Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/fsl/preprocess.py#L876
Wraps command applywarp
Use FSL’s applywarp to apply the results of a FNIRT registration
>>> from nipype.interfaces import fsl
>>> from nipype.testing import example_data
>>> aw = fsl.ApplyWarp()
>>> aw.inputs.in_file = example_data('structural.nii')
>>> aw.inputs.ref_file = example_data('mni.nii')
>>> aw.inputs.field_file = 'my_coefficients_filed.nii'
>>> res = aw.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
image to be warped
ref_file: (an existing file name)
reference image
[Optional]
abswarp: (a boolean)
treat warp field as absolute: x' = w(x)
mutually_exclusive: relwarp
args: (a string)
Additional parameters to the command
datatype: ('char' or 'short' or 'int' or 'float' or 'double')
Force output data type [char short int float double].
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
field_file: (an existing file name)
file containing warp field
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
interp: ('nn' or 'trilinear' or 'sinc' or 'spline')
interpolation method
mask_file: (an existing file name)
filename for mask image (in reference space)
out_file: (a file name)
output filename
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
postmat: (an existing file name)
filename for post-transform (affine matrix)
premat: (an existing file name)
filename for pre-transform (affine matrix)
relwarp: (a boolean)
treat warp field as relative: x' = x + w(x)
mutually_exclusive: abswarp
superlevel: ('a' or an integer)
level of intermediary supersampling, a for 'automatic' or integer level. Default = 2
supersample: (a boolean)
intermediary supersampling of output, default is off
Outputs:
out_file: (an existing file name)
Warped output file
Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/fsl/preprocess.py#L493
Wraps command flirt
Currently just a light wrapper around FLIRT, with no modifications
ApplyXfm is used to apply an existing tranform to an image
>>> import nipype.interfaces.fsl as fsl
>>> from nipype.testing import example_data
>>> applyxfm = fsl.ApplyXfm()
>>> applyxfm.inputs.in_file = example_data('structural.nii')
>>> applyxfm.inputs.in_matrix_file = example_data('trans.mat')
>>> applyxfm.inputs.out_file = 'newfile.nii'
>>> applyxfm.inputs.reference = example_data('mni.nii')
>>> applyxfm.inputs.apply_xfm = True
>>> result = applyxfm.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file
reference: (an existing file name)
reference file
[Optional]
angle_rep: ('quaternion' or 'euler')
representation of rotation angles
apply_xfm: (a boolean)
apply transformation supplied by in_matrix_file
requires: in_matrix_file
args: (a string)
Additional parameters to the command
bins: (an integer)
number of histogram bins
coarse_search: (an integer)
coarse search delta angle
cost: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or 'labeldiff')
cost function
cost_func: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or
'labeldiff')
cost function
datatype: ('char' or 'short' or 'int' or 'float' or 'double')
force output data type
display_init: (a boolean)
display initial matrix
dof: (an integer)
number of transform degrees of freedom
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
fine_search: (an integer)
fine search delta angle
force_scaling: (a boolean)
force rescaling even for low-res images
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
in_matrix_file: (a file name)
input 4x4 affine matrix
in_weight: (an existing file name)
File for input weighting volume
interp: ('trilinear' or 'nearestneighbour' or 'sinc')
final interpolation method used in reslicing
min_sampling: (a float)
set minimum voxel dimension for sampling
no_clamp: (a boolean)
do not use intensity clamping
no_resample: (a boolean)
do not change input sampling
no_resample_blur: (a boolean)
do not use blurring on downsampling
no_search: (a boolean)
set all angular searches to ranges 0 to 0
out_file: (a file name)
registered output file
out_matrix_file: (a file name)
output affine matrix in 4x4 asciii format
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
padding_size: (an integer)
for applyxfm: interpolates outside image by size
ref_weight: (an existing file name)
File for reference weighting volume
rigid2D: (a boolean)
use 2D rigid body mode - ignores dof
schedule: (an existing file name)
replaces default schedule
searchr_x: (a list of from 2 to 2 items which are an integer)
search angles along x-axis, in degrees
searchr_y: (a list of from 2 to 2 items which are an integer)
search angles along y-axis, in degrees
searchr_z: (a list of from 2 to 2 items which are an integer)
search angles along z-axis, in degrees
sinc_width: (an integer)
full-width in voxels
sinc_window: ('rectangular' or 'hanning' or 'blackman')
sinc window
uses_qform: (a boolean)
initialize using sform or qform
verbose: (an integer)
verbose mode, 0 is least
Outputs:
out_file: (an existing file name)
path/name of registered file (if generated)
out_matrix_file: (an existing file name)
path/name of calculated affine transform (if generated)
Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/fsl/preprocess.py#L101
Wraps command bet
Use FSL BET command for skull stripping.
