interfaces.freesurfer.preprocess¶
ApplyVolTransform¶
Wraps command mri_vol2vol
Use FreeSurfer mri_vol2vol to apply a transform.
Examples¶
>>> from nipype.interfaces.freesurfer import ApplyVolTransform
>>> applyreg = ApplyVolTransform()
>>> applyreg.inputs.source_file = 'structural.nii'
>>> applyreg.inputs.reg_file = 'register.dat'
>>> applyreg.inputs.transformed_file = 'struct_warped.nii'
>>> applyreg.inputs.fs_target = True
>>> applyreg.cmdline
'mri_vol2vol --fstarg --reg register.dat --mov structural.nii --o struct_warped.nii'
Inputs:
[Mandatory]
fs_target: (a boolean)
use orig.mgz from subject in regfile as target
flag: --fstarg
mutually_exclusive: target_file, tal, fs_target
requires: reg_file
fsl_reg_file: (an existing file name)
fslRAS-to-fslRAS matrix (FSL format)
flag: --fsl %s
mutually_exclusive: reg_file, fsl_reg_file, xfm_reg_file,
reg_header, subject
reg_file: (an existing file name)
tkRAS-to-tkRAS matrix (tkregister2 format)
flag: --reg %s
mutually_exclusive: reg_file, fsl_reg_file, xfm_reg_file,
reg_header, subject
reg_header: (a boolean)
ScannerRAS-to-ScannerRAS matrix = identity
flag: --regheader
mutually_exclusive: reg_file, fsl_reg_file, xfm_reg_file,
reg_header, subject
source_file: (an existing file name)
Input volume you wish to transform
flag: --mov %s
subject: (a unicode string)
set matrix = identity and use subject for any templates
flag: --s %s
mutually_exclusive: reg_file, fsl_reg_file, xfm_reg_file,
reg_header, subject
tal: (a boolean)
map to a sub FOV of MNI305 (with --reg only)
flag: --tal
mutually_exclusive: target_file, tal, fs_target
target_file: (an existing file name)
Output template volume
flag: --targ %s
mutually_exclusive: target_file, tal, fs_target
xfm_reg_file: (an existing file name)
ScannerRAS-to-ScannerRAS matrix (MNI format)
flag: --xfm %s
mutually_exclusive: reg_file, fsl_reg_file, xfm_reg_file,
reg_header, subject
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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
interp: (u'trilin' or u'nearest' or u'cubic')
Interpolation method (<trilin> or nearest)
flag: --interp %s
inverse: (a boolean)
sample from target to source
flag: --inv
invert_morph: (a boolean)
Compute and use the inverse of the non-linear morph to resample the
input volume. To be used by --m3z.
flag: --inv-morph
requires: m3z_file
m3z_file: (a file name)
This is the morph to be applied to the volume. Unless the morph is
in mri/transforms (eg.: for talairach.m3z computed by reconall), you
will need to specify the full path to this morph and use the
--noDefM3zPath flag.
flag: --m3z %s
no_ded_m3z_path: (a boolean)
To be used with the m3z flag. Instructs the code not to look for
them3z morph in the default location
(SUBJECTS_DIR/subj/mri/transforms), but instead just use the path
indicated in --m3z.
flag: --noDefM3zPath
requires: m3z_file
no_resample: (a boolean)
Do not resample; just change vox2ras matrix
flag: --no-resample
subjects_dir: (an existing directory name)
subjects directory
tal_resolution: (a float)
Resolution to sample when using tal
flag: --talres %.10f
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
transformed_file: (a file name)
Output volume
flag: --o %s
Outputs:
transformed_file: (an existing file name)
Path to output file if used normally
BBRegister¶
Wraps command bbregister
Use FreeSurfer bbregister to register a volume to the Freesurfer anatomical.
This program performs within-subject, cross-modal registration using a boundary-based cost function. The registration is constrained to be 6 DOF (rigid). It is required that you have an anatomical scan of the subject that has already been recon-all-ed using freesurfer.
Examples¶
>>> from nipype.interfaces.freesurfer import BBRegister
>>> bbreg = BBRegister(subject_id='me', source_file='structural.nii', init='header', contrast_type='t2')
>>> bbreg.cmdline
'bbregister --t2 --init-header --reg structural_bbreg_me.dat --mov structural.nii --s me'
Inputs:
[Mandatory]
contrast_type: (u't1' or u't2')
contrast type of image
flag: --%s
init: (u'spm' or u'fsl' or u'header')
initialize registration spm, fsl, header
flag: --init-%s
mutually_exclusive: init_reg_file
init_reg_file: (an existing file name)
existing registration file
flag: --init-reg %s
mutually_exclusive: init
source_file: (a file name)
source file to be registered
flag: --mov %s
subject_id: (a unicode string)
freesurfer subject id
flag: --s %s
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
epi_mask: (a boolean)
mask out B0 regions in stages 1 and 2
flag: --epi-mask
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
intermediate_file: (an existing file name)
Intermediate image, e.g. in case of partial FOV
flag: --int %s
out_fsl_file: (a boolean or a file name)
write the transformation matrix in FSL FLIRT format
flag: --fslmat %s
out_reg_file: (a file name)
output registration file
flag: --reg %s
reg_frame: (an integer (int or long))
0-based frame index for 4D source file
flag: --frame %d
mutually_exclusive: reg_middle_frame
reg_middle_frame: (a boolean)
Register middle frame of 4D source file
flag: --mid-frame
mutually_exclusive: reg_frame
registered_file: (a boolean or a file name)
output warped sourcefile either True or filename
flag: --o %s
spm_nifti: (a boolean)
force use of nifti rather than analyze with SPM
flag: --spm-nii
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
min_cost_file: (an existing file name)
Output registration minimum cost file
out_fsl_file: (a file name)
Output FLIRT-style registration file
out_reg_file: (an existing file name)
Output registration file
registered_file: (a file name)
Registered and resampled source file
CALabel¶
Wraps command mri_ca_label
For complete details, see the FS Documentation
Examples¶
>>> from nipype.interfaces import freesurfer
>>> ca_label = freesurfer.CALabel()
>>> ca_label.inputs.in_file = "norm.mgz"
>>> ca_label.inputs.out_file = "out.mgz"
>>> ca_label.inputs.transform = "trans.mat"
>>> ca_label.inputs.template = "Template_6.nii" # in practice use .gcs extension
>>> ca_label.cmdline
'mri_ca_label norm.mgz trans.mat Template_6.nii out.mgz'
Inputs:
[Mandatory]
in_file: (an existing file name)
Input volume for CALabel
flag: %s, position: -4
out_file: (a file name)
Output file for CALabel
flag: %s, position: -1
template: (an existing file name)
Input template for CALabel
flag: %s, position: -2
transform: (an existing file name)
Input transform for CALabel
flag: %s, position: -3
[Optional]
align: (a boolean)
Align CALabel
flag: -align
args: (a unicode string)
Additional parameters to the command
flag: %s
aseg: (a file name)
Undocumented flag. Autorecon3 uses ../mri/aseg.presurf.mgz as input
file
flag: -aseg %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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
in_vol: (an existing file name)
set input volume
flag: -r %s
intensities: (an existing file name)
input label intensities file(used in longitudinal processing)
flag: -r %s
label: (a file name)
Undocumented flag. Autorecon3 uses
../label/{hemisphere}.cortex.label as input file
flag: -l %s
no_big_ventricles: (a boolean)
No big ventricles
flag: -nobigventricles
num_threads: (an integer (int or long))
allows for specifying more threads
prior: (a float)
Prior for CALabel
flag: -prior %.1f
relabel_unlikely: (a tuple of the form: (an integer (int or long), a
float))
Reclassify voxels at least some std devs from the mean using some
size Gaussian window
flag: -relabel_unlikely %d %.1f
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
out_file: (a file name)
Output volume from CALabel
CANormalize¶
Wraps command mri_ca_normalize
This program creates a normalized volume using the brain volume and an input gca file.
