NAME

mri_robust_template

SYNOPSIS

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

DESCRIPTION

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.

POSITIONAL ARGUMENTS

None.

REQUIRED-FLAGGED ARGUMENTS

ArgumentExplanation
--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)

One of the following is required for sensitivity:

ArgumentExplanation
--sat <real>set outlier sensitivity manually (e.g. '--sat 4.685' ). Higher values mean less sensitivity.
--satitauto-detect good sensitivity (recommended for head or full brain scans)

OPTIONAL-FLAGGED ARGUMENTS

ArgumentExplanation
--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
--oneminuswweights (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
--fixtpmap everthing to init TP# (init TP is not resampled)
--iscaleallow 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)
--vox2voxoutput VOX2VOX lta file (default is RAS2RAS)
--leastsquaresuse least squares instead of robust M-estimator (for testing only)
--noitdo 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.)
--floattypeconvert images to float internally (default: keep input type)
--finalnearestuse nearest neighbor in final interpolation when creating average. This is useful, e.g., when -noit and --ixforms are specified and brainmasks are mapped.
--doubleprecdouble precision (instead of float) internally (large memory usage!!!)
--crasCenter template at average CRAS, instead of average barycenter (default)
--debugshow 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).

REFERENCES


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

REPORTING

Report bugs to <freesurfer@nmr.mgh.harvard.edu>

SEE-ALSO

mri_robust_register