interfaces.freesurfer.preprocess

ApplyVolTransform

Link to code

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, lta_file, lta_inv_file, fsl_reg_file,
         xfm_reg_file, reg_header, mni_152_reg, subject
lta_file: (an existing file name)
        Linear Transform Array file
        flag: --lta %s
        mutually_exclusive: reg_file, lta_file, lta_inv_file, fsl_reg_file,
         xfm_reg_file, reg_header, mni_152_reg, subject
lta_inv_file: (an existing file name)
        LTA, invert
        flag: --lta-inv %s
        mutually_exclusive: reg_file, lta_file, lta_inv_file, fsl_reg_file,
         xfm_reg_file, reg_header, mni_152_reg, subject
mni_152_reg: (a boolean)
        target MNI152 space
        flag: --regheader
        mutually_exclusive: reg_file, lta_file, lta_inv_file, fsl_reg_file,
         xfm_reg_file, reg_header, mni_152_reg, subject
reg_file: (an existing file name)
        tkRAS-to-tkRAS matrix (tkregister2 format)
        flag: --reg %s
        mutually_exclusive: reg_file, lta_file, lta_inv_file, fsl_reg_file,
         xfm_reg_file, reg_header, mni_152_reg, subject
reg_header: (a boolean)
        ScannerRAS-to-ScannerRAS matrix = identity
        flag: --regheader
        mutually_exclusive: reg_file, lta_file, lta_inv_file, fsl_reg_file,
         xfm_reg_file, reg_header, mni_152_reg, 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, lta_file, lta_inv_file, fsl_reg_file,
         xfm_reg_file, reg_header, mni_152_reg, 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, lta_file, lta_inv_file, fsl_reg_file,
         xfm_reg_file, reg_header, mni_152_reg, subject

[Optional]
args: (a unicode string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
interp: ('trilin' or 'nearest' or '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
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

Link to code

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. 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: ('t1' or 't2' or 'bold' or 'dti')
        contrast type of image
        flag: --%s
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
dof: (6 or 9 or 12)
        number of transform degrees of freedom
        flag: --%d
environ: (a dictionary with keys which are a bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
epi_mask: (a boolean)
        mask out B0 regions in stages 1 and 2
        flag: --epi-mask
fsldof: (an integer (int or long))
        degrees of freedom for initial registration (FSL)
        flag: --fsl-dof %d
init: ('coreg' or 'rr' or 'spm' or 'fsl' or 'header' or 'best')
        initialize registration with mri_coreg, spm, fsl, or header
        flag: --init-%s
        mutually_exclusive: init_reg_file
init_cost_file: (a boolean or a file name)
        output initial registration cost file
        flag: --initcost %s
init_reg_file: (an existing file name)
        existing registration file
        flag: --init-reg %s
        mutually_exclusive: init
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_lta_file: (a boolean or a file name)
        write the transformation matrix in LTA format
        flag: --lta %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

Outputs:

init_cost_file: (an existing file name)
        Output initial registration cost file
min_cost_file: (an existing file name)
        Output registration minimum cost file
out_fsl_file: (an existing file name)
        Output FLIRT-style registration file
out_lta_file: (an existing file name)
        Output LTA-style registration file
out_reg_file: (an existing file name)
        Output registration file
registered_file: (an existing file name)
        Registered and resampled source file

CALabel

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
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

Outputs:

out_file: (a file name)
        Output volume from CALabel

CANormalize

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
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

Outputs:

control_points: (a file name)
        The output control points for Normalize
out_file: (a file name)
        The output file for Normalize

CARegister

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
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
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

Link to code

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 = 'lta1.lta'
>>> conc_lta.inputs.in_lta2 = 'lta2.lta'
>>> conc_lta.cmdline
'mri_concatenate_lta lta1.lta lta2.lta lta1_concat.lta'

You can use ‘identity.nofile’ as the filename for in_lta2, e.g.:

>>> conc_lta.inputs.in_lta2 = 'identity.nofile'
>>> conc_lta.inputs.invert_1 = True
>>> conc_lta.inputs.out_file = 'inv1.lta'
>>> conc_lta.cmdline
'mri_concatenate_lta -invert1 lta1.lta identity.nofile inv1.lta'