For complete details, see the FS Documentation
Examples¶
>>> from nipype.interfaces import freesurfer
>>> ca_normalize = freesurfer.CANormalize()
>>> ca_normalize.inputs.in_file = "T1.mgz"
>>> ca_normalize.inputs.atlas = "atlas.nii.gz" # in practice use .gca atlases
>>> ca_normalize.inputs.transform = "trans.mat" # in practice use .lta transforms
>>> ca_normalize.cmdline
'mri_ca_normalize T1.mgz atlas.nii.gz trans.mat T1_norm.mgz'
Inputs:
[Mandatory]
atlas: (an existing file name)
The atlas file in gca format
flag: %s, position: -3
in_file: (an existing file name)
The input file for CANormalize
flag: %s, position: -4
transform: (an existing file name)
The tranform file in lta format
flag: %s, position: -2
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
control_points: (a file name)
File name for the output control points
flag: -c %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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
long_file: (a file name)
undocumented flag used in longitudinal processing
flag: -long %s
mask: (an existing file name)
Specifies volume to use as mask
flag: -mask %s
out_file: (a file name)
The output file for CANormalize
flag: %s, position: -1
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
control_points: (a file name)
The output control points for Normalize
out_file: (a file name)
The output file for Normalize
CARegister¶
Wraps command mri_ca_register
Generates a multi-dimensional talairach transform from a gca file and talairach.lta file
For complete details, see the FS Documentation
Examples¶
>>> from nipype.interfaces import freesurfer
>>> ca_register = freesurfer.CARegister()
>>> ca_register.inputs.in_file = "norm.mgz"
>>> ca_register.inputs.out_file = "talairach.m3z"
>>> ca_register.cmdline
'mri_ca_register norm.mgz talairach.m3z'
Inputs:
[Mandatory]
in_file: (an existing file name)
The input volume for CARegister
flag: %s, position: -3
[Optional]
A: (an integer (int or long))
undocumented flag used in longitudinal processing
flag: -A %d
align: (a string)
Specifies when to perform alignment
flag: -align-%s
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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
invert_and_save: (a boolean)
Invert and save the .m3z multi-dimensional talaraich transform to x,
y, and z .mgz files
flag: -invert-and-save, position: -4
l_files: (a list of items which are a file name)
undocumented flag used in longitudinal processing
flag: -l %s
levels: (an integer (int or long))
defines how many surrounding voxels will be used in interpolations,
default is 6
flag: -levels %d
mask: (an existing file name)
Specifies volume to use as mask
flag: -mask %s
no_big_ventricles: (a boolean)
No big ventricles
flag: -nobigventricles
num_threads: (an integer (int or long))
allows for specifying more threads
out_file: (a file name)
The output volume for CARegister
flag: %s, position: -1
subjects_dir: (an existing directory name)
subjects directory
template: (an existing file name)
The template file in gca format
flag: %s, position: -2
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
transform: (an existing file name)
Specifies transform in lta format
flag: -T %s
Outputs:
out_file: (a file name)
The output file for CARegister
ConcatenateLTA¶
Wraps command mri_concatenate_lta
concatenates two consecutive LTA transformations into one overall transformation, Out = LTA2*LTA1
Examples¶
>>> from nipype.interfaces.freesurfer import ConcatenateLTA
>>> conc_lta = ConcatenateLTA()
>>> conc_lta.inputs.in_lta1 = 'trans.mat'
>>> conc_lta.inputs.in_lta2 = 'trans.mat'
>>> conc_lta.cmdline
'mri_concatenate_lta trans.mat trans.mat trans-long.mat'
Inputs:
[Mandatory]
in_lta1: (an existing file name)
maps some src1 to dst1
flag: %s, position: -3
in_lta2: (an existing file name)
maps dst1(src2) to dst2
flag: %s, position: -2
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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)
the combined LTA maps: src1 to dst2 = LTA2*LTA1
flag: %s, position: -1
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
out_file: (a file name)
the combined LTA maps: src1 to dst2 = LTA2*LTA1
DICOMConvert¶
Wraps command mri_convert
use fs mri_convert to convert dicom files
Examples¶
>>> from nipype.interfaces.freesurfer import DICOMConvert
>>> cvt = DICOMConvert()
>>> cvt.inputs.dicom_dir = 'dicomdir'
>>> cvt.inputs.file_mapping = [('nifti', '*.nii'), ('info', 'dicom*.txt'), ('dti', '*dti.bv*')]
Inputs:
[Mandatory]
base_output_dir: (a directory name)
directory in which subject directories are created
dicom_dir: (an existing directory name)
dicom directory from which to convert dicom files
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
dicom_info: (an existing file name)
File containing summary information from mri_parse_sdcmdir
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
file_mapping: (a list of items which are a tuple of the form: (a
unicode string, a unicode string))
defines the output fields of interface
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
ignore_single_slice: (a boolean)
ignore volumes containing a single slice
requires: dicom_info
out_type: (u'cor' or u'mgh' or u'mgz' or u'minc' or u'analyze' or
u'analyze4d' or u'spm' or u'afni' or u'brik' or u'bshort' or
u'bfloat' or u'sdt' or u'outline' or u'otl' or u'gdf' or u'nifti1'
or u'nii' or u'niigz', nipype default value: niigz)
defines the type of output file produced
seq_list: (a list of items which are a unicode string)
list of pulse sequence names to be converted.
