MR:Processing:Preprocessing - Revision history

Ccn admin: 9 revisions

2014-01-16T03:43:16Z

9 revisions

New page

Once files have been transferred from the scanner to Hoffman and converted to .nii, they are run through standard preprocessing steps. All resultant files are stored in:<br>
/u/home/FMRI/sbook/data/<project_id>/subjects/<subject_id>/<scan_date>/analysis/preprocessing

==All files==
* converted to Radiological orientation if necessary (fslorient -swaporient). Note that this changes the orientation of the original file, as all files should be stored in Radiological orientation before subsequent analysis.
* reoriented to RL PA IS orientation (fslreorient2std)
output: <filename>_rpi.nii.gz

==Initial structurals (eg axial coplanar, matched bandwidth)==
* skull stripped with f=0.3 (bet)
output: <init_struct>_brain.nii.gz

==Main structurals (eg MP-RAGE)==
* skull stripped with f=0.3 and bias field & neck cleanup (bet)
output: <main_struct>_brain.nii.gz
* Note: Sometimes, BET will skull strip incorrectly. Modifying the -f and -g may help (see: [http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET/FAQ '''BET FAQ'''])
**When BET fails to skull strip correctly, manual skull stripping may be necessary (see: [[MR:Processing:Preprocessing:ManualSkullStripping]])

==Functionals==
* example functional obtained
output: <functional>_example.nii.gz
* motion corrected (mcflirt)
outputs: <functional>_mcf.nii.gz, <functional>_mcf_abs.rms, <functional>_mcf_rel.rms, <functional>_mcf.par
* mean 3d image calculated
output: <functional>_mean.nii.gz
* skull stripped with f=0.3 (bet2)
output: <functional>_mask.nii.gz
* mask motion corrected data with brain mask
output: <functional>_mcf_brain.nii.gz

==DTI==
* file converted from DICOM to .nii with dcm2nii
outputs: <dti>_raw.nii.gz, bvecs, bvals

==Manual preprocessing==
The program that performs preprocessing on Hoffman is hoffman_preproc.py. It should be in your path. Type
'hoffman_preproc.py --h' to see the usage.
Usage: hoffman_preproc.py -p <project_name> -s <subject_id> -d <date> <options>
Run basic preprocessing on raw .nii files
Example: hoffman_preproc.py -p AD -s AD_10374 -d 20100909 -m mp_rage_3 -f axial_bold_ur1_5
Assumed directory structure: /u/home/FMRI/sbook/data/<project_name>/subjects/<subject_id>/<date>
Options:
-h, --help show this help message and exit
-p PROJ, --project=PROJ
project name
-s SUBJID, --subjid=SUBJID
directory name for the subject
-d DATE, --date=DATE date directory name for the subject
-m MAINSTRUCT, --mainstruct=MAINSTRUCT
main structural image file prefix, eg mp_rage_3
-i INITSTRUCT, --initstruct=INITSTRUCT
initial structural image file prefix, eg axial_t2_4
-f FUNC, --func=FUNC functional image file prefix, eg axial_bold_ur1_5
-t DTI, --dti=DTI diffusion image file prefix, eg dti_30_dir_9
-q use qsub

Elau: moved Data-MR:Processing:Preprocessing to MR:Processing:Preprocessing

2012-04-25T20:35:17Z

moved Data-MR:Processing:Preprocessing to MR:Processing:Preprocessing

← Older revision	Revision as of 20:35, 25 April 2012
(No difference)

Elau: moved Hoffman2:Data Preprocessing to Data-MR:Processing:Preprocessing

2012-04-25T20:30:59Z

moved Hoffman2:Data Preprocessing to Data-MR:Processing:Preprocessing

New page

Once files have been transferred from the scanner to Hoffman and converted to .nii, they are run through standard preprocessing steps. All resultant files are stored in:<br>
/u/home/FMRI/sbook/data/<project_id>/subjects/<subject_id>/<scan_date>/analysis/preprocessing

==All files==
* converted to Radiological orientation if necessary (fslorient -swaporient). Note that this changes the orientation of the original file, as all files should be stored in Radiological orientation before subsequent analysis.
* reoriented to RL PA IS orientation (fslreorient2std)
output: <filename>_rpi.nii.gz

==Initial structurals (eg axial coplanar, matched bandwidth)==
* skull stripped with f=0.3 (bet)
output: <init_struct>_brain.nii.gz

==Main structurals (eg MP-RAGE)==
* skull stripped with f=0.3 and bias field & neck cleanup (bet)
output: <main_struct>_brain.nii.gz

==Functionals==
* example functional obtained
output: <functional>_example.nii.gz
* motion corrected (mcflirt)
outputs: <functional>_mcf.nii.gz, <functional>_mcf_abs.rms, <functional>_mcf_rel.rms, <functional>_mcf.par
* mean 3d image calculated
output: <functional>_mean.nii.gz
* skull stripped with f=0.3 (bet2)
output: <functional>_mask.nii.gz
* mask motion corrected data with brain mask
output: <functional>_mcf_brain.nii.gz

==DTI==
* file converted from DICOM to .nii with dcm2nii
outputs: <dti>_raw.nii.gz, bvecs, bvals

==Manual preprocessing==
The program that performs preprocessing on Hoffman is hoffman_preproc.py. It should be in your path. Type
'hoffman_preproc.py --h' to see the usage.
Usage: hoffman_preproc.py -p <project_name> -s <subject_id> -d <date> <options>
Run basic preprocessing on raw .nii files
Example: hoffman_preproc.py -p AD -s AD_10374 -d 20100909 -m mp_rage_3 -f axial_bold_ur1_5
Assumed directory structure: /u/home/FMRI/sbook/data/<project_name>/subjects/<subject_id>/<date>
Options:
-h, --help show this help message and exit
-p PROJ, --project=PROJ
project name
-s SUBJID, --subjid=SUBJID
directory name for the subject
-d DATE, --date=DATE date directory name for the subject
-m MAINSTRUCT, --mainstruct=MAINSTRUCT
main structural image file prefix, eg mp_rage_3
-i INITSTRUCT, --initstruct=INITSTRUCT
initial structural image file prefix, eg axial_t2_4
-f FUNC, --func=FUNC functional image file prefix, eg axial_bold_ur1_5
-t DTI, --dti=DTI diffusion image file prefix, eg dti_30_dir_9
-q use qsub