## Note: This page was created with the CommandTemplate ## ## If you're modifying this page please take a look at the ## latest version of CommandTemplate to ensure that you're ## using the latest version of the CommandTemplate ## ## See HelpOnCommandTemplate for description of formatting '''Index''' <> = Name = mri_robust_template - construct an unbiased robust template for longitudinal volumes = Synopsis = {{{ mri_robust_template --mov ... --template --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. http://dx.doi.org/10.1016/j.neuroimage.2012.02.084 http://reuter.mit.edu/papers/reuter-long12.pdf = 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 [[motioncor | Motion Correction]] step in recon-all and for creating a within-subject template in the [[LongitudinalProcessing | 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. = Arguments = == Required Flagged Arguments == || -- mov ... || input movable volumes to be aligned to common mean/median template || || -- template || output template volume (final mean/median image) || One of the following is required for sensitivity: || -- sat || 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 ... || output xforms to template (for each input) || || -- mapmov ... || output images: map and resample each input to template || || -- weights ... || 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 ... || use initial intensity scales || || -- iscaleout ... || output final intensity scales (will activate --iscale) || || -- ixforms ... || use initial transforms (lta) on source ('id'=identity) || || -- vox2vox || output VOX2VOX lta file (default is RAS2RAS) || || -- affine || compute 12 DOF registration (default is 6 DOF) || || -- 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 || stop iterations when all tp transform updates fall below (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) || = Examples = == 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). = Bugs = None (of course) = See Also = [[mri_robust_register]] = Links = FreeSurfer, FsFast = References = ## [[References/Lastname###]] 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. http://dx.doi.org/10.1016/j.neuroimage.2012.02.084 http://reuter.mit.edu/papers/reuter-long12.pdf 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. http://dx.doi.org/10.1016/j.neuroimage.2010.07.020 <
> http://reuter.mit.edu/papers/reuter-robreg10.pdf Related Publication: '''''Avoiding Asymmetry-Induced Bias in Longitudinal Image Processing,'''''<
> M. Reuter, B. Fischl.<
> !NeuroImage 57(1), pp. 19-21, 2011. http://dx.doi.org/10.1016/j.neuroimage.2011.02.076 <
> http://reuter.mit.edu/papers/reuter-bias11.pdf Also see [[FreeSurferMethodsCitation]]. = Reporting Bugs = Report bugs to = Author/s = MartinReuter