| Deletions are marked like this. | Additions are marked like this. |
| Line 23: | Line 23: |
|
= Citation = Please cite this: '''''Highly Accurate Inverse Consistent Registration: A Robust Approach,'''''<<BR>> M. Reuter, H.D. Rosas, B. Fischl.<<BR>> !NeuroImage 53(4), pp. 1181-1196, 2010. http://dx.doi.org/10.1016/j.neuroimage.2010.07.020 <<BR>> http://reuter.mit.edu/papers/reuter-robreg10.pdf |
|
| Line 26: | Line 36: |
| This program symmetrically aligns two volumes. It uses a method based on robust statistics to detect outliers and removes them from the registration. This leads to highly accurate registrations even with local changes in the image (e.g. jaw movement, tumor growth, atrophy). The main purpose is to find the rigid registration (translation, rotation) of longitudinal data, but the method can be used to rigidly align different images. An additional optional intensity scale parameter can be used to adjust for global intensity differences. The extension to affine registration is being tested. | This program symmetrically aligns two volumes. It uses a method based on robust statistics to detect outliers and removes them from the registration. This leads to highly accurate registrations even with local changes in the image (e.g. jaw movement, tumor growth, atrophy). The main purpose is to find the rigid registration (translation, rotation) of longitudinal data, but the method can be used to rigidly (6DOF) and affinely (12DOF) align different images. An additional optional intensity scale parameter can be used to adjust for global intensity differences. It can also work on 2D images. |
| Line 29: | Line 39: |
|
The registration can fail because of several reasons, most likely due to large intensity differences or non-linear differences in the image. You can try: * Switch on ''intensity scaling'' ( - -iscale). * When specifying a manual saturation (- -sat) too many voxels might be considered outlier early in the process. |
The registration can fail because of several reasons, most likely due to large intensity differences (different modalities) or non-linear differences in the image. You can try: * Switch on ''intensity scaling'' ( --iscale). * When specifying a manual saturation (--sat) too many voxels might be considered outlier early in the process. |
| Line 34: | Line 44: |
| tkmedit -f dst.mgz -aux mov.mgz -overlay ow.mgz | freeview dst.mgz mov_to_dst.mgz -overlay ow.mgz:colormap=heat |
| Line 38: | Line 48: |
| * When using automatic saturation estimation ( - -satit) you can try specifying the sensitivity manually or twiddle around with - -wlimit (which is around 0.16 by default). A lower wlimit should reduce the number of outlier voxels. | * When using automatic saturation estimation ( --satit) you can try specifying the sensitivity manually or twiddle around with --wlimit (expert option, which is around 0.16 by default). A lower wlimit should reduce the number of outlier voxels. |
| Line 50: | Line 60: |
| One of the following is required for sensitivity: | One of the following is '''required''' for robust registration: |
| Line 56: | Line 66: |
| || - - mapmov <aligned.mgz> || output image: movable mapped and resampled at destination || |
|| - - mapmov <aligned.mgz> || output image: movable mapped and resampled at destination (cubic-bspline-interpolation)|| || - - mapmovhdr <aligned.mgz> || output image: movable aligned to destination (no resampling, only adjusting header vox2ras) || |
| Line 87: | Line 98: |
|
Register two images: {{{ mri_robust_register --mov vol1.mgz --dst vol2.mgz --lta v1to2.lta --mapmov v1to2.mgz --weights v1to2-weights.mgz --iscale --satit }}} Computes the rigid registration (6 degrees of freedom) of vol1.mgz to vol2.mgz using robust statistics and with an additional 7th global intensity scaling parameter (recommended e.g. for orig.mgz). The output is the transform (v1to2.lta) and v1to2.mgz (the aligned vol1.mgz to the target image). Additionally the weights of the robust registation (outlier detection) are saved. Everything can be viewed in tkmedit with: {{{ tkmedit -f vol2.mgz -aux v1to2.mgz -overlay v1to2-weights.mgz }}} |
