mri_ca_normalize
mri_ca_normalize [<options>] <inbrain1> <inbrain2> ... <atlas> <xform> <output1> <output2> ...
This program creates a normalized volume using the brain volume and an input gca file.
Argument | Explanation |
---|---|
inbrain1 | input volume |
inbrain2 | input volume |
atlas | atlas file in gca format |
xform | tranform file in lta format |
output1 | output volume |
output2 | output volume |
Argument | Explanation |
---|---|
-seg <filename> | aseg file, to help normalization |
-sigma <bias sigma> | smoothing sigma for bias field if control points specified (def=4) |
-fsamples <filename> | write control points to filename |
-nsamples <filename> | write transformed normalization control points to filename |
-mask <mri_vol> | use mri_vol to mask input |
-f <filename> | define control points from filename |
-fonly <filename> | only use control points from filename |
-diag <filename> | write to log file |
-debug_voxel <x> <y> <z> | debug voxel |
-debug_node <x> <y> <z> | debug node |
-tr <float n> | set TR in msec |
-te <float n> | set TE in msec |
-alpha <float n> | set alpha in radians |
-example <mri_vol> <segmentation> | use T1 (mri_vol) and segmentation as example |
-novar | do not use variance estimates |
-renorm <mri_vol> | renormalize using predicted intensity values in mri_vol |
-flash | use FLASH forward model to predict intensity values |
-prior <float t> | use prior threshold t (default=.6) |
-w | write normalized volume each nregion iteration to norm(n).mgh(see -n) |
-n <int n> | use n regions/struct for normalization |
-v <int n> | used for debugging and diagnostics |
-p <float p> | use top p percent(default=.25) white matter points as control points |
Output | Explanation |
---|---|
outvol | output volume in either mgh or mgz format |
mri_ca_normalize -mask -p 0.25 subject1/mri/brain subject1/mri/nu single_one.gca subject1/mri/transforms/talairach_one.lta subject1/mri/norm_one.mgh
None
Report bugs to <freesurfer@nmr.mgh.harvard.edu>
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