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.eduwrote:

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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