mri_robust_template - construct an unbiased robust template for longitudinal volumes


mri_robust_template --mov <tp1.mgz> <tp2.mgz> ... --template <template.mgz> --satit [options]

Please Cite This

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.


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 Motion Correction 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.


Required Flagged Arguments

-- 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)

-- 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)

Optional Flagged Arguments

-- 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

-- 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!!!)

-- debug

show debug output (default no debug output)


Example 1

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

Example 2

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).


None (of course)

See Also



FreeSurfer, FsFast


Please cite this:

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.

The robust registration is based on this:

Highly Accurate Inverse Consistent Registration: A Robust Approach,
M. Reuter, H.D. Rosas, B. Fischl.
NeuroImage 53(4), pp. 1181-1196, 2010.

Related Publication:

Avoiding Asymmetry-Induced Bias in Longitudinal Image Processing,
M. Reuter, B. Fischl.
NeuroImage 57(1), pp. 19-21, 2011.

Also see FreeSurferMethodsCitation.

Reporting Bugs

Report bugs to <>



mri_robust_template (last edited 2013-11-21 17:08:43 by MartinReuter)