On 6/20/2022 3:03 AM, Wittayer, Matthias wrote:
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Hi, thanks for your answer.
Yes I did the longitudinal runs using the base as described in the
manual. So the pipeline is:
1.) Cross sectional Recon all
2.) recon-all -base Template.nii -tp timepoint1.nii -tp timepoint2.nii
-tp timepoint3.nii -all
3.) recon all -Long timepoint1.nii Template.nii -all
Then I read volumes of the segments from asegstats from the folders of
the longitudinal runs with a R script and subtract values of two
consecutive timepoints from one another. Miraculously some grey matter
areas seem to grow in MS patients (Not only a few voxels but up to
46%, upon visual inspection I cannot really tell the difference as
these still are just some voxels, but in the raw data areas are pretty
much the same size).
Either my boss is heading for a highly remunerated price here or I
just did something wrong with the analysis. He hopes for the former… I
am guessing the latter. The problem is not systematically distributed
as there is not for example one timepoint that is corrupted in several
areas and the others are fine and there is not one particular area
that has a problem. So garbage in garbage out is maybe not the main
problem.
The only things that came to my mind (and did not improve the problem)
were:
1.)Bias field correction (I used ANTS N4), though we only have 3T- data.
2.)Lesion filling with a binary lesion mask using FSL
Do I take the right tables (asegstats from the longitudinal runs
folders) to calculate my differences?
Yes, that is right
My understanding of the longitudinal pipeline was that all timeponits
available (and not only the timeponit I do the longitudinal run with
and the next one) are used to form a template that is warped to atlas
space to measure volumes by combinig transformation matrices of the
warping of the raw timepoint- image of the longitudinal run to the
common template and the transformation matrix of the warping of the
template to atlas space. Is that roughly the way it works?
I did not entirely follow all of that, but it sounds right. How do the
volumes look in the cross analysis? Do you see the same trend of
increase? When you look at the segmentations, are you saying that only a
few voxels change even though the volume is changing by 46%? One thing
you can try is samseg longitudinal analysis with lesion segmentation. It
was developed using MS lesions in mind, so it would be good to check on
a few cases at least. I'm cc'ing Stefano who developed the software.
Thanks a lot!
Matthias
------------------------------------------------------------------------
*Von:* freesurfer-boun...@nmr.mgh.harvard.edu
<freesurfer-boun...@nmr.mgh.harvard.edu> im Auftrag von Douglas N.
Greve <dgr...@mgh.harvard.edu>
*Gesendet:* Sonntag, 19. Juni 2022 16:58:18
*An:* freesurfer@nmr.mgh.harvard.edu
*Betreff:* Re: [Freesurfer] Problem with longitudinal processing
When you say they are growing rather than shrinking, do you mean in
the longitudnial recon-all run? The reason I ask is that you only
mention the base and cross. When you do the longitudinal analysis, you
need to do cross, then base, then long.
On 6/15/2022 11:43 AM, Wittayer, Matthias wrote:
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Dear community,
I tried to process MS- patient’s MRIs (mostly same scanner, same
settings) Longitudinally over a long period of time. I first
processed all timeponits crosssectionally and then initialised
the base image by recon- all – base TP1 TP2 TP3 etc. Now I am
trying to run label based morphometry and it seems some areas are
growing rather than shrinking. Which is highly unlikely. I tried
to exclude timepoint of a relapse to rule out perifocal edema
interfering with measures but the problem remains.
Did anybody have the same problem?
Is it a potential bug or just a garbage in garbage out problem
(though I don’t know what would be wrong with our scans)?
Does it make sense to make an intermediate template for two
timeponits only? I.e. recon- all –base TP1 TP2; recon –all –base
TP2 TP3 … and use them for longitudinal runs?
Thanks for your opinions. Best
Matthias
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