here is brief description (in case someone else is looking for something similar)
Basically, I have 2 scans of the same subject and I need three steps:
(1) skull-stripping, (2) intensity normalization between time-points and (3) registration of time-points;
(following your recommendation, I put skull removal before registration)
For the intensity normalization, ideally, a good option would be to do a "histogram matching" between the two time-points. However, I am guessing that the mri_normalize would be a good first approximation/substitute to the histogram matching step.
(I did see mri_histo_eq function but I am not sure if I should use that instead of mri_normalize.)
Alternatively, Bruce suggested I could use the FS longitudinal pipeline and compare the norm.mgz images for each time-point from the longitudinal data.
I like this approach better since the longitudinal stream maps all the timepoints to the template space and I can look for changes in the template space.
Thanks
Mehul
On Wed, Jan 25, 2012 at 1:45 PM, Martin Reuter
<mreuter@nmr.mgh.harvard.edu> wrote:
Hi Mehul,
- if lesions show large changes, normalization might be dangerous
- mri_robust_register has a flag --iscale for global intensity
adjustment (a global scaling parameter that adjusts the intensity images
of both inputs to better match)
- mri_normalize, normalized the white matter to be around 110 is that
what you want?
- usually registration will be more accurate if images are skull
stripped.
Best, Martin
On Tue, 2012-01-24 at 09:39 -0800, Mehul Sampat wrote:
> Hi Folks,
> We have subjects with high lesion load which changes significantly
> over time.
> I want to use FS functions to build a pipeline for comparing lesion
> changes in two time-points of the same-subject.
> I am thinking of using the following steps;
>
>
> 1) Use mri_normalize to normalize the two time-points.
> 2) Use mri_robust_register to register two time-points of the same
> subject to half-way space.
> 3) Use mri_skull_strip
> 4) Use subtraction imaging or some other techniques to look for lesion
> changes.
>
>
>
>
> My questions are:
> 1) I think I need mri_normalize since the output from
> mri_robust_register is not intensity normalized ?
> 2) Instead of the first three steps, I could also do the following:
> Run autorecon1 for both timepoints and then run mri_robust_register
> on the skull stripped images
> Does it matter if we run mri_robust_register before or after skull
> stripping ?
>
>
> Thanks
> Mehul
>
>
>
>
>
>
>
>
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