Index
Contents
Name
mri_robust_template - construct an unbiased robust template for longitudinal volumes
Synopsis
mri_robust_template --mov <tp1.mgz> <tp2.mgz> ... --template <template.mgz> [options]
Arguments
Positional Arguments
None
Required Flagged Arguments
- - mov <tp1.mgz> <tp2.mgz> ... |
input volumes |
movable volumes to be aligned to common mean/median template |
- - template <template.mgz> |
output template volume |
mean/median image |
Optional Flagged Arguments
- - lta <tp1.lta> <tp2.lta> ... |
|
output xforms to template (for each input) |
- - warp <warp1.mgz> ... |
|
map each input to template |
- - weights <weights1.mgz> ... |
|
output weights in target space |
- - average # |
|
construct template from: 0 Mean, 1 Median (default) |
- - iscale |
|
allow also intensity scaling on high-res. (default no) |
- - ixforms <t1.lta> <t2.lta> ... |
|
use initial transforms (lta) on source ('id'=identity) |
- - vox2vox |
|
output VOX2VOX lta file (default is RAS2RAS) |
- - leastsquares |
|
use least squares instead of robust M-estimator |
- - sat <float> |
|
set saturation for robust estimator (default 4.685) |
- - uchar |
|
set input type to UCHAR (with intensity scaling) |
- - conform |
|
conform volumes to 1mm vox (256^3) |
- - debug |
|
show debug output (default no debug output) |
Outputs
See above
Description
Uses an iterative method to construct a mean/median volume and the robust rigid registration of all input images to the current mean/median.
Important Note: The input images should all have the same intensity level! Good images are, for example, the T1.mgz and norm.mgz from the FreeSurfer stream.
Examples
Example 1
mri_robust_template --mov tp1.mgz tp2.mgz tp3.mgz --template mean.mgz --lta tp1.lta tp2.lta tp3.lta --warp tp1tomean.mgz tp2tomean.mgz tp3tomean.mgz --average 0 --iscale
Constructs a mean template from tp1,tp2 and tp3 and outputs the mean.mgz, the corresponding transforms (tp?.lta) and aligned images (tp?tomean.mgz). Intensity scaling is allowed (on highest resolution, once the images are well aligned).
View results:
tkmedit -f mean.mgz -aux tp1tomean.mgz
Bugs
None (of course)
See Also
Links
Methods Description
description description
References
Highly Accurate Inverse Consistent Registration: A Robust Approach, M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53 (4), pp. 1181-1196, 2010.
Reporting Bugs
Report bugs to <analysis-bugs@nmr.mgh.harvard.edu>