For complete details, see the BET Documentation.
>>> from nipype.interfaces import fsl
>>> from nipype.testing import example_data
>>> btr = fsl.BET()
>>> btr.inputs.in_file = example_data('structural.nii')
>>> btr.inputs.frac = 0.7
>>> res = btr.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to skull strip
[Optional]
args: (a string)
Additional parameters to the command
center: (a list of at most 3 items which are an integer)
center of gravity in voxels
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
frac: (a float)
fractional intensity threshold
functional: (a boolean)
apply to 4D fMRI data
mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
t2_guided
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (a boolean)
create binary mask image
mesh: (a boolean)
generate a vtk mesh brain surface
no_output: (a boolean)
Don't generate segmented output
out_file: (a file name)
name of output skull stripped image
outline: (a boolean)
create surface outline image
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
padding: (a boolean)
improve BET if FOV is very small in Z (by temporarily padding end slices)
mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
t2_guided
radius: (an integer)
head radius
reduce_bias: (a boolean)
bias field and neck cleanup
mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
t2_guided
remove_eyes: (a boolean)
eye & optic nerve cleanup (can be useful in SIENA)
mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
t2_guided
robust: (a boolean)
robust brain centre estimation (iterates BET several times)
mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
t2_guided
skull: (a boolean)
create skull image
surfaces: (a boolean)
run bet2 and then betsurf to get additional skull and scalp surfaces (includes
registrations)
mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
t2_guided
t2_guided: (a file name)
as with creating surfaces, when also feeding in non-brain-extracted T2 (includes
registrations)
mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
t2_guided
threshold: (a boolean)
apply thresholding to segmented brain image and mask
vertical_gradient: (a float)
vertical gradient in fractional intensity threshold (-1, 1)
Outputs:
mask_file: (a file name)
path/name of binary brain mask (if generated)
meshfile: (a file name)
path/name of vtk mesh file (if generated)
out_file: (a file name)
path/name of skullstripped file
outline_file: (a file name)
path/name of outline file (if generated)
Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/fsl/preprocess.py#L255
Wraps command fast
Use FSL FAST for segmenting and bias correction.
For complete details, see the FAST Documentation.
>>> from nipype.interfaces import fsl
>>> from nipype.testing import example_data
Assign options through the inputs attribute:
>>> fastr = fsl.FAST()
>>> fastr.inputs.in_files = example_data('structural.nii')
>>> out = fastr.run()
Inputs:
[Mandatory]
in_files: (an existing file name)
image, or multi-channel set of images, to be segmented
[Optional]
args: (a string)
Additional parameters to the command
bias_iters: (1 <= an integer <= 10)
number of main-loop iterations during bias-field removal
bias_lowpass: (4 <= an integer <= 40)
bias field smoothing extent (FWHM) in mm
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
hyper: (0.0 <= a floating point number <= 1.0)
segmentation spatial smoothness
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
img_type: (1 or 2 or 3)
int specifying type of image: (1 = T1, 2 = T2, 3 = PD)
init_seg_smooth: (0.0001 <= a floating point number <= 0.1)
initial segmentation spatial smoothness (during bias field estimation)
init_transform: (an existing file name)