To create a RAS2RAS transform:

>>> conc_lta.inputs.out_type = 'RAS2RAS'
>>> conc_lta.cmdline
'mri_concatenate_lta -invert1 -out_type 1 lta1.lta identity.nofile inv1.lta'

Inputs:

[Mandatory]
in_lta1: (an existing file name)
        maps some src1 to dst1
        flag: %s, position: -3
in_lta2: (an existing file name or 'identity.nofile')
        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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
invert_1: (a boolean)
        invert in_lta1 before applying it
        flag: -invert1
invert_2: (a boolean)
        invert in_lta2 before applying it
        flag: -invert2
invert_out: (a boolean)
        invert output LTA
        flag: -invertout
out_file: (a file name)
        the combined LTA maps: src1 to dst2 = LTA2*LTA1
        flag: %s, position: -1
out_type: ('VOX2VOX' or 'RAS2RAS')
        set final LTA type
        flag: -out_type %d
subject: (a unicode string)
        set subject in output LTA
        flag: -subject %s
subjects_dir: (an existing directory name)
        subjects directory
tal_source_file: (a file name)
        if in_lta2 is talairach.xfm, specify source for talairach
        flag: -tal %s, position: -5
        requires: tal_template_file
tal_template_file: (a file name)
        if in_lta2 is talairach.xfm, specify template for talairach
        flag: %s, position: -4
        requires: tal_source_file

Outputs:

out_file: (a file name)
        the combined LTA maps: src1 to dst2 = LTA2*LTA1

DICOMConvert

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', 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_single_slice: (a boolean)
        ignore volumes containing a single slice
        requires: dicom_info
out_type: ('cor' or 'mgh' or 'mgz' or 'minc' or 'analyze' or
         'analyze4d' or 'spm' or 'afni' or 'brik' or 'bshort' or 'bfloat' or
         'sdt' or 'outline' or 'otl' or 'gdf' or 'nifti1' or 'nii' or
         '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

Outputs:

None

EditWMwithAseg

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
keep_in: (a boolean)
        Keep edits as found in input volume
        flag: -keep-in
subjects_dir: (an existing directory name)
        subjects directory

Outputs:

out_file: (a file name)
        Output edited WM file

FitMSParams

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', 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
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)
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

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
iterations: (an integer (int or long), nipype default value: 4)
        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
transform: (an existing file name)
        tal.xfm. Use mri_make_uchar instead of conforming
        flag: --uchar %s

Outputs:

out_file: (an existing file name)
        output volume

MRIConvert

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', 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
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: ('LAI' or 'LIA' or 'ALI' or 'AIL' or 'ILA' or 'IAL'
         or 'LAS' or 'LSA' or 'ALS' or 'ASL' or 'SLA' or 'SAL' or 'LPI' or
         'LIP' or 'PLI' or 'PIL' or 'ILP' or 'IPL' or 'LPS' or 'LSP' or
         'PLS' or 'PSL' or 'SLP' or 'SPL' or 'RAI' or 'RIA' or 'ARI' or
         'AIR' or 'IRA' or 'IAR' or 'RAS' or 'RSA' or 'ARS' or 'ASR' or
         'SRA' or 'SAR' or 'RPI' or 'RIP' or 'PRI' or 'PIR' or 'IRP' or
         'IPR' or 'RPS' or 'RSP' or 'PRS' or 'PSR' or 'SRP' or '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: ('cor' or 'mgh' or 'mgz' or 'minc' or 'analyze' or
         'analyze4d' or 'spm' or 'afni' or 'brik' or 'bshort' or 'bfloat' or
         'sdt' or 'outline' or 'otl' or 'gdf' or 'nifti1' or 'nii' or
         'niigz' or 'ge' or 'gelx' or 'lx' or 'ximg' or 'siemens' or 'dicom'
         or '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: ('uchar' or 'short' or 'int' or '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: ('LAI' or 'LIA' or 'ALI' or 'AIL' or 'ILA' or 'IAL'
         or 'LAS' or 'LSA' or 'ALS' or 'ASL' or 'SLA' or 'SAL' or 'LPI' or
         'LIP' or 'PLI' or 'PIL' or 'ILP' or 'IPL' or 'LPS' or 'LSP' or
         'PLS' or 'PSL' or 'SLP' or 'SPL' or 'RAI' or 'RIA' or 'ARI' or
         'AIR' or 'IRA' or 'IAR' or 'RAS' or 'RSA' or 'ARS' or 'ASR' or
         'SRA' or 'SAR' or 'RPI' or 'RIP' or 'PRI' or 'PIR' or 'IRP' or
         'IPR' or 'RPS' or 'RSP' or 'PRS' or 'PSR' or 'SRP' or '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: ('cor' or 'mgh' or 'mgz' or 'minc' or 'analyze' or
         'analyze4d' or 'spm' or 'afni' or 'brik' or 'bshort' or 'bfloat' or
         'sdt' or 'outline' or 'otl' or 'gdf' or 'nifti1' or 'nii' or
         '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: ('interpolate' or 'weighted' or 'nearest' or 'sinc' or
         '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
        flag: --template_info
template_type: ('cor' or 'mgh' or 'mgz' or 'minc' or 'analyze' or
         'analyze4d' or 'spm' or 'afni' or 'brik' or 'bshort' or 'bfloat' or
         'sdt' or 'outline' or 'otl' or 'gdf' or 'nifti1' or 'nii' or
         'niigz' or 'ge' or 'gelx' or 'lx' or 'ximg' or 'siemens' or 'dicom'
         or 'siemens_dicom')
        template file type
        flag: --template_type %s
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

Link to code

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: ('lh' or '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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
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

Outputs:

out_file: (a file name)
        Output volume from MRIsCALabel

Normalize

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
gradient: (an integer (int or long))
        use max intensity/mm gradient g (default=1)
        flag: -g %d
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
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

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
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

Outputs:

dicom_info_file: (an existing file name)
        text file containing dicom information

ReconAll

Link to code

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 .'
>>> reconall.inputs.flags = "-qcache"
>>> reconall.cmdline
'recon-all -all -i structural.nii -qcache -subjid foo -sd .'
>>> reconall.inputs.flags = ["-cw256", "-qcache"]
>>> reconall.cmdline
'recon-all -all -i structural.nii -cw256 -qcache -subjid foo -sd .'

Hemisphere may be specified regardless of directive:

>>> reconall.inputs.flags = []
>>> reconall.inputs.hemi = 'lh'
>>> reconall.cmdline
'recon-all -all -i structural.nii -hemi lh -subjid foo -sd .'

-autorecon-hemi uses the -hemi input to specify the hemisphere to operate upon:

>>> reconall.inputs.directive = 'autorecon-hemi'
>>> reconall.cmdline
'recon-all -autorecon-hemi lh -i structural.nii -subjid foo -sd .'

Hippocampal subfields can accept T1 and T2 images:

>>> reconall_subfields = ReconAll()
>>> reconall_subfields.inputs.subject_id = 'foo'
>>> reconall_subfields.inputs.directive = 'all'
>>> reconall_subfields.inputs.subjects_dir = '.'
>>> reconall_subfields.inputs.T1_files = 'structural.nii'
>>> reconall_subfields.inputs.hippocampal_subfields_T1 = True
>>> reconall_subfields.cmdline
'recon-all -all -i structural.nii -hippocampal-subfields-T1 -subjid foo -sd .'
>>> reconall_subfields.inputs.hippocampal_subfields_T2 = (
... 'structural.nii', 'test')
>>> reconall_subfields.cmdline
'recon-all -all -i structural.nii -hippocampal-subfields-T1T2 structural.nii test -subjid foo -sd .'
>>> reconall_subfields.inputs.hippocampal_subfields_T1 = False
>>> reconall_subfields.cmdline
'recon-all -all -i structural.nii -hippocampal-subfields-T2 structural.nii test -subjid foo -sd .'