requires: dicom_info
subject_dir_template: (a unicode string, nipype default value:
S.%04d)
template for subject directory name
subject_id: (any value)
subject identifier to insert into template
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
None
EditWMwithAseg¶
Wraps command mri_edit_wm_with_aseg
Edits a wm file using a segmentation
Examples¶
>>> from nipype.interfaces.freesurfer import EditWMwithAseg
>>> editwm = EditWMwithAseg()
>>> editwm.inputs.in_file = "T1.mgz"
>>> editwm.inputs.brain_file = "norm.mgz"
>>> editwm.inputs.seg_file = "aseg.mgz"
>>> editwm.inputs.out_file = "wm.asegedit.mgz"
>>> editwm.inputs.keep_in = True
>>> editwm.cmdline
'mri_edit_wm_with_aseg -keep-in T1.mgz norm.mgz aseg.mgz wm.asegedit.mgz'
Inputs:
[Mandatory]
brain_file: (an existing file name)
Input brain/T1 file
flag: %s, position: -3
in_file: (an existing file name)
Input white matter segmentation file
flag: %s, position: -4
out_file: (a file name)
File to be written as output
flag: %s, position: -1
seg_file: (an existing file name)
Input presurf segmentation file
flag: %s, position: -2
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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
keep_in: (a boolean)
Keep edits as found in input volume
flag: -keep-in
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
out_file: (a file name)
Output edited WM file
FitMSParams¶
Wraps command mri_ms_fitparms
Estimate tissue paramaters from a set of FLASH images.
Examples¶
>>> from nipype.interfaces.freesurfer import FitMSParams
>>> msfit = FitMSParams()
>>> msfit.inputs.in_files = ['flash_05.mgz', 'flash_30.mgz']
>>> msfit.inputs.out_dir = 'flash_parameters'
>>> msfit.cmdline
'mri_ms_fitparms flash_05.mgz flash_30.mgz flash_parameters'
Inputs:
[Mandatory]
in_files: (a list of items which are an existing file name)
list of FLASH images (must be in mgh format)
flag: %s, position: -2
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
flip_list: (a list of items which are an integer (int or long))
list of flip angles of the input files
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_dir: (a directory name)
directory to store output in
flag: %s, position: -1
subjects_dir: (an existing directory name)
subjects directory
te_list: (a list of items which are a float)
list of TEs of the input files (in msec)
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
tr_list: (a list of items which are an integer (int or long))
list of TRs of the input files (in msec)
xfm_list: (a list of items which are an existing file name)
list of transform files to apply to each FLASH image
Outputs:
pd_image: (an existing file name)
image of estimated proton density values
t1_image: (an existing file name)
image of estimated T1 relaxation values
t2star_image: (an existing file name)
image of estimated T2* values
MNIBiasCorrection¶
Wraps command mri_nu_correct.mni
Wrapper for nu_correct, a program from the Montreal Neurological Insitute (MNI) used for correcting intensity non-uniformity (ie, bias fields). You must have the MNI software installed on your system to run this. See [www.bic.mni.mcgill.ca/software/N3] for more info.
mri_nu_correct.mni uses float internally instead of uchar. It also rescales the output so that the global mean is the same as that of the input. These two changes are linked and can be turned off with –no-float
Examples¶
>>> from nipype.interfaces.freesurfer import MNIBiasCorrection
>>> correct = MNIBiasCorrection()
>>> correct.inputs.in_file = "norm.mgz"
>>> correct.inputs.iterations = 6
>>> correct.inputs.protocol_iterations = 1000
>>> correct.inputs.distance = 50
>>> correct.cmdline
'mri_nu_correct.mni --distance 50 --i norm.mgz --n 6 --o norm_output.mgz --proto-iters 1000'
References:¶
[http://freesurfer.net/fswiki/mri_nu_correct.mni] [http://www.bic.mni.mcgill.ca/software/N3] [https://github.com/BIC-MNI/N3]
Inputs:
[Mandatory]
in_file: (an existing file name)
input volume. Input can be any format accepted by mri_convert.
flag: --i %s
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
distance: (an integer (int or long))
N3 -distance option
flag: --distance %d
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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
iterations: (an integer (int or long))
Number of iterations to run nu_correct. Default is 4. This is the
number of times that nu_correct is repeated (ie, using the output
from the previous run as the input for the next). This is different
than the -iterations option to nu_correct.
flag: --n %d
mask: (an existing file name)
brainmask volume. Input can be any format accepted by mri_convert.
flag: --mask %s
no_rescale: (a boolean)
do not rescale so that global mean of output == input global mean
flag: --no-rescale
out_file: (a file name)
output volume. Output can be any format accepted by mri_convert. If
the output format is COR, then the directory must exist.
flag: --o %s
protocol_iterations: (an integer (int or long))
Passes Np as argument of the -iterations flag of nu_correct. This is
different than the --n flag above. Default is not to pass nu_correct
the -iterations flag.
