Dear experts,
given sub-optimal results of my surfaces I referred to in my previous post, I
have tested several different settings and input data preparation to obtain
optimal pial surfaces in my modified HCP pipeline. I tested all variants in 5
representative subjects.
My modified HCP pipeline uses FreeSurfer V6beta and full hires recon (-cm
option) with 0.7mm3 data (including FreeSurferHiResWhite.sh and
FreeSurferHiResPial.sh where effectively no up and downsampling is done since
all steps are done in original 0.7 mm3 resolution).
I have played with input data and parameters of 2nd pass mris_make_surfaces
-T2pial in FreeSurferHiResPial.sh
Since FreeSurferHiResPial.sh does not use aseg-informed and white
surface-informed normalization and brain masking of T2 (which is default in
recon-all), I also tested performance of this option (using commands borrowed
from recon-all -T2pial). Since -nsigma_below 3 (default in
FreeSurferHiResPial.sh) with combination of freeSurfer V6beta version of
mris_make_surfaces cuts out some portions of gray matter in my data (in
contrast to FreeSurfer 5.3 version where it seems that nsigma_below 3
suffices!), I increased -nsigma_below to 5 (which is default value used in
recon-all -T2pial).
My tested versions were following:
For modified full hires HCP pipeline with FreeSurfer V6beta:
1. -nsigma_below 3 (original FreeSurferHiResPial.sh)
2. -nsigma_below 5
3. -nsigma_below 5 with additional masking of T2 by brainmask.mgz
4. -nsigma_below 5 with aseg-informed normalization of T2 (using mri_normalize)
5. -nsigma_below 5 with aseg-informed normalization of T2 (using mri_normalize)
and masking of T2 by brainmask.mgz
And, as Matt suggested, I also tried:
6. standard FreeSurfer V6 beta recon-all -pial and -T2pial (apart from standard
recon-all I used as ?h.white input the output of FreeSurferHiResWhite.sh , but
I think it should not matter in this context).
My observations are following:
It is definitely needed to increase -nsigma_below from 3 due to significant cut
out of portions of gray matter in some regions, in some subjects these regions
are very large.
Increase nsigma_below to 5 prevents cutting out of gray matter, however it
leads to leak of surfaces to cerebellum (as I showed in my previous post) for
all my versions (to varying extent). I am not sure how much significant the
extend of error is, looking subjective it seems significant, but accounting the
fact that the data is of high resolution, the maximal error would be approx. 2
mm.
-nsigma_below 5 with aseg-informed normalization does not work well in some
areas and results in cutting out gray matter in some regions (but not to such
great extend as in nsigma_below 3)
FreeSurfer V6beta tends in some (not very large) areas to cut out gray matter.
On the other hand, in some areas and in some subjects it produces largest leak
of surfaces to dura and cerebellum from all my tested versions.
mri_normalize of T2 has in some regions sub-optimal performance on pial
surface estimation and actually performs worse than global (no aseg-informed
and no white surface-informed) normalization ( especially at temporal poles,
but also in some areas at convexity).
I have indecisive results with masking. In some subjects it had almost no
effect, in one subject it definitely improved the leak to dura, but in some
subjects the results were quite opposite. I am not sure why the masking should
matter since the mask is larger than the regions where the surfaces are
extending, but I am not very familiar with internals of mris_make_surfaces (the
masked T2 maybe after internal smoothing in mris_make_surfaces can propagate
the voxel values even to regions relevant for surface estimation). Therefore I
would tend to use the masking.
Overall best results (better than with V6beta recon-all) I obtained with
-nsigma_below 5 with global (no aseg-informed and white surface-informed)
normalization of T2 as it is done in FreeSurferHiResPial.sh.
My overall impresion: I was quite frustrated to observe, how much the results
are dependent on particular setting of the parameters and input data. But maybe
my expectations of accuracy of surfaces were unrealistically high....
Regards,
Antonin
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