Inputs:

[Mandatory]

[Optional]
FLAIR_file: (an existing file name)
        Convert FLAIR image to orig directory
        flag: -FLAIR %s
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
big_ventricles: (a boolean)
        For use in subjects with enlarged ventricles
        flag: -bigventricles
brainstem: (a boolean)
        Segment brainstem structures
        flag: -brainstem-structures
directive: ('all' or 'autorecon1' or 'autorecon2' or
         'autorecon2-volonly' or 'autorecon2-perhemi' or
         'autorecon2-inflate1' or 'autorecon2-cp' or 'autorecon2-wm' or
         'autorecon3' or 'autorecon3-T2pial' or 'autorecon-pial' or
         'autorecon-hemi' or 'localGI' or 'qcache', nipype default value:
         all)
        process directive
        flag: -%s, position: 0
environ: (a dictionary with keys which are a bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
expert: (an existing file name)
        Set parameters using expert file
        flag: -expert %s
flags: (a list of items which are a unicode string)
        additional parameters
        flag: %s
hemi: ('lh' or 'rh')
        hemisphere to process
        flag: -hemi %s
hippocampal_subfields_T1: (a boolean)
        segment hippocampal subfields using input T1 scan
        flag: -hippocampal-subfields-T1
hippocampal_subfields_T2: (a tuple of the form: (an existing file
         name, a unicode string))
        segment hippocampal subfields using T2 scan, identified by ID (may
        be combined with hippocampal_subfields_T1)
        flag: -hippocampal-subfields-T2 %s %s
hires: (a boolean)
        Conform to minimum voxel size (for voxels < 1mm)
        flag: -hires
mprage: (a boolean)
        Assume scan parameters are MGH MP-RAGE protocol, which produces
        darker gray matter
        flag: -mprage
mri_aparc2aseg: (a unicode string)
        Flags to pass to mri_aparc2aseg commands
        mutually_exclusive: expert
mri_ca_label: (a unicode string)
        Flags to pass to mri_ca_label commands
        mutually_exclusive: expert
mri_ca_normalize: (a unicode string)
        Flags to pass to mri_ca_normalize commands
        mutually_exclusive: expert
mri_ca_register: (a unicode string)
        Flags to pass to mri_ca_register commands
        mutually_exclusive: expert
mri_edit_wm_with_aseg: (a unicode string)
        Flags to pass to mri_edit_wm_with_aseg commands
        mutually_exclusive: expert
mri_em_register: (a unicode string)
        Flags to pass to mri_em_register commands
        mutually_exclusive: expert
mri_fill: (a unicode string)
        Flags to pass to mri_fill commands
        mutually_exclusive: expert
mri_mask: (a unicode string)
        Flags to pass to mri_mask commands
        mutually_exclusive: expert
mri_normalize: (a unicode string)
        Flags to pass to mri_normalize commands
        mutually_exclusive: expert
mri_pretess: (a unicode string)
        Flags to pass to mri_pretess commands
        mutually_exclusive: expert
mri_remove_neck: (a unicode string)
        Flags to pass to mri_remove_neck commands
        mutually_exclusive: expert
mri_segment: (a unicode string)
        Flags to pass to mri_segment commands
        mutually_exclusive: expert
mri_segstats: (a unicode string)
        Flags to pass to mri_segstats commands
        mutually_exclusive: expert
mri_tessellate: (a unicode string)
        Flags to pass to mri_tessellate commands
        mutually_exclusive: expert
mri_watershed: (a unicode string)
        Flags to pass to mri_watershed commands
        mutually_exclusive: expert
mris_anatomical_stats: (a unicode string)
        Flags to pass to mris_anatomical_stats commands
        mutually_exclusive: expert
mris_ca_label: (a unicode string)
        Flags to pass to mris_ca_label commands
        mutually_exclusive: expert
mris_fix_topology: (a unicode string)
        Flags to pass to mris_fix_topology commands
        mutually_exclusive: expert
mris_inflate: (a unicode string)
        Flags to pass to mri_inflate commands
        mutually_exclusive: expert
mris_make_surfaces: (a unicode string)
        Flags to pass to mris_make_surfaces commands
        mutually_exclusive: expert
mris_register: (a unicode string)
        Flags to pass to mris_register commands
        mutually_exclusive: expert
mris_smooth: (a unicode string)
        Flags to pass to mri_smooth commands
        mutually_exclusive: expert
mris_sphere: (a unicode string)
        Flags to pass to mris_sphere commands
        mutually_exclusive: expert
mris_surf2vol: (a unicode string)
        Flags to pass to mris_surf2vol commands
        mutually_exclusive: expert
mrisp_paint: (a unicode string)
        Flags to pass to mrisp_paint commands
        mutually_exclusive: expert
openmp: (an integer (int or long))
        Number of processors to use in parallel
        flag: -openmp %d
parallel: (a boolean)
        Enable parallel execution
        flag: -parallel
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
talairach: (a unicode string)
        Flags to pass to talairach commands
        mutually_exclusive: expert
use_FLAIR: (a boolean)
        Use FLAIR image to refine the pial surface
        flag: -FLAIRpial
        mutually_exclusive: use_T2
use_T2: (a boolean)
        Use T2 image to refine the pial surface
        flag: -T2pial
        mutually_exclusive: use_FLAIR
xopts: ('use' or 'clean' or 'overwrite')
        Use, delete or overwrite existing expert options file
        flag: -xopts-%s