flag: --proto-iters %d
shrink: (an integer (int or long))
Shrink parameter for finer sampling (default is 4)
flag: --shrink %d
stop: (a float)
Convergence threshold below which iteration stops (suggest 0.01 to
0.0001)
flag: --stop %f
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
transform: (an existing file name)
tal.xfm. Use mri_make_uchar instead of conforming
flag: --uchar %s
Outputs:
out_file: (a file name)
output volume
MRIConvert¶
Wraps command mri_convert
use fs mri_convert to manipulate files
Note
Adds niigz as an output type option
Examples¶
>>> mc = MRIConvert()
>>> mc.inputs.in_file = 'structural.nii'
>>> mc.inputs.out_file = 'outfile.mgz'
>>> mc.inputs.out_type = 'mgz'
>>> mc.cmdline
'mri_convert --out_type mgz --input_volume structural.nii --output_volume outfile.mgz'
Inputs:
[Mandatory]
in_file: (an existing file name)
File to read/convert
flag: --input_volume %s, position: -2
[Optional]
apply_inv_transform: (an existing file name)
apply inverse transformation xfm file
flag: --apply_inverse_transform %s
apply_transform: (an existing file name)
apply xfm file
flag: --apply_transform %s
args: (a unicode string)
Additional parameters to the command
flag: %s
ascii: (a boolean)
save output as ascii col>row>slice>frame
flag: --ascii
autoalign_matrix: (an existing file name)
text file with autoalign matrix
flag: --autoalign %s
color_file: (an existing file name)
color file
flag: --color_file %s
conform: (a boolean)
conform to 1mm voxel size in coronal slice direction with 256^3 or
more
flag: --conform
conform_min: (a boolean)
conform to smallest size
flag: --conform_min
conform_size: (a float)
conform to size_in_mm
flag: --conform_size %s
crop_center: (a tuple of the form: (an integer (int or long), an
integer (int or long), an integer (int or long)))
<x> <y> <z> crop to 256 around center (x, y, z)
flag: --crop %d %d %d
crop_gdf: (a boolean)
apply GDF cropping
flag: --crop_gdf
crop_size: (a tuple of the form: (an integer (int or long), an
integer (int or long), an integer (int or long)))
<dx> <dy> <dz> crop to size <dx, dy, dz>
flag: --cropsize %d %d %d
cut_ends: (an integer (int or long))
remove ncut slices from the ends
flag: --cutends %d
cw256: (a boolean)
confrom to dimensions of 256^3
flag: --cw256
devolve_transform: (a unicode string)
subject id
flag: --devolvexfm %s
drop_n: (an integer (int or long))
drop the last n frames
flag: --ndrop %d
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
fill_parcellation: (a boolean)
fill parcellation
flag: --fill_parcellation
force_ras: (a boolean)
use default when orientation info absent
flag: --force_ras_good
frame: (an integer (int or long))
keep only 0-based frame number
flag: --frame %d
frame_subsample: (a tuple of the form: (an integer (int or long), an
integer (int or long), an integer (int or long)))
start delta end : frame subsampling (end = -1 for end)
flag: --fsubsample %d %d %d
fwhm: (a float)
smooth input volume by fwhm mm
flag: --fwhm %f
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_center: (a list of at most 3 items which are a float)
<R coordinate> <A coordinate> <S coordinate>
flag: --in_center %s
in_i_dir: (a tuple of the form: (a float, a float, a float))
<R direction> <A direction> <S direction>
flag: --in_i_direction %f %f %f
in_i_size: (an integer (int or long))
input i size
flag: --in_i_size %d
in_info: (a boolean)
display input info
flag: --in_info
in_j_dir: (a tuple of the form: (a float, a float, a float))
<R direction> <A direction> <S direction>
flag: --in_j_direction %f %f %f
in_j_size: (an integer (int or long))
input j size
flag: --in_j_size %d
in_k_dir: (a tuple of the form: (a float, a float, a float))
<R direction> <A direction> <S direction>
flag: --in_k_direction %f %f %f
in_k_size: (an integer (int or long))
input k size
flag: --in_k_size %d
in_like: (an existing file name)
input looks like
flag: --in_like %s
in_matrix: (a boolean)
display input matrix
flag: --in_matrix
in_orientation: (u'LAI' or u'LIA' or u'ALI' or u'AIL' or u'ILA' or
u'IAL' or u'LAS' or u'LSA' or u'ALS' or u'ASL' or u'SLA' or u'SAL'
or u'LPI' or u'LIP' or u'PLI' or u'PIL' or u'ILP' or u'IPL' or
u'LPS' or u'LSP' or u'PLS' or u'PSL' or u'SLP' or u'SPL' or u'RAI'
or u'RIA' or u'ARI' or u'AIR' or u'IRA' or u'IAR' or u'RAS' or
u'RSA' or u'ARS' or u'ASR' or u'SRA' or u'SAR' or u'RPI' or u'RIP'
or u'PRI' or u'PIR' or u'IRP' or u'IPR' or u'RPS' or u'RSP' or
u'PRS' or u'PSR' or u'SRP' or u'SPR')
specify the input orientation
flag: --in_orientation %s
in_scale: (a float)
input intensity scale factor
flag: --scale %f
in_stats: (a boolean)
display input stats
flag: --in_stats
in_type: (u'cor' or u'mgh' or u'mgz' or u'minc' or u'analyze' or
u'analyze4d' or u'spm' or u'afni' or u'brik' or u'bshort' or
u'bfloat' or u'sdt' or u'outline' or u'otl' or u'gdf' or u'nifti1'
or u'nii' or u'niigz' or u'ge' or u'gelx' or u'lx' or u'ximg' or
u'siemens' or u'dicom' or u'siemens_dicom')
input file type
flag: --in_type %s
invert_contrast: (a float)
threshold for inversting contrast
flag: --invert_contrast %f
midframe: (a boolean)
keep only the middle frame
flag: --mid-frame
no_change: (a boolean)
don't change type of input to that of template
flag: --nochange
no_scale: (a boolean)
dont rescale values for COR
flag: --no_scale 1
no_translate: (a boolean)
~~~
flag: --no_translate
no_write: (a boolean)
do not write output
flag: --no_write
out_center: (a tuple of the form: (a float, a float, a float))
<R coordinate> <A coordinate> <S coordinate>
flag: --out_center %f %f %f
out_datatype: (u'uchar' or u'short' or u'int' or u'float')
output data type <uchar|short|int|float>
flag: --out_data_type %s
out_file: (a file name)
output filename or True to generate one
flag: --output_volume %s, position: -1
out_i_count: (an integer (int or long))
some count ?? in i direction
flag: --out_i_count %d
out_i_dir: (a tuple of the form: (a float, a float, a float))
<R direction> <A direction> <S direction>
flag: --out_i_direction %f %f %f
out_i_size: (an integer (int or long))
output i size
flag: --out_i_size %d
out_info: (a boolean)
display output info
flag: --out_info
out_j_count: (an integer (int or long))
some count ?? in j direction
flag: --out_j_count %d
out_j_dir: (a tuple of the form: (a float, a float, a float))
<R direction> <A direction> <S direction>
flag: --out_j_direction %f %f %f
out_j_size: (an integer (int or long))
output j size
flag: --out_j_size %d
out_k_count: (an integer (int or long))
some count ?? in k direction
flag: --out_k_count %d
out_k_dir: (a tuple of the form: (a float, a float, a float))
<R direction> <A direction> <S direction>
flag: --out_k_direction %f %f %f
out_k_size: (an integer (int or long))
output k size
flag: --out_k_size %d
out_matrix: (a boolean)
display output matrix
flag: --out_matrix
out_orientation: (u'LAI' or u'LIA' or u'ALI' or u'AIL' or u'ILA' or
u'IAL' or u'LAS' or u'LSA' or u'ALS' or u'ASL' or u'SLA' or u'SAL'
or u'LPI' or u'LIP' or u'PLI' or u'PIL' or u'ILP' or u'IPL' or
u'LPS' or u'LSP' or u'PLS' or u'PSL' or u'SLP' or u'SPL' or u'RAI'
or u'RIA' or u'ARI' or u'AIR' or u'IRA' or u'IAR' or u'RAS' or
u'RSA' or u'ARS' or u'ASR' or u'SRA' or u'SAR' or u'RPI' or u'RIP'
or u'PRI' or u'PIR' or u'IRP' or u'IPR' or u'RPS' or u'RSP' or
u'PRS' or u'PSR' or u'SRP' or u'SPR')
specify the output orientation
flag: --out_orientation %s
out_scale: (a float)
output intensity scale factor
flag: --out-scale %d
out_stats: (a boolean)
display output stats
flag: --out_stats
out_type: (u'cor' or u'mgh' or u'mgz' or u'minc' or u'analyze' or
u'analyze4d' or u'spm' or u'afni' or u'brik' or u'bshort' or
u'bfloat' or u'sdt' or u'outline' or u'otl' or u'gdf' or u'nifti1'
or u'nii' or u'niigz')
output file type
flag: --out_type %s
parse_only: (a boolean)
parse input only
flag: --parse_only
read_only: (a boolean)
read the input volume
flag: --read_only
reorder: (a tuple of the form: (an integer (int or long), an integer
(int or long), an integer (int or long)))
olddim1 olddim2 olddim3
flag: --reorder %d %d %d
resample_type: (u'interpolate' or u'weighted' or u'nearest' or
u'sinc' or u'cubic')
<interpolate|weighted|nearest|sinc|cubic> (default is interpolate)
flag: --resample_type %s
reslice_like: (an existing file name)
reslice output to match file
flag: --reslice_like %s
sdcm_list: (an existing file name)
list of DICOM files for conversion
flag: --sdcmlist %s
skip_n: (an integer (int or long))
skip the first n frames
flag: --nskip %d
slice_bias: (a float)
apply half-cosine bias field
flag: --slice-bias %f
slice_crop: (a tuple of the form: (an integer (int or long), an
integer (int or long)))
s_start s_end : keep slices s_start to s_end
flag: --slice-crop %d %d
slice_reverse: (a boolean)
reverse order of slices, update vox2ras
flag: --slice-reverse
smooth_parcellation: (a boolean)
smooth parcellation
flag: --smooth_parcellation
sphinx: (a boolean)
change orientation info to sphinx
flag: --sphinx
split: (a boolean)
split output frames into separate output files.