Simple Full Head Registration (same modality): {{{mri_robust_register --mov vol1.mgz --dst vol2.mgz --lta v1to2.lta --mapmov v1to2.mgz --weights v1to2-weights.mgz --iscale --satit}}} Computes the symmetric rigid registration (translation and rotation) of vol1.mgz to vol2.mgz using robust statistics and with an additional global intensity scaling parameter. The output is the transform (v1to2.lta) and image v1to2.mgz (the vol1.mgz resampled to the target image). Additionally the weights of the robust registation (outlier detection) are saved. Everything can be viewed with: {{{freeview vol2.mgz v1to2.mgz v1to2-weights.mgz:colormap=heat}}} |
| Line 100: | Line 107: |
|
Output final results in half-way space: {{{ mri_robust_register --mov vol1.mgz --dst vol2.mgz --lta v1to2.lta --halfmov h1.mgz --halfdst h2.mgz --halfmovlta h1.lta --halfdstlta h2.lta --iscale --satit }}} |
Half Way Space Output (same modality): {{{mri_robust_register --mov vol1.nii --dst vol2.nii --lta v1to2.lta --halfmov h1.nii --halfdst h2.nii --halfmovlta h1.lta --halfdstlta h2.lta --iscale --satit}}} Computes the rigid robust registration with intensity scaling of Nifti vol1 to vol2 (the registration will be saved in v1to2.lta). Additionally outputs the half-way volumes h1.nii and h2.nii (with corresponding transforms h1.lta and h2.lta). As both volumes are mapped to the half-way space, they will both be resampled. This can be used to construct an unbiased mean volume (e.g. with mri_average) or to compute change maps. The output can be viewed with: |
| Line 105: | Line 113: |
|
Computes the rigid robust registration with intensity scaling of vol1 to vol2 (the registration will be in v1to2.lta). Additionally outputs the half-way volumes h1 and h2 (with corresponding transforms h1.lta and h2.lta). As both volumes are mapped to the half-way space, they will both be resampled. This can be used to construct an unbiased mean volume (e.g. with [[mri_average]]) or to compute change maps. The output can be viewed with: {{{ tkmedit -f h1.mgz -aux h2.mgz }}} |
{{{freeview -v h1.nii h2.nii}}} |
| Line 111: | Line 116: |
|
Register large image to small block (allready approximately aligned): {{{ mri_robust_register --mov large.mgz --dst small.mgz --lta l2s.lta --mapmov l2s.mgz --sat 6 --nosym --iscale --noinit --highit 0 }}} Image small will be used when computing the cost function (note the --nosym forcing move to be resampled to dst internally). The saturation might need to be adjusted. Since the image center of gravity is likely not the same, it should not be used for the initialization, therefore --noinit is passed. If the images are high res (even the small), --highit switches off the highest resolution. --subsamp <int> can also be used to force subsampling on lower resolutions (so only a subset of voxels is selected). |
Part to Full Registration (same modality): {{{mri_robust_register --mov fullhemi.mgz --dst part.mgz --noinit --nosym --sat 8 --maxsize 380 --mapmovhdr hemi2part.mgz --lta hemi2part.lta}}} Registers a full hemisphere with a high-resolutional part (e.g. hippocampal slices). It is recommended to specify the part as the target (the full hemi image will then be cropped internally). For partial registration to work we need to skip the center of mass initialization (--noinit) and switch off the half way space (--nosym). Also the inputs need to be in an approximate alignment, alternatively you can pass --ixform with a transform that approximately aligns the images. The satuarion needs to be specified manually with --sat. You can output the weights with --weights to see if too many voxels are removed and increase the value (to reduce outlier sensitivity). For high-res inputs we limit the resolution to 380 to reduce run time and mem usage. The output will be the transform (--lta) and the mov mapped to dst w/o resampling (--mapmovhdr), only adjusting the header information. Look at results with: {{{freeview -v part.mgz part2hemi.mgz}}} You can also invert transforms and apply them : {{{mri_concatenate_lta -invert1 hemi2part.lta identity.nofile part2hemi.lta}}} {{{mri_convert -at inv1.lta part.mgz part2hemi.mgz }}} |
| Line 130: | Line 142: |
| Please cite this: | |
| Line 136: | Line 149: |
|
Extension to multi registration (template estimation, [[mri_robust_template]] ): |
|
| Line 145: | Line 160: |
|
!NeuroImage, in press, 2012. http://dx.doi.org/10.1016/j.neuroimage.2012.02.084 |
!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 |
Index
Contents
Name
mri_robust_register - computes symmetric robust registration of two volumes (within modality)
Synopsis
mri_robust_register --mov <mov.mgz> --dst <dst.mgz> --lta <m2d.lta> [options]
Citation
Please cite this:
