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Dear all,
thanks for the advice to use Samseg. That improved things a lot. Is there a way to assess more parcels than there are included in Samseg by default (i.e. something like aseg/astats- parcels)? I want to apply multivariate lonitudinal atrophy models on the data and need to separate i.e. frontal from parietal cortex.
Best Regards Matthias
Von: freesurfer-bounces@nmr.mgh.harvard.edu freesurfer-bounces@nmr.mgh.harvard.edu Im Auftrag von Douglas N. Greve Gesendet: Freitag, 24. Juni 2022 16:22 An: freesurfer@nmr.mgh.harvard.edu; Cerri, Stefano SCERRI@MGH.HARVARD.EDU Betreff: Re: [Freesurfer] Problem with longitudinal processing (MS lesions/samseg)
On 6/20/2022 3:03 AM, Wittayer, Matthias wrote:
External Email - Use Caution 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-bounces@nmr.mgh.harvard.edumailto:freesurfer-bounces@nmr.mgh.harvard.edu freesurfer-bounces@nmr.mgh.harvard.edumailto:freesurfer-bounces@nmr.mgh.harvard.edu im Auftrag von Douglas N. Greve dgreve@MGH.HARVARD.EDUmailto:dgreve@MGH.HARVARD.EDU Gesendet: Sonntag, 19. Juni 2022 16:58:18 An: freesurfer@nmr.mgh.harvard.edumailto: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:
External Email - Use Caution 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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