flag: --split
status_file: (a file name)
status file for DICOM conversion
flag: --status %s
subject_name: (a unicode string)
subject name ???
flag: --subject_name %s
subjects_dir: (an existing directory name)
subjects directory
te: (an integer (int or long))
TE in msec
flag: -te %d
template_info: (a boolean)
dump info about template
template_type: (u'cor' or u'mgh' or u'mgz' or u'minc' or u'analyze'
or u'analyze4d' or u'spm' or u'afni' or u'brik' or u'bshort' or
u'bfloat' or u'sdt' or u'outline' or u'otl' or u'gdf' or u'nifti1'
or u'nii' or u'niigz' or u'ge' or u'gelx' or u'lx' or u'ximg' or
u'siemens' or u'dicom' or u'siemens_dicom')
template file type
flag: --template_type %s
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
ti: (an integer (int or long))
TI in msec (note upper case flag)
flag: -ti %d
tr: (an integer (int or long))
TR in msec
flag: -tr %d
unwarp_gradient: (a boolean)
unwarp gradient nonlinearity
flag: --unwarp_gradient_nonlinearity
vox_size: (a tuple of the form: (a float, a float, a float))
<size_x> <size_y> <size_z> specify the size (mm) - useful for
upsampling or downsampling
flag: -voxsize %f %f %f
zero_ge_z_offset: (a boolean)
zero ge z offset ???
flag: --zero_ge_z_offset
zero_outlines: (a boolean)
zero outlines
flag: --zero_outlines
Outputs:
out_file: (a list of items which are an existing file name)
converted output file
MRIsCALabel¶
Wraps command mris_ca_label
For a single subject, produces an annotation file, in which each cortical surface vertex is assigned a neuroanatomical label.This automatic procedure employs data from a previously-prepared atlas file. An atlas file is created from a training set, capturing region data manually drawn by neuroanatomists combined with statistics on variability correlated to geometric information derived from the cortical model (sulcus and curvature). Besides the atlases provided with FreeSurfer, new ones can be prepared using mris_ca_train).
Examples¶
>>> from nipype.interfaces import freesurfer
>>> ca_label = freesurfer.MRIsCALabel()
>>> ca_label.inputs.subject_id = "test"
>>> ca_label.inputs.hemisphere = "lh"
>>> ca_label.inputs.canonsurf = "lh.pial"
>>> ca_label.inputs.curv = "lh.pial"
>>> ca_label.inputs.sulc = "lh.pial"
>>> ca_label.inputs.classifier = "im1.nii" # in pracice, use .gcs extension
>>> ca_label.inputs.smoothwm = "lh.pial"
>>> ca_label.cmdline
'mris_ca_label test lh lh.pial im1.nii lh.aparc.annot'
Inputs:
[Mandatory]
canonsurf: (an existing file name)
Input canonical surface file
flag: %s, position: -3
classifier: (an existing file name)
Classifier array input file
flag: %s, position: -2
curv: (an existing file name)
implicit input {hemisphere}.curv
hemisphere: (u'lh' or u'rh')
Hemisphere ('lh' or 'rh')
flag: %s, position: -4
smoothwm: (an existing file name)
implicit input {hemisphere}.smoothwm
subject_id: (a string, nipype default value: subject_id)
Subject name or ID
flag: %s, position: -5
sulc: (an existing file name)
implicit input {hemisphere}.sulc
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
aseg: (a file name)
Undocumented flag. Autorecon3 uses ../mri/aseg.presurf.mgz as input
file
flag: -aseg %s
copy_inputs: (a boolean)
Copies implicit inputs to node directory and creates a temp
subjects_directory. Use this when running as a node
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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: (a file name)
Undocumented flag. Autorecon3 uses
../label/{hemisphere}.cortex.label as input file
flag: -l %s
num_threads: (an integer (int or long))
allows for specifying more threads
out_file: (a file name)
Annotated surface output file
flag: %s, position: -1
seed: (an integer (int or long))
flag: -seed %d
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
out_file: (a file name)
Output volume from MRIsCALabel
Normalize¶
Wraps command mri_normalize
Normalize the white-matter, optionally based on control points. The input volume is converted into a new volume where white matter image values all range around 110.
Examples¶
>>> from nipype.interfaces import freesurfer
>>> normalize = freesurfer.Normalize()
>>> normalize.inputs.in_file = "T1.mgz"
>>> normalize.inputs.gradient = 1
>>> normalize.cmdline
'mri_normalize -g 1 T1.mgz T1_norm.mgz'
Inputs:
[Mandatory]
in_file: (an existing file name)
The input file for Normalize
flag: %s, position: -2
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
gradient: (an integer (int or long))
use max intensity/mm gradient g (default=1)
flag: -g %d
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: (an existing file name)
The input mask file for Normalize
flag: -mask %s
out_file: (a file name)
The output file for Normalize
flag: %s, position: -1
segmentation: (an existing file name)
The input segmentation for Normalize
flag: -aseg %s
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
transform: (an existing file name)
Tranform file from the header of the input file
Outputs:
out_file: (a file name)
The output file for Normalize
ParseDICOMDir¶
Wraps command mri_parse_sdcmdir
Uses mri_parse_sdcmdir to get information from dicom directories
Examples¶
>>> from nipype.interfaces.freesurfer import ParseDICOMDir
>>> dcminfo = ParseDICOMDir()
>>> dcminfo.inputs.dicom_dir = '.'
