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Nov 8, 2024
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ENH: Remove unused and recently unsupported antsRegistration flag #3695

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67 changes: 33 additions & 34 deletions nipype/interfaces/ants/registration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -710,9 +710,9 @@ class Registration(ANTSCommand):
--initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] \
--transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] \
--convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] --write-composite-transform 1'
>>> reg.run() # doctest: +SKIP

Expand All @@ -726,9 +726,9 @@ class Registration(ANTSCommand):
--initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] \
--transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] \
--convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.025, 1.0 ] --write-composite-transform 1'
>>> reg1.run() # doctest: +SKIP

Expand All @@ -742,9 +742,9 @@ class Registration(ANTSCommand):
--initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] \
--transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] \
--convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 0.975 ] --write-composite-transform 1'

Clip extremely low intensity data points using winsorize_lower_quantile. All data points
Expand All @@ -759,9 +759,9 @@ class Registration(ANTSCommand):
--initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] \
--transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] \
--convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.025, 0.975 ] --write-composite-transform 1'

Use float instead of double for computations (saves memory usage)
Expand All @@ -773,10 +773,10 @@ class Registration(ANTSCommand):
--initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear \
--output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] \
--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 \
--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-histogram-matching 1 \
--transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] \
--convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] \
--write-composite-transform 1'

Force to use double instead of float for computations (more precision and memory usage).
Expand All @@ -788,10 +788,10 @@ class Registration(ANTSCommand):
--initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear \
--output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] \
--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 \
--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-histogram-matching 1 \
--transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] \
--convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] \
--write-composite-transform 1'

'collapse_output_transforms' can be used to put all transformation in a single 'composite_transform'-
Expand Down Expand Up @@ -823,10 +823,10 @@ class Registration(ANTSCommand):
--initialize-transforms-per-stage 1 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] \
--restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] \
--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 \
--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-histogram-matching 1 \
--transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] \
--convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] \
--write-composite-transform 1'


Expand Down Expand Up @@ -857,10 +857,10 @@ class Registration(ANTSCommand):
--initialize-transforms-per-stage 1 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] \
--restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] \
--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 \
--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-histogram-matching 1 \
--transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] \
--convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] \
--write-composite-transform 0'

One can use multiple similarity metrics in a single registration stage.The Node below first
Expand All @@ -885,10 +885,10 @@ class Registration(ANTSCommand):
--initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] \
--transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] \
--convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] \
--metric CC[ fixed1.nii, moving1.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] --write-composite-transform 1'

ANTS Registration can also use multiple modalities to perform the registration. Here it is assumed
Expand All @@ -906,10 +906,10 @@ class Registration(ANTSCommand):
--initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] \
--transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] \
--convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] \
--metric CC[ fixed2.nii, moving2.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] --write-composite-transform 1'

Different methods can be used for the interpolation when applying transformations.
Expand All @@ -923,9 +923,9 @@ class Registration(ANTSCommand):
--initialize-transforms-per-stage 0 --interpolation BSpline[ 3 ] --output [ output_, output_warped_image.nii.gz ] \
--transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] \
--convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] --write-composite-transform 1'

>>> # Test Interpolation Parameters (MultiLabel/Gaussian)
Expand All @@ -937,10 +937,10 @@ class Registration(ANTSCommand):
--initialize-transforms-per-stage 0 --interpolation Gaussian[ 1.0, 1.0 ] \
--output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] \
--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 \
--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-histogram-matching 1 \
--transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] \
--convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] \
--write-composite-transform 1'

BSplineSyN non-linear registration with custom parameters.
Expand All @@ -954,9 +954,9 @@ class Registration(ANTSCommand):
--initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] \
--transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] \
--convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform BSplineSyN[ 0.25, 26, 0, 3 ] \
--use-histogram-matching 1 --transform BSplineSyN[ 0.25, 26, 0, 3 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 \
--smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] --write-composite-transform 1'

Mask the fixed image in the second stage of the registration (but not the first).
Expand All @@ -969,10 +969,10 @@ class Registration(ANTSCommand):
--initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] \
--transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] \
--convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --masks [ NULL, NULL ] \
--use-histogram-matching 1 --masks [ NULL, NULL ] \
--transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] \
--convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --masks [ fixed1.nii, NULL ] \
--use-histogram-matching 1 --masks [ fixed1.nii, NULL ] \
--winsorize-image-intensities [ 0.0, 1.0 ] --write-composite-transform 1'

Here we use both a warpfield and a linear transformation, before registration commences. Note that
Expand All @@ -988,10 +988,10 @@ class Registration(ANTSCommand):
[ func_to_struct.mat, 0 ] [ ants_Warp.nii.gz, 0 ] --initialize-transforms-per-stage 0 --interpolation Linear \
--output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] \
--metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] \
--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 \
--smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-histogram-matching 1 \
--transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] \
--convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 \
--use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] \
--use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ] \
--write-composite-transform 1'
"""

Expand Down Expand Up @@ -1155,10 +1155,9 @@ def _format_registration(self):
% self._format_xarray(self.inputs.shrink_factors[ii])
)
if isdefined(self.inputs.use_estimate_learning_rate_once):
retval.append(
"--use-estimate-learning-rate-once %d"
% self.inputs.use_estimate_learning_rate_once[ii]
)
# this flag was removed because it was never used in the ants codebase
# removed from Ants in commit e1e47994b on 2022-08-09
pass
if isdefined(self.inputs.use_histogram_matching):
# use_histogram_matching is either a common flag for all transforms
# or a list of transform-specific flags
Expand Down