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Inspired from subject sub-032159 in site-mountsinai-S from PRIME-DE, which fail miserably since the first step of macapype.
Here is for instance the image after the first stage:
option 1
-- Prepare the data with FSL-FAST: fast -l 8 -B sub-032159_ses-001_run-1_T1w.nii.gz
-- Apply the reg_aladin command used in macapype to the bias corrected image:
With a little more exploration, using sites ucdavis and mount sinai-S, I had to choose between two sets of parameters for reg_aladin: (with ref inia19)
reg_aladin -ln 12 -lp 10 -nac -noSym -omp 1 -ref -rigOnly -smooR 1
=> works for all subjects of ucdavis and all but two subjects of mount sinai-S
reg_aladin -ln 5 -lp 10 -nac -noSym -omp 1 -ref -rigOnly -smooR 1
=> works for all subjects of ucdavis, mount sinai-S ( even for the two subjects of mount sinai-S that weren't working)
For INT macaques, option worked for all the one I tried, except Galileo, which needed to be cropped before and use of option 1.
Inspired from subject
sub-032159
insite-mountsinai-S
from PRIME-DE, which fail miserably since the first step of macapype.Here is for instance the image after the first stage:
![Capture d’écran 2022-09-22 à 18 03 45](https://user-images.githubusercontent.com/32452352/191804840-027c977e-695a-4304-89f1-50953257172c.png)
-- Prepare the data with FSL-FAST:
fast -l 8 -B sub-032159_ses-001_run-1_T1w.nii.gz
-- Apply the
reg_aladin
command used in macapype to the bias corrected image:-- Apply the calculated transform to the original image
This is the result:
![Capture d’écran 2022-09-22 à 18 09 02](https://user-images.githubusercontent.com/32452352/191804726-ca9d88e4-70cb-46f1-948c-1e0dfb537330.png)
-- change the parameters of
reg_aladin
on the original image:=> same result
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