>>> dcminfo.inputs.sortbyrun = True
>>> dcminfo.inputs.summarize = True
>>> dcminfo.cmdline
'mri_parse_sdcmdir --d . --o dicominfo.txt --sortbyrun --summarize'
Inputs:
[Mandatory]
dicom_dir: (an existing directory name)
path to siemens dicom directory
flag: --d %s
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
dicom_info_file: (a file name, nipype default value: dicominfo.txt)
file to which results are written
flag: --o %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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
sortbyrun: (a boolean)
assign run numbers
flag: --sortbyrun
subjects_dir: (an existing directory name)
subjects directory
summarize: (a boolean)
only print out info for run leaders
flag: --summarize
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
dicom_info_file: (an existing file name)
text file containing dicom information
ReconAll¶
Wraps command recon-all
Uses recon-all to generate surfaces and parcellations of structural data from anatomical images of a subject.
Examples¶
>>> from nipype.interfaces.freesurfer import ReconAll
>>> reconall = ReconAll()
>>> reconall.inputs.subject_id = 'foo'
>>> reconall.inputs.directive = 'all'
>>> reconall.inputs.subjects_dir = '.'
>>> reconall.inputs.T1_files = 'structural.nii'
>>> reconall.cmdline
'recon-all -all -i structural.nii -subjid foo -sd .'
Inputs:
[Mandatory]
[Optional]
T1_files: (a list of items which are an existing file name)
name of T1 file to process
flag: -i %s...
T2_file: (an existing file name)
Convert T2 image to orig directory
flag: -T2 %s
args: (a unicode string)
Additional parameters to the command
flag: %s
directive: (u'all' or u'autorecon1' or u'autorecon2' or
u'autorecon2-cp' or u'autorecon2-wm' or u'autorecon2-inflate1' or
u'autorecon2-perhemi' or u'autorecon3' or u'localGI' or u'qcache',
nipype default value: all)
process directive
flag: -%s, position: 0
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
flags: (a unicode string)
additional parameters
flag: %s
hemi: (u'lh' or u'rh')
hemisphere to process
flag: -hemi %s
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
openmp: (an integer (int or long))
Number of processors to use in parallel
flag: -openmp %d
subject_id: (a unicode string, nipype default value: recon_all)
subject name
flag: -subjid %s
subjects_dir: (an existing directory name)
path to subjects directory
flag: -sd %s
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
use_T2: (a boolean)
Use converted T2 to refine the cortical surface
flag: -T2pial
Outputs:
BA_stats: (a list of items which are an existing file name)
Brodmann Area statistics files
T1: (an existing file name)
Intensity normalized whole-head volume
annot: (a list of items which are an existing file name)
Surface annotation files
aparc_a2009s_stats: (a list of items which are an existing file name)
Aparc a2009s parcellation statistics files
aparc_aseg: (a list of items which are an existing file name)
Aparc parcellation projected into aseg volume
aparc_stats: (a list of items which are an existing file name)
Aparc parcellation statistics files
aseg: (an existing file name)
Volumetric map of regions from automatic segmentation
aseg_stats: (a list of items which are an existing file name)
Automated segmentation statistics file
brain: (an existing file name)
Intensity normalized brain-only volume
brainmask: (an existing file name)
Skull-stripped (brain-only) volume
curv: (a list of items which are an existing file name)
Maps of surface curvature
curv_stats: (a list of items which are an existing file name)
Curvature statistics files
entorhinal_exvivo_stats: (a list of items which are an existing file
name)
Entorhinal exvivo statistics files
filled: (an existing file name)
Subcortical mass volume
inflated: (a list of items which are an existing file name)
Inflated surface meshes
label: (a list of items which are an existing file name)
Volume and surface label files
norm: (an existing file name)
Normalized skull-stripped volume
nu: (an existing file name)
Non-uniformity corrected whole-head volume
orig: (an existing file name)
Base image conformed to Freesurfer space
pial: (a list of items which are an existing file name)
Gray matter/pia mater surface meshes
rawavg: (an existing file name)
Volume formed by averaging input images
ribbon: (a list of items which are an existing file name)
Volumetric maps of cortical ribbons
smoothwm: (a list of items which are an existing file name)
Smoothed original surface meshes
sphere: (a list of items which are an existing file name)
Spherical surface meshes
sphere_reg: (a list of items which are an existing file name)
Spherical registration file
subject_id: (a unicode string)
Subject name for whom to retrieve data
subjects_dir: (an existing directory name)
Freesurfer subjects directory.
sulc: (a list of items which are an existing file name)
Surface maps of sulcal depth
thickness: (a list of items which are an existing file name)
Surface maps of cortical thickness
volume: (a list of items which are an existing file name)
Surface maps of cortical volume
white: (a list of items which are an existing file name)
White/gray matter surface meshes
wm: (an existing file name)
Segmented white-matter volume
wmparc: (an existing file name)
Aparc parcellation projected into subcortical white matter
wmparc_stats: (a list of items which are an existing file name)
White matter parcellation statistics file
Resample¶
Wraps command mri_convert
Use FreeSurfer mri_convert to up or down-sample image files
Examples¶
>>> from nipype.interfaces import freesurfer
>>> resampler = freesurfer.Resample()
>>> resampler.inputs.in_file = 'structural.nii'
>>> resampler.inputs.resampled_file = 'resampled.nii'
>>> resampler.inputs.voxel_size = (2.1, 2.1, 2.1)
>>> resampler.cmdline
'mri_convert -vs 2.10 2.10 2.10 -i structural.nii -o resampled.nii'
Inputs:
[Mandatory]
in_file: (an existing file name)
file to resample
flag: -i %s, position: -2
voxel_size: (a tuple of the form: (a float, a float, a float))
triplet of output voxel sizes
flag: -vs %.2f %.2f %.2f
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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
resampled_file: (a file name)
output filename
flag: -o %s, position: -1
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
resampled_file: (an existing file name)
output filename
RobustRegister¶
Wraps command mri_robust_register
Perform intramodal linear registration (translation and rotation) using robust statistics.
