.. AUTO-GENERATED FILE -- DO NOT EDIT!

interfaces.freesurfer.preprocess
================================


.. _nipype.interfaces.freesurfer.preprocess.ApplyVolTransform:


.. index:: ApplyVolTransform

ApplyVolTransform
-----------------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L1651>`__

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 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

.. _nipype.interfaces.freesurfer.preprocess.BBRegister:


.. index:: BBRegister

BBRegister
----------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L1445>`__

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: (u't1' or u't2' or u'bold' or u'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 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
        fsldof: (an integer (int or long))
                degrees of freedom for initial registration (FