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if pad_template: stereo_padded_brain_mask and stereo_padded_segmented brain_mask
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| from nipype.interfaces.ants.base import (ANTSCommand, ANTSCommandInputSpec) | ||
| from nipype.interfaces.base import TraitedSpec, File, traits, isdefined | ||
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| class DenoiseImageInputSpec(ANTSCommandInputSpec): | ||
| dimension = traits.Enum( | ||
| 2, | ||
| 3, | ||
| 4, | ||
| argstr="-d %d", | ||
| desc="This option forces the image to be treated " | ||
| "as a specified-dimensional image. If not " | ||
| "specified, the program tries to infer the " | ||
| "dimensionality from the input image.", | ||
| ) | ||
| input_image = File( | ||
| exists=True, | ||
| argstr="-i %s", | ||
| mandatory=True, | ||
| desc="A scalar image is expected as input for noise correction.", | ||
| ) | ||
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| mask_image = File( | ||
| exists=True, | ||
| argstr="-x %s", | ||
| mandatory=False, | ||
| desc="Restriction of computation on the mask only", | ||
| ) | ||
| noise_model = traits.Enum( | ||
| "Gaussian", | ||
| "Rician", | ||
| argstr="-n %s", | ||
| usedefault=True, | ||
| desc=("Employ a Rician or Gaussian noise model."), | ||
| ) | ||
| shrink_factor = traits.Int( | ||
| default_value=1, | ||
| usedefault=True, | ||
| argstr="-s %s", | ||
| desc=( | ||
| "Running noise correction on large images can" | ||
| " be time consuming. To lessen computation time," | ||
| " the input image can be resampled. The shrink" | ||
| " factor, specified as a single integer, describes" | ||
| " this resampling. Shrink factor = 1 is the default." | ||
| ), | ||
| ) | ||
| output_image = File( | ||
| argstr="-o %s", | ||
| name_source=["input_image"], | ||
| hash_files=False, | ||
| keep_extension=True, | ||
| name_template="%s_noise_corrected", | ||
| desc="The output consists of the noise corrected" | ||
| " version of the input image.", | ||
| ) | ||
| save_noise = traits.Bool( | ||
| False, | ||
| mandatory=True, | ||
| usedefault=True, | ||
| desc=("True if the estimated noise should be saved to file."), | ||
| xor=["noise_image"], | ||
| ) | ||
| noise_image = File( | ||
| name_source=["input_image"], | ||
| hash_files=False, | ||
| keep_extension=True, | ||
| name_template="%s_noise", | ||
| desc="Filename for the estimated noise.", | ||
| ) | ||
| verbose = traits.Bool(False, argstr="-v", desc=("Verbose output.")) | ||
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| class DenoiseImageOutputSpec(TraitedSpec): | ||
| output_image = File(exists=True) | ||
| noise_image = File() | ||
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| class DenoiseImage(ANTSCommand): | ||
| """ | ||
| Examples | ||
| -------- | ||
| >>> import copy | ||
| >>> from nipype.interfaces.ants import DenoiseImage | ||
| >>> denoise = DenoiseImage() | ||
| >>> denoise.inputs.dimension = 3 | ||
| >>> denoise.inputs.input_image = 'im1.nii' | ||
| >>> denoise.cmdline | ||
| 'DenoiseImage -d 3 -i im1.nii -n Gaussian -o im1_noise_corrected.nii -s 1' | ||
| >>> denoise_2 = copy.deepcopy(denoise) | ||
| >>> denoise_2.inputs.output_image = 'output_corrected_image.nii.gz' | ||
| >>> denoise_2.inputs.noise_model = 'Rician' | ||
| >>> denoise_2.inputs.shrink_factor = 2 | ||
| >>> denoise_2.cmdline | ||
| 'DenoiseImage -d 3 -i im1.nii -n Rician | ||
| -o output_corrected_image.nii.gz -s 2' | ||
| >>> denoise_3 = DenoiseImage() | ||
| >>> denoise_3.inputs.input_image = 'im1.nii' | ||
| >>> denoise_3.inputs.save_noise = True | ||
| >>> denoise_3.cmdline | ||
| 'DenoiseImage -i im1.nii -n Gaussian | ||
| -o [ im1_noise_corrected.nii, im1_noise.nii ] -s 1' | ||
| """ | ||
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| input_spec = DenoiseImageInputSpec | ||
| output_spec = DenoiseImageOutputSpec | ||
| _cmd = "DenoiseImage" | ||
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| def _format_arg(self, name, trait_spec, value): | ||
| if (name == "output_image") and ( | ||
| self.inputs.save_noise or isdefined(self.inputs.noise_image) | ||
| ): | ||
| newval = "[ {}, {} ]".format( | ||
| self._filename_from_source("output_image"), | ||
| self._filename_from_source("noise_image"), | ||
| ) | ||
| return trait_spec.argstr % newval | ||
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| return super()._format_arg(name, trait_spec, value) |
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