Highly Accurate Inverse Consistent Registration: A Robust Approach,
M. Reuter, H.D. Rosas, B. Fischl.
NeuroImage 53(4), pp. 1181-1196, 2010.
Description
This program symmetrically aligns two volumes. It uses a method based on robust statistics to detect outliers and removes them from the registration. This leads to highly accurate registrations even with local changes in the image (e.g. jaw movement, tumor growth, atrophy). The main purpose is to find the rigid registration (translation, rotation) of longitudinal data, but the method can be used to rigidly (6DOF) and affinely (12DOF) align different images. An additional optional intensity scale parameter can be used to adjust for global intensity differences. It can also work on 2D images.
If the registration fails: The registration can fail because of several reasons, most likely due to large intensity differences (different modalities) or non-linear differences in the image. You can try:
Switch on intensity scaling ( --iscale).
- When specifying a manual saturation (--sat) too many voxels might be considered outlier early in the process. You can check this by outputing the weights (--weights ow.mgz) and by looking at them in:
freeview dst.mgz mov_to_dst.mgz -overlay ow.mgz:colormap=heat
If most of the brain is labeled outlier, try to set the saturation to a higher value (eg. --sat 12) or use --satit to automatically determine a good sat value. - When using automatic saturation estimation ( --satit) you can try specifying the sensitivity manually or twiddle around with --wlimit (expert option, which is around 0.16 by default). A lower wlimit should reduce the number of outlier voxels.
Arguments
Positional Arguments
No positional arguments
Required Flagged Arguments
- - mov <mov.mgz> |
input movable volume to be aligned to target |
- - dst <dst.mgz> |
input target volume |
- - lta <reg.lta> |
output registration (transform from mov to dst) |
One of the following is required for robust registration:
- - 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
- - mapmov <aligned.mgz> |
output image: movable mapped and resampled at destination (cubic-bspline-interpolation) |
- - mapmovhdr <aligned.mgz> |
output image: movable aligned to destination (no resampling, only adjusting header vox2ras) |
- - weights <weights.mgz> |
output weights (outliers) in destination space |
- - iscale |
estimate intensity scale factor (default no). Highly recommended for unnormalized images! |
- - iscaleout <fname.txt> |
output txt file for iscale value (will activate --iscale). Default: no iscale output |
- - iscalein <fname.txt> |
initial input txt file for iscale value (probably you want to also activate --iscale to estimate final value?) |
- - transonly |
find 3 parameter translation only |
- - ixform lta |
use initial transform lta on source ('id'=identity), default is align center (using moments) |
- - initorient |
use moments for orientation init (default false). Recommended for stripped brains, but not with full head images with different cropping |
- - noinit |
skip transform init, default: translation of centers |
- - vox2vox |
output VOX2VOX lta file (default is RAS2RAS) |
- - maxit <#> |
iterate max # times on each resolution (default 5) |
- - highit <#> |
iterate max # times on highest resolution (default 5) |
- - epsit <real> |
stop iterations when all tp transform updates fall below <real> (default 0.01) |
- - nomulti |
work on highest resolution only (no multiscale) |
- - wlimit <real> |
sets maximal outlier limit for --satit (default 0.16), reduce to decrease outlier sensitivity |
- - subsample <real> |
subsample if dim > # on all axes (default no subs.) |
- - floattype |
convert images to float internally (default: keep input type) |
- - doubleprec |
double precision (instead of float) internally (large memory usage!!!) |