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
area_pial: (a list of items which are an existing file name)
        Mean area of triangles each vertex on the pial surface is associated
        with
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
avg_curv: (a list of items which are an existing file name)
        Average atlas curvature, sampled to subject
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_pial: (a list of items which are an existing file name)
        Curvature of pial surface
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
graymid: (a list of items which are an existing file name)
        Graymid/midthickness surface meshes
inflated: (a list of items which are an existing file name)
        Inflated surface meshes
jacobian_white: (a list of items which are an existing file name)
        Distortion required to register to spherical atlas
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

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
resampled_file: (a file name)
        output filename
        flag: -o %s, position: -1
subjects_dir: (an existing directory name)
        subjects directory

Outputs:

resampled_file: (an existing file name)
        output filename

RobustRegister

Link to code

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: (an existing file name)
        volume to be registered
        flag: --mov %s
target_file: (an existing 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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', 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
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 bool or None or a file name, nipype default value:
         True)
        registration file; either True or filename
        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
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: (an existing file name)
        source image mapped to halfway space
half_source_xfm: (an existing file name)
        transform file to map source image to halfway space
half_targ: (an existing file name)
        target image mapped to halfway space
half_targ_xfm: (an existing file name)
        transform file to map target image to halfway space
half_weights: (an existing file name)
        weights image mapped to halfway space
out_reg_file: (an existing file name)
        output registration file
registered_file: (an existing file name)
        output image with registration applied
weights_file: (an existing file name)
        image of weights used

SegmentCC

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
out_file: (a file name)
        Filename to write aseg including CC
        flag: -o %s
subjects_dir: (an existing directory name)
        subjects directory

Outputs:

out_file: (a file name)
        Output segmentation uncluding corpus collosum
out_rotation: (a file name)
        Output lta rotation file

SegmentWM

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
subjects_dir: (an existing directory name)
        subjects directory

Outputs:

out_file: (a file name)
        Output white matter segmentation

Smooth

Link to code

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: (a long 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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
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
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

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
fixed_weighting: (a boolean)
        use a fixed weighting to generate optimal gray/white contrast
        flag: -w, position: 1
out_file: (a file name)
        image to write
        flag: %s
subjects_dir: (an existing directory name)
        subjects directory

Outputs:

out_file: (an existing file name)
        synthesized FLASH acquisition

UnpackSDICOMDir

Link to code

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: ('fsfast' or 'generic')
        unpack to specified directory structures
        flag: -%s
environ: (a dictionary with keys which are a bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
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

Outputs:

None

WatershedSkullStrip

Link to code

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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
subjects_dir: (an existing directory name)
        subjects directory
t1: (a boolean)
        specify T1 input volume (T1 grey value = 110)
        flag: -T1
transform: (a file name)
        undocumented
        flag: %s, position: -3

Outputs:

out_file: (a file name)
        skull stripped brain volume