Examples¶
>>> from nipype.interfaces.freesurfer import RobustRegister
>>> reg = RobustRegister()
>>> reg.inputs.source_file = 'structural.nii'
>>> reg.inputs.target_file = 'T1.nii'
>>> reg.inputs.auto_sens = True
>>> reg.inputs.init_orient = True
>>> reg.cmdline
'mri_robust_register --satit --initorient --lta structural_robustreg.lta --mov structural.nii --dst T1.nii'
References¶
Reuter, M, Rosas, HD, and Fischl, B, (2010). Highly Accurate Inverse Consistent Registration: A Robust Approach. Neuroimage 53(4) 1181-96.
Inputs:
[Mandatory]
auto_sens: (a boolean)
auto-detect good sensitivity
flag: --satit
mutually_exclusive: outlier_sens
outlier_sens: (a float)
set outlier sensitivity explicitly
flag: --sat %.4f
mutually_exclusive: auto_sens
source_file: (a file name)
volume to be registered
flag: --mov %s
target_file: (a file name)
target volume for the registration
flag: --dst %s
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
est_int_scale: (a boolean)
estimate intensity scale (recommended for unnormalized images)
flag: --iscale
force_double: (a boolean)
use double-precision intensities
flag: --doubleprec
force_float: (a boolean)
use float intensities
flag: --floattype
half_source: (a boolean or a file name)
write source volume mapped to halfway space
flag: --halfmov %s
half_source_xfm: (a boolean or a file name)
write transform from source to halfway space
flag: --halfmovlta %s
half_targ: (a boolean or a file name)
write target volume mapped to halfway space
flag: --halfdst %s
half_targ_xfm: (a boolean or a file name)
write transform from target to halfway space
flag: --halfdstlta %s
half_weights: (a boolean or a file name)
write weights volume mapped to halfway space
flag: --halfweights %s
high_iterations: (an integer (int or long))
max # of times on highest resolution
flag: --highit %d
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_xfm_file: (an existing file name)
use initial transform on source
flag: --transform
init_orient: (a boolean)
use moments for initial orient (recommended for stripped brains)
flag: --initorient
iteration_thresh: (a float)
stop iterations when below threshold
flag: --epsit %.3f
least_squares: (a boolean)
use least squares instead of robust estimator
flag: --leastsquares
mask_source: (an existing file name)
image to mask source volume with
flag: --maskmov %s
mask_target: (an existing file name)
image to mask target volume with
flag: --maskdst %s
max_iterations: (an integer (int or long))
maximum # of times on each resolution
flag: --maxit %d
no_init: (a boolean)
skip transform init
flag: --noinit
no_multi: (a boolean)
work on highest resolution
flag: --nomulti
out_reg_file: (a file name)
registration file to write
flag: --lta %s
outlier_limit: (a float)
set maximal outlier limit in satit
flag: --wlimit %.3f
registered_file: (a boolean or a file name)
registered image; either True or filename
flag: --warp %s
subjects_dir: (an existing directory name)
subjects directory
subsample_thresh: (an integer (int or long))
subsample if dimension is above threshold size
flag: --subsample %d
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
trans_only: (a boolean)
find 3 parameter translation only
flag: --transonly
weights_file: (a boolean or a file name)
weights image to write; either True or filename
flag: --weights %s
write_vo2vox: (a boolean)
output vox2vox matrix (default is RAS2RAS)
flag: --vox2vox
Outputs:
half_source: (a file name)
source image mapped to halfway space
half_source_xfm: (a file name)
transform file to map source image to halfway space
half_targ: (a file name)
target image mapped to halfway space
half_targ_xfm: (a file name)
transform file to map target image to halfway space
half_weights: (a file name)
weights image mapped to halfway space
out_reg_file: (an existing file name)
output registration file
registered_file: (a file name)
output image with registration applied
weights_file: (a file name)
image of weights used
SegmentCC¶
Wraps command mri_cc
This program segments the corpus callosum into five separate labels in the subcortical segmentation volume ‘aseg.mgz’. The divisions of the cc are equally spaced in terms of distance along the primary eigendirection (pretty much the long axis) of the cc. The lateral extent can be changed with the -T <thickness> parameter, where <thickness> is the distance off the midline (so -T 1 would result in the who CC being 3mm thick). The default is 2 so it’s 5mm thick. The aseg.stats values should be volume.
Examples¶
>>> from nipype.interfaces import freesurfer
>>> SegmentCC_node = freesurfer.SegmentCC()
>>> SegmentCC_node.inputs.in_file = "aseg.mgz"
>>> SegmentCC_node.inputs.in_norm = "norm.mgz"
>>> SegmentCC_node.inputs.out_rotation = "cc.lta"
>>> SegmentCC_node.inputs.subject_id = "test"
>>> SegmentCC_node.cmdline
'mri_cc -aseg aseg.mgz -o aseg.auto.mgz -lta cc.lta test'
Inputs:
[Mandatory]
in_file: (an existing file name)
Input aseg file to read from subjects directory
flag: -aseg %s
in_norm: (an existing file name)
Required undocumented input {subject}/mri/norm.mgz
out_rotation: (a file name)
Global filepath for writing rotation lta
flag: -lta %s
subject_id: (a string, nipype default value: subject_id)
Subject name
flag: %s, position: -1
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
copy_inputs: (a boolean)
If running as a node, set this to True.This will copy the input
files to the node directory.
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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)
Filename to write aseg including CC
flag: -o %s
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
out_file: (a file name)
Output segmentation uncluding corpus collosum
out_rotation: (a file name)
Output lta rotation file
SegmentWM¶
Wraps command mri_segment
This program segments white matter from the input volume. The input volume should be normalized such that white matter voxels are ~110-valued, and the volume is conformed to 256^3.
Examples¶
>>> from nipype.interfaces import freesurfer
>>> SegmentWM_node = freesurfer.SegmentWM()
>>> SegmentWM_node.inputs.in_file = "norm.mgz"
>>> SegmentWM_node.inputs.out_file = "wm.seg.mgz"
>>> SegmentWM_node.cmdline
'mri_segment norm.mgz wm.seg.mgz'
Inputs:
[Mandatory]
in_file: (an existing file name)
Input file for SegmentWM
flag: %s, position: -2
out_file: (a file name)
File to be written as output for SegmentWM
flag: %s, position: -1
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
out_file: (a file name)
Output white matter segmentation
Smooth¶
Wraps command mris_volsmooth
Use FreeSurfer mris_volsmooth to smooth a volume
This function smoothes cortical regions on a surface and non-cortical regions in volume.
Note
Cortical voxels are mapped to the surface (3D->2D) and then the smoothed values from the surface are put back into the volume to fill the cortical ribbon. If data is smoothed with this algorithm, one has to be careful about how further processing is interpreted.