<standard2input.mat> initialise using priors
iters_afterbias: (an integer >= 1)
number of main-loop iterations after bias-field removal
manual_seg: (an existing file name)
Filename containing intensities
mixel_smooth: (0.0 <= a floating point number <= 1.0)
spatial smoothness for mixeltype
no_bias: (a boolean)
do not remove bias field
no_pve: (a boolean)
turn off PVE (partial volume estimation)
number_classes: (1 <= an integer <= 10)
number of tissue-type classes
other_priors: (a list of from 3 to 3 items which are a file name)
alternative prior images
out_basename: (a file name)
base name of output files
output_biascorrected: (a boolean)
output restored image (bias-corrected image)
output_biasfield: (a boolean)
output estimated bias field
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
probability_maps: (a boolean)
outputs individual probability maps
segment_iters: (1 <= an integer <= 50)
number of segmentation-initialisation iterations
segments: (a boolean)
outputs a separate binary image for each tissue type
use_priors: (a boolean)
use priors throughout
verbose: (a boolean)
switch on diagnostic messages
Outputs:
bias_field: (a file name)
mixeltype: (a file name)
path/name of mixeltype volume file _mixeltype
partial_volume_files: (a file name)
partial_volume_map: (a file name)
path/name of partial volume file _pveseg
probability_maps: (a file name)
restored_image: (a file name)
tissue_class_files: (a file name)
tissue_class_map: (an existing file name)
path/name of binary segmented volume file one val for each class _seg
Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/fsl/preprocess.py#L1279
Wraps command run_first_all
Use FSL’s run_first_all command to segment subcortical volumes
http://www.fmrib.ox.ac.uk/fsl/first/index.html
>>> from nipype.interfaces import fsl
>>> first = fsl.FIRST()
>>> first.inputs.in_file = 'structural.nii'
>>> first.inputs.out_file = 'segmented.nii'
>>> res = first.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input data file
out_file: (a file name, nipype default value: segmented)
output data file
[Optional]
affine_file: (an existing file name)
Affine matrix to use (e.g. img2std.mat) (does not re-run registration)
args: (a string)
Additional parameters to the command
brain_extracted: (a boolean)
Input structural image is already brain-extracted
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
list_of_specific_structures: (a list of at least 1 items which are a string)
Runs only on the specified structures (e.g. L_Hipp, R_HippL_Accu, R_Accu, L_Amyg,
R_AmygL_Caud, R_Caud, L_Pall, R_PallL_Puta, R_Puta, L_Thal, R_Thal, BrStem
method: ('auto' or 'fast' or 'none')
Method must be one of auto, fast, none, or it can be entered using the
'method_as_numerical_threshold' input
mutually_exclusive: method_as_numerical_threshold
method_as_numerical_threshold: (a float)
Specify a numerical threshold value or use the 'method' input to choose auto, fast, or
none
no_cleanup: (a boolean)
Input structural image is already brain-extracted
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
verbose: (a boolean)
Use verbose logging.
Outputs:
bvars: (an existing file name)
bvars for each subcortical region
original_segmentations: (an existing file name)
3D image file containing the segmented regions as integer values. Uses CMA labelling
segmentation_file: (an existing file name)
4D image file containing a single volume per segmented region
vtk_surfaces: (an existing file name)
VTK format meshes for each subcortical region
Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/fsl/preprocess.py#L445
Wraps command flirt
Use FSL FLIRT for coregistration.
For complete details, see the FLIRT Documentation.