- - maskmov <mask.mgz> |
mask mov/src with mask.mgz |
- - maskdst <mask.mgz> |
mask dst/target with mask.mgz |
- - halfmov <hm.mgz> |
outputs half-way mov (mapped to halfway space) |
- - halfdst <hd.mgz> |
outputs half-way dst (mapped to halfway space) |
- - halfweights hw.mgz |
outputs half-way weights (mapped to halfway space) |
- - halfmovlta hm.lta |
outputs transform from mov to half-way space |
- - halfdstlta hd.lta |
outputs transform from dst to half-way space |
- - debug |
show debug output (default no debug output) |
- - verbose |
0 quiet, 1 normal (default), 2 detail |
Examples
Example 1
Simple Full Head Registration (same modality):
mri_robust_register --mov vol1.mgz --dst vol2.mgz --lta v1to2.lta --mapmov v1to2.mgz --weights v1to2-weights.mgz --iscale --satit
Computes the symmetric rigid registration (translation and rotation) of vol1.mgz to vol2.mgz using robust statistics and with an additional global intensity scaling parameter. The output is the transform (v1to2.lta) and image v1to2.mgz (the vol1.mgz resampled to the target image). Additionally the weights of the robust registation (outlier detection) are saved. Everything can be viewed with:
freeview vol2.mgz v1to2.mgz v1to2-weights.mgz:colormap=heat
Example 2
Half Way Space Output (same modality):
mri_robust_register --mov vol1.nii --dst vol2.nii --lta v1to2.lta --halfmov h1.nii --halfdst h2.nii --halfmovlta h1.lta --halfdstlta h2.lta --iscale --satit
Computes the rigid robust registration with intensity scaling of Nifti vol1 to vol2 (the registration will be saved in v1to2.lta). Additionally outputs the half-way volumes h1.nii and h2.nii (with corresponding transforms h1.lta and h2.lta). As both volumes are mapped to the half-way space, they will both be resampled. This can be used to construct an unbiased mean volume (e.g. with mri_average) or to compute change maps. The output can be viewed with:
freeview -v h1.nii h2.nii
Example 3
Part to Full Registration (same modality):
mri_robust_register --mov fullhemi.mgz --dst part.mgz --noinit --nosym --sat 8 --maxsize 380 --mapmovhdr hemi2part.mgz --lta hemi2part.lta
Registers a full hemisphere with a high-resolutional part (e.g. hippocampal slices). It is recommended to specify the part as the target (the full hemi image will then be cropped internally). For partial registration to work we need to skip the center of mass initialization (--noinit) and switch off the half way space (--nosym). Also the inputs need to be in an approximate alignment, alternatively you can pass --ixform with a transform that approximately aligns the images. The satuarion needs to be specified manually with --sat. You can output the weights with --weights to see if too many voxels are removed and increase the value (to reduce outlier sensitivity). For high-res inputs we limit the resolution to 380 to reduce run time and mem usage. The output will be the transform (--lta) and the mov mapped to dst w/o resampling (--mapmovhdr), only adjusting the header information. Look at results with:
freeview -v part.mgz part2hemi.mgz
You can also invert transforms and apply them :
mri_concatenate_lta -invert1 hemi2part.lta identity.nofile part2hemi.lta
mri_convert -at inv1.lta part.mgz part2hemi.mgz
Bugs
None (of course)
See Also
Links
References
Please cite this:
Highly Accurate Inverse Consistent Registration: A Robust Approach,
M. Reuter, H.D. Rosas, B. Fischl.
NeuroImage 53(4), pp. 1181-1196, 2010.
Extension to multi registration (template estimation, mri_robust_template ):
Avoiding Asymmetry-Induced Bias in Longitudinal Image Processing,
M. Reuter, B. Fischl.
NeuroImage 57(1), pp. 19-21, 2011.
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.
Also see FreeSurferMethodsCitation.
Reporting Bugs
Report bugs to <freesurfer@nmr.mgh.harvard.edu>