Examples¶
>>> from nipype.interfaces.freesurfer import Smooth
>>> smoothvol = Smooth(in_file='functional.nii', smoothed_file = 'foo_out.nii', reg_file='register.dat', surface_fwhm=10, vol_fwhm=6)
>>> smoothvol.cmdline
'mris_volsmooth --i functional.nii --reg register.dat --o foo_out.nii --fwhm 10.000000 --vol-fwhm 6.000000'
Inputs:
[Mandatory]
in_file: (an existing file name)
source volume
flag: --i %s
num_iters: (an integer >= 1)
number of iterations instead of fwhm
flag: --niters %d
mutually_exclusive: surface_fwhm
reg_file: (an existing file name)
registers volume to surface anatomical
flag: --reg %s
surface_fwhm: (a floating point number >= 0.0)
surface FWHM in mm
flag: --fwhm %f
mutually_exclusive: num_iters
requires: reg_file
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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
proj_frac: (a float)
project frac of thickness a long surface normal
flag: --projfrac %s
mutually_exclusive: proj_frac_avg
proj_frac_avg: (a tuple of the form: (a float, a float, a float))
average a long normal min max delta
flag: --projfrac-avg %.2f %.2f %.2f
mutually_exclusive: proj_frac
smoothed_file: (a file name)
output volume
flag: --o %s
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
vol_fwhm: (a floating point number >= 0.0)
volume smoothing outside of surface
flag: --vol-fwhm %f
Outputs:
smoothed_file: (an existing file name)
smoothed input volume
SynthesizeFLASH¶
Wraps command mri_synthesize
Synthesize a FLASH acquisition from T1 and proton density maps.
Examples¶
>>> from nipype.interfaces.freesurfer import SynthesizeFLASH
>>> syn = SynthesizeFLASH(tr=20, te=3, flip_angle=30)
>>> syn.inputs.t1_image = 'T1.mgz'
>>> syn.inputs.pd_image = 'PD.mgz'
>>> syn.inputs.out_file = 'flash_30syn.mgz'
>>> syn.cmdline
'mri_synthesize 20.00 30.00 3.000 T1.mgz PD.mgz flash_30syn.mgz'
Inputs:
[Mandatory]
flip_angle: (a float)
flip angle (in degrees)
flag: %.2f, position: 3
pd_image: (an existing file name)
image of proton density values
flag: %s, position: 6
t1_image: (an existing file name)
image of T1 values
flag: %s, position: 5
te: (a float)
echo time (in msec)
flag: %.3f, position: 4
tr: (a float)
repetition time (in msec)
flag: %.2f, position: 2
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, nipype default value: {})
Environment variables
fixed_weighting: (a boolean)
use a fixed weighting to generate optimal gray/white contrast
flag: -w, position: 1
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)
image to write
flag: %s
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
out_file: (an existing file name)
synthesized FLASH acquisition
UnpackSDICOMDir¶
Wraps command unpacksdcmdir
Use unpacksdcmdir to convert dicom files
Call unpacksdcmdir -help from the command line to see more information on using this command.
Examples¶
>>> from nipype.interfaces.freesurfer import UnpackSDICOMDir
>>> unpack = UnpackSDICOMDir()
>>> unpack.inputs.source_dir = '.'
>>> unpack.inputs.output_dir = '.'
>>> unpack.inputs.run_info = (5, 'mprage', 'nii', 'struct')
>>> unpack.inputs.dir_structure = 'generic'
>>> unpack.cmdline
'unpacksdcmdir -generic -targ . -run 5 mprage nii struct -src .'
Inputs:
[Mandatory]
config: (an existing file name)
specify unpacking rules in file
flag: -cfg %s
mutually_exclusive: run_info, config, seq_config
run_info: (a tuple of the form: (an integer (int or long), a unicode
string, a unicode string, a unicode string))
runno subdir format name : spec unpacking rules on cmdline
flag: -run %d %s %s %s
mutually_exclusive: run_info, config, seq_config
seq_config: (an existing file name)
specify unpacking rules based on sequence
flag: -seqcfg %s
mutually_exclusive: run_info, config, seq_config
source_dir: (an existing directory name)
directory with the DICOM files
flag: -src %s
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
dir_structure: (u'fsfast' or u'generic')
unpack to specified directory structures
flag: -%s
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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
log_file: (an existing file name)
explicilty set log file
flag: -log %s
no_info_dump: (a boolean)
do not create infodump file
flag: -noinfodump
no_unpack_err: (a boolean)
do not try to unpack runs with errors
flag: -no-unpackerr
output_dir: (a directory name)
top directory into which the files will be unpacked
flag: -targ %s
scan_only: (an existing file name)
only scan the directory and put result in file
flag: -scanonly %s
spm_zeropad: (an integer (int or long))
set frame number zero padding width for SPM
flag: -nspmzeropad %d
subjects_dir: (an existing directory name)
subjects directory
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
Outputs:
None
WatershedSkullStrip¶
Wraps command mri_watershed
This program strips skull and other outer non-brain tissue and produces the brain volume from T1 volume or the scanned volume.
The “watershed” segmentation algorithm was used to dertermine the intensity values for white matter, grey matter, and CSF. A force field was then used to fit a spherical surface to the brain. The shape of the surface fit was then evaluated against a previously derived template.
The default parameters are: -w 0.82 -b 0.32 -h 10 -seedpt -ta -wta
(Segonne 2004)
Examples¶
>>> from nipype.interfaces.freesurfer import WatershedSkullStrip
>>> skullstrip = WatershedSkullStrip()
>>> skullstrip.inputs.in_file = "T1.mgz"
>>> skullstrip.inputs.t1 = True
>>> skullstrip.inputs.transform = "transforms/talairach_with_skull.lta"
>>> skullstrip.inputs.out_file = "brainmask.auto.mgz"
>>> skullstrip.cmdline
'mri_watershed -T1 transforms/talairach_with_skull.lta T1.mgz brainmask.auto.mgz'
Inputs:
[Mandatory]
in_file: (an existing file name)
input volume
flag: %s, position: -2
out_file: (a file name, nipype default value: brainmask.auto.mgz)
output volume
flag: %s, position: -1
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
brain_atlas: (an existing file name)
flag: -brain_atlas %s, position: -4
environ: (a dictionary with keys which are a newbytes or None or a
newstr or None and with values which are a newbytes or None or a
newstr or None, 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
subjects_dir: (an existing directory name)
subjects directory
t1: (a boolean)
specify T1 input volume (T1 grey value = 110)
flag: -T1
terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
Control terminal output: `stream` - displays to terminal immediately
(default), `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
transform: (a file name)
undocumented
flag: %s, position: -3
Outputs:
out_file: (a file name)
skull stripped brain volume