>>> from nipype.interfaces import fsl
>>> from nipype.testing import example_data
>>> flt = fsl.FLIRT(bins=640, cost_func='mutualinfo')
>>> flt.inputs.in_file = example_data('structural.nii')
>>> flt.inputs.reference = example_data('mni.nii')
>>> flt.inputs.out_file = 'moved_subject.nii'
>>> flt.inputs.out_matrix_file = 'subject_to_template.mat'
>>> res = flt.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file
reference: (an existing file name)
reference file
[Optional]
angle_rep: ('quaternion' or 'euler')
representation of rotation angles
apply_xfm: (a boolean)
apply transformation supplied by in_matrix_file
requires: in_matrix_file
args: (a string)
Additional parameters to the command
bins: (an integer)
number of histogram bins
coarse_search: (an integer)
coarse search delta angle
cost: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or 'labeldiff')
cost function
cost_func: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or
'labeldiff')
cost function
datatype: ('char' or 'short' or 'int' or 'float' or 'double')
force output data type
display_init: (a boolean)
display initial matrix
dof: (an integer)
number of transform degrees of freedom
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
fine_search: (an integer)
fine search delta angle
force_scaling: (a boolean)
force rescaling even for low-res images
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
in_matrix_file: (a file name)
input 4x4 affine matrix
in_weight: (an existing file name)
File for input weighting volume
interp: ('trilinear' or 'nearestneighbour' or 'sinc')
final interpolation method used in reslicing
min_sampling: (a float)
set minimum voxel dimension for sampling
no_clamp: (a boolean)
do not use intensity clamping
no_resample: (a boolean)
do not change input sampling
no_resample_blur: (a boolean)
do not use blurring on downsampling
no_search: (a boolean)
set all angular searches to ranges 0 to 0
out_file: (a file name)
registered output file
out_matrix_file: (a file name)
output affine matrix in 4x4 asciii format
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
padding_size: (an integer)
for applyxfm: interpolates outside image by size
ref_weight: (an existing file name)
File for reference weighting volume
rigid2D: (a boolean)
use 2D rigid body mode - ignores dof
schedule: (an existing file name)
replaces default schedule
searchr_x: (a list of from 2 to 2 items which are an integer)
search angles along x-axis, in degrees
searchr_y: (a list of from 2 to 2 items which are an integer)
search angles along y-axis, in degrees
searchr_z: (a list of from 2 to 2 items which are an integer)
search angles along z-axis, in degrees
sinc_width: (an integer)
full-width in voxels
sinc_window: ('rectangular' or 'hanning' or 'blackman')
sinc window
uses_qform: (a boolean)
initialize using sform or qform
verbose: (an integer)
verbose mode, 0 is least
Outputs:
out_file: (an existing file name)
path/name of registered file (if generated)
out_matrix_file: (an existing file name)
path/name of calculated affine transform (if generated)
Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/fsl/preprocess.py#L745
Wraps command fnirt
Use FSL FNIRT for non-linear registration.
>>> from nipype.interfaces import fsl
>>> from nipype.testing import example_data
>>> fnt = fsl.FNIRT(affine_file=example_data('trans.mat'))
>>> res = fnt.run(ref_file=example_data('mni.nii', in_file=example_data('structural.nii'))
T1 -> Mni153
>>> from nipype.interfaces import fsl
>>> fnirt_mprage = fsl.FNIRT()
>>> fnirt_mprage.inputs.in_fwhm = [8, 4, 2, 2]
>>> fnirt_mprage.inputs.subsampling_scheme = [4, 2, 1, 1]
Specify the resolution of the warps
>>> fnirt_mprage.inputs.warp_resolution = (6, 6, 6)
>>> res = fnirt_mprage.run(in_file='structural.nii', ref_file='mni.nii', warped_file='warped.nii', fieldcoeff_file='fieldcoeff.nii')
We can check the command line and confirm that it’s what we expect.
>>> fnirt_mprage.cmdline
'fnirt --cout=fieldcoeff.nii --in=structural.nii --infwhm=8,4,2,2 --ref=mni.nii --subsamp=4,2,1,1 --warpres=6,6,6 --iout=warped.nii'
Inputs:
[Mandatory]
in_file: (an existing file name)
name of input image
ref_file: (an existing file name)
name of reference image
[Optional]
affine_file: (an existing file name)
name of file containing affine transform
apply_inmask: (a list of items which are 0 or 1)
list of iterations to use input mask on (1 to use, 0 to skip)
mutually_exclusive: skip_inmask
apply_intensity_mapping: (a list of items which are 0 or 1)
List of subsampling levels to apply intensity mapping for (0 to skip, 1 to apply)
mutually_exclusive: skip_intensity_mapping
apply_refmask: (a list of items which are 0 or 1)
