mri_robust_template
mri_robust_template --mov <tp1.mgz> <tp2.mgz> ... --template <template.mgz> [options]
This program constructs an unbiased robust template for longitudinal volumes (within modality, 6-7 DOF). It uses an iterative method to construct a mean/median volume and the robust rigid registration of all input images to the current mean/median.
It is used for the MotionCorrection step in recon-all and for creating a within-subject template in the longitudinal stream (-base) in FreeSurfer.
Important Note: For best performance 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.
| Argument | Explanation |
|---|---|
| --mov <tp1.mgz> <tp2.mgz>... | input movable volumes to be aligned to common mean/median template |
| --template <template.mgz> | output template volume (final mean/median image) |
| Argument | Explanation |
|---|---|
| --sat <real> | set outlier sensitivity manually (e.g. '--sat 4.685' ). Higher values mean less sensitivity. |
| --satit | auto-detect good sensitivity (recommended for head or full brain scans) |
| Argument | Explanation |
|---|---|
| --lta <tp1.lta> <tp2.lta> ... | output xforms to template (for each input) |
| --mapmov <aligned1.mgz> ... | output images: map and resample each input to template |
| --weights <weights1.mgz> ... | output weights (outliers) in target space |
| --oneminusw | weights (outlier) map will be inverted (0=outlier), as in earlier versions |
| --average <#> | construct template from: 0 Mean, 1 Median (default) |
| --inittp <#> | use TP# for spacial init (default random), 0: no init |
| --fixtp | map everthing to init TP# (init TP is not resampled) |
| --iscale | allow also intensity scaling (default off) |
| --iscalein <is1.txt> <is2.txt> ... | use initial intensity scales |
| --iscaleout <is1.txt> <is2.txt> ... | output final intensity scales (will activate --iscale) |
| --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 (for testing only) |
| --noit | do not iterate, just create first template |
| --maxit <#> | iterate max # times (if #tp>2 default 6, else 5 for 2tp reg.) |
| --highit <#> | iterate max # times on highest resolution (default 5) |
| --epsit <real> | stop iterations when all tp transform updates fall below <real> (if #tp>2 default 0.03, else 0.01 for 2tp reg.) |
| --subsample <#> | subsample if dim > # on all axes (default no subs.) |
| --floattype | convert images to float internally (default: keep input type) |
| --finalnearest | use nearest neighbor in final interpolation when creating average. This is useful, e.g., when -noit and --ixforms are specified and brainmasks are mapped. |
| --doubleprec | double precision (instead of float) internally (large memory usage!!!) |
| --cras | Center template at average CRAS, instead of average barycenter (default) |
| --debug | show debug output (default no debug output) |
mri_robust_template --mov tp1.mgz tp2.mgz tp3.mgz --template mean.mgz --lta tp1.lta tp2.lta tp3.lta --mapmov tp1tomean.mgz tp2tomean.mgz tp3tomean.mgz --average 0 --iscale --satit
Constructs a mean (--average 0) 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, the saturation/sensitivity for outliers is automatically computed (only possible for
full head or full brain images).
View results:
tkmedit -f mean.mgz -aux tp1tomean.mgz
mri_robust_template --mov 001.mgz 002.mgz --average 1 --template rawavg.mgz --satit --inittp 1 --fixtp --noit --iscale --subsample 200
Is used in the recon-all stream for motion correction of several (here two: 001.mgz and 002.mgz) inputs. In this case all follow-ups are registered to the first input (as specified with --inittp 1 --fixtp --noit) and the rawavg.mgz is output as the median image (--average 1).
Highly Accurate Inverse Consistent Registration: A Robust Approach, M. Reuter, H.D. Rosas, B. Fischl. NeuroImage 53(4):1181-1196, 2010.
http://dx.doi.org/10.1016/j.neuroimage.2010.07.020
http://reuter.mit.edu/papers/reuter-robreg10.pdf
Avoiding Asymmetry-Induced Bias in Longitudinal Image Processing, M. Reuter, B. Fischl. NeuroImage 57(1):19-21, 2011.
http://dx.doi.org/10.1016/j.neuroimage.2011.02.076
http://reuter.mit.edu/papers/reuter-bias11.pdf
Within-Subject Template Estimation for Unbiased Longitudinal Image Analysis. M. Reuter, N.J. Schmansky, H.D. Rosas, B. Fischl. NeuroImage 61(4):1402-1418, 2012.
http://dx.doi.org/10.1016/j.neuroimage.2012.02.084
http://reuter.mit.edu/papers/reuter-long12.pdf
Report bugs to <freesurfer@nmr.mgh.harvard.edu>
mri_robust_register