list of iterations to use reference mask on (1 to use, 0 to skip)
mutually_exclusive: skip_refmask
args: (a string)
Additional parameters to the command
bias_regularization_lambda: (a float)
Weight of regularisation for bias-field, default 10000
biasfield_resolution: (a tuple of the form: (an integer, an integer, an integer))
Resolution (in mm) of bias-field modelling local intensities, default 50, 50, 50
config_file: (an existing file name)
Name of config file specifying command line arguments
derive_from_ref: (a boolean)
If true, ref image is used to calculate derivatives. Default false
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
field_file: (a boolean or a file name)
name of output file with field or true
fieldcoeff_file: (a boolean or a file name)
name of output file with field coefficients or true
hessian_precision: ('double' or 'float')
Precision for representing Hessian, double or float. Default double
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
in_fwhm: (a list of items which are an integer)
FWHM (in mm) of gaussian smoothing kernel for input volume, default [6, 4, 2, 2]
in_intensitymap_file: (an existing file name)
name of file/files containing initial intensity mapingusually generated by previos fnirt
run
inmask_file: (an existing file name)
name of file with mask in input image space
inmask_val: (a float)
Value to mask out in --in image. Default =0.0
intensity_mapping_model: ('none' or 'global_linear' or 'global_non_linearlocal_linear' or
'global_non_linear_with_bias' or 'local_non_linear')
Model for intensity-mapping
intensity_mapping_order: (an integer)
Order of poynomial for mapping intensities, default 5
inwarp_file: (an existing file name)
name of file containing initial non-linear warps
jacobian_file: (a boolean or a file name)
name of file for writing out the Jacobianof the field (for diagnostic or VBM purposes)
jacobian_range: (a tuple of the form: (a float, a float))
Allowed range of Jacobian determinants, default 0.01, 100.0
log_file: (a file name)
Name of log-file
max_nonlin_iter: (a list of items which are an integer)
Max # of non-linear iterations list, default [5, 5, 5, 5]
modulatedref_file: (a boolean or a file name)
name of file for writing out intensity modulated--ref (for diagnostic purposes)
out_intensitymap_file: (a boolean or a file name)
name of files for writing information pertaining to intensity mapping
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
ref_fwhm: (a list of items which are an integer)
FWHM (in mm) of gaussian smoothing kernel for ref volume, default [4, 2, 0, 0]
refmask_file: (an existing file name)
name of file with mask in reference space
refmask_val: (a float)
Value to mask out in --ref image. Default =0.0
regularization_lambda: (a list of items which are a float)
Weight of regularisation, default depending on --ssqlambda and --regmod switches. See
user documetation.
regularization_model: ('membrane_energy' or 'bending_energy')
Model for regularisation of warp-field [membrane_energy bending_energy], default
bending_energy
skip_implicit_in_masking: (a boolean)
skip implicit masking based on valuein --in image. Default = 0
skip_implicit_ref_masking: (a boolean)
skip implicit masking based on valuein --ref image. Default = 0
skip_inmask: (a boolean)
skip specified inmask if set, default false
mutually_exclusive: apply_inmask
skip_intensity_mapping: (a boolean)
Skip estimate intensity-mapping default false
mutually_exclusive: apply_intensity_mapping
skip_lambda_ssq: (a boolean)
If true, lambda is not weighted by current ssq, default false
skip_refmask: (a boolean)
Skip specified refmask if set, default false
mutually_exclusive: apply_refmask
spline_order: (an integer)
Order of spline, 2->Qadratic spline, 3->Cubic spline. Default=3
subsampling_scheme: (a list of items which are an integer)
sub-sampling scheme, list, default [4, 2, 1, 1]
warp_resolution: (a tuple of the form: (an integer, an integer, an integer))
(approximate) resolution (in mm) of warp basis in x-, y- and z-direction, default 10,
10, 10
warped_file: (a file name)
name of output image
Outputs:
field_file: (a file name)
file with warp field
fieldcoeff_file: (an existing file name)
file with field coefficients
jacobian_file: (a file name)
file containing Jacobian of the field
log_file: (a file name)
Name of log-file
modulatedref_file: (a file name)
file containing intensity modulated --ref
out_intensitymap_file: (a file name)
file containing info pertaining to intensity mapping
warped_file: (an existing file name)
warped image
Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/fsl/preprocess.py#L1122
Wraps command fugue
Use FSL FUGUE to unwarp epi’s with fieldmaps
Please insert examples for use of this command
Inputs:
[Mandatory]
[Optional]
args: (a string)
Additional parameters to the command
asym_se_time: (a float)
set the fieldmap asymmetric spin echo time (sec)
despike_2dfilter: (a boolean)
apply a 2D de-spiking filter
despike_theshold: (a float)
specify the threshold for de-spiking (default=3.0)
dwell_time: (a float)
set the EPI dwell time per phase-encode line - same as echo spacing - (sec)
dwell_to_asym_ratio: (a float)
set the dwell to asym time ratio
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
fmap_in_file: (an existing file name)
filename for loading fieldmap (rad/s)
fmap_out_file: (a file name)
filename for saving fieldmap (rad/s)
fourier_order: (an integer)
apply Fourier (sinusoidal) fitting of order N
icorr: (a boolean)
apply intensity correction to unwarping (pixel shift method only)
requires: shift_in_file
icorr_only: (a boolean)
apply intensity correction only
requires: unwarped_file
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
in_file: (an existing file name)
filename of input volume
mask_file: (an existing file name)
filename for loading valid mask
median_2dfilter: (a boolean)
apply 2D median filtering
no_extend: (a boolean)
do not apply rigid-body extrapolation to the fieldmap
no_gap_fill: (a boolean)
do not apply gap-filling measure to the fieldmap
nokspace: (a boolean)
do not use k-space forward warping
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
pava: (a boolean)
apply monotonic enforcement via PAVA
phase_conjugate: (a boolean)
apply phase conjugate method of unwarping
phasemap_file: (an existing file name)
filename for input phase image
poly_order: (an integer)
apply polynomial fitting of order N
save_unmasked_fmap: (a boolean or a file name)
saves the unmasked fieldmap when using --savefmap
requires: fmap_out_file
save_unmasked_shift: (a boolean or a file name)
saves the unmasked shiftmap when using --saveshift
requires: shift_out_file
shift_in_file: (an existing file name)
filename for reading pixel shift volume
shift_out_file: (a file name)
filename for saving pixel shift volume
smooth2d: (a float)
apply 2D Gaussian smoothing of sigma N (in mm)
smooth3d: (a float)
apply 3D Gaussian smoothing of sigma N (in mm)
unwarp_direction: ('x' or 'y' or 'z' or 'x-' or 'y-' or 'z-')
specifies direction of warping (default y)
unwarped_file: (a file name)
apply unwarping and save as filename
Outputs:
unwarped_file: (an existing file name)
unwarped file
Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/fsl/preprocess.py#L556
Wraps command mcflirt
Use FSL MCFLIRT to do within-modality motion correction.
For complete details, see the MCFLIRT Documentation.
>>> from nipype.interfaces import fsl
>>> from nipype.testing import example_data
>>> mcflt = fsl.MCFLIRT(in_file=example_data('functional.nii'), cost='mutualinfo')
>>> res = mcflt.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
timeseries to motion-correct
[Optional]
args: (a string)
Additional parameters to the command
bins: (an integer)
number of histogram bins
cost: ('mutualinfo' or 'woods' or 'corratio' or 'normcorr' or 'normmi' or 'leastsquares')
cost function to optimize
dof: (an integer)
degrees of freedom for the transformation
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
init: (an existing file name)
inital transformation matrix
interpolation: ('spline' or 'nn' or 'sinc')
interpolation method for transformation
mean_vol: (a boolean)
register to mean volume
out_file: (a file name)
file to write
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
ref_file: (an existing file name)
target image for motion correction
ref_vol: (an integer)
volume to align frames to
rotation: (an integer)
scaling factor for rotation tolerances
save_mats: (a boolean)
save transformation matrices
save_plots: (a boolean)
save transformation parameters
save_rms: (a boolean)
save rms displacement parameters
scaling: (a float)
scaling factor to use
smooth: (a float)
smoothing factor for the cost function
stages: (an integer)
stages (if 4, perform final search with sinc interpolation
stats_imgs: (a boolean)
produce variance and std. dev. images
use_contour: (a boolean)
run search on contour images
use_gradient: (a boolean)
run search on gradient images
Outputs:
mat_file: (an existing file name)
transformation matrices
mean_img: (an existing file name)
mean timeseries image
out_file: (an existing file name)
motion-corrected timeseries
par_file: (an existing file name)
text-file with motion parameters
rms_files: (an existing file name)
absolute and relative displacement parameters
std_img: (an existing file name)
standard deviation image
variance_img: (an existing file name)
variance image
Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/fsl/preprocess.py#L1202
Wraps command prelude
Use FSL prelude to do phase unwrapping
Please insert examples for use of this command
Inputs:
[Mandatory]
complex_phase_file: (an existing file name)
complex phase input volume
mutually_exclusive: magnitude_file, phase_file
magnitude_file: (an existing file name)
file containing magnitude image
mutually_exclusive: complex_phase_file
phase_file: (an existing file name)
raw phase file
mutually_exclusive: complex_phase_file
[Optional]
args: (a string)
Additional parameters to the command
end: (an integer)
final image number to process (default Inf)
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
label_file: (a file name)
saving the area labels output
labelprocess2d: (a boolean)
does label processing in 2D (slice at a time)
mask_file: (an existing file name)
filename of mask input volume
num_partitions: (an integer)
number of phase partitions to use
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
process2d: (a boolean)
does all processing in 2D (slice at a time)
mutually_exclusive: labelprocess2d
process3d: (a boolean)
forces all processing to be full 3D
mutually_exclusive: labelprocess2d, process2d
rawphase_file: (a file name)
saving the raw phase output
removeramps: (a boolean)
remove phase ramps during unwrapping
savemask_file: (a file name)
saving the mask volume
start: (an integer)
first image number to process (default 0)
threshold: (a float)
intensity threshold for masking
unwrapped_phase_file: (a file name)
file containing unwrapepd phase
Outputs:
unwrapped_phase_file: (a file name)
unwrapped phase file
Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/fsl/preprocess.py#L1005
Wraps command susan
use FSL SUSAN to perform smoothing
>>> from nipype.interfaces import fsl
>>> from nipype.testing import example_data
>>> print anatfile
anatomical.nii
>>> sus = fsl.SUSAN()
>>> sus.inputs.in_file = example_data('structural.nii')
>>> sus.inputs.brightness_threshold = 2000.0
>>> sus.inputs.fwhm = 8.0
>>> result = sus.run()
Inputs:
[Mandatory]
brightness_threshold: (a float)
brightness threshold and should be greater than noise level and less than contrast of
edges to be preserved.
fwhm: (a float)
fwhm of smoothing, in mm, gets converted using sqrt(8*log(2))
in_file: (an existing file name)
filename of input timeseries
[Optional]
args: (a string)
Additional parameters to the command
dimension: (3 or 2, nipype default value: 3)
within-plane (2) or fully 3D (3)
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name)
output file name
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
usans: (a list of at most 2 items which are a tuple of the form: (an existing file name,
a float), nipype default value: [])
determines whether the smoothing area (USAN) is to be found from secondary images (0, 1
or 2). A negative value for any brightness threshold will auto-set the threshold at 10%
of the robust range
use_median: (1 or 0, nipype default value: 1)
whether to use a local median filter in the cases where single-point noise is detected
Outputs:
smoothed_file: (an existing file name)
smoothed output file
Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/fsl/preprocess.py#L942
Wraps command slicetimer
use FSL slicetimer to perform slice timing correction.
>>> from nipype.interfaces import fsl
>>> from nipype.testing import example_data
>>> st = fsl.SliceTimer()
>>> st.inputs.in_file = example_data('functional.nii')
>>> st.inputs.interleaved = True
>>> result = st.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
filename of input timeseries
[Optional]
args: (a string)
Additional parameters to the command
custom_order: (an existing file name)
filename of single-column custom interleave order file (first slice is referred to as 1
not 0)
custom_timings: (an existing file name)
slice timings, in fractions of TR, range 0:1 (default is 0.5 = no shift)
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
global_shift: (a float)
shift in fraction of TR, range 0:1 (default is 0.5 = no shift)
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
index_dir: (a boolean)
slice indexing from top to bottom
interleaved: (a boolean)
use interleaved acquisition
out_file: (a file name)
filename of output timeseries
output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
FSL output type
slice_direction: (1 or 2 or 3)
direction of slice acquisition (x=1, y=2, z=3) - default is z
time_repetition: (a float)
Specify TR of data - default is 3s
Outputs:
slice_time_corrected_file: (an existing file name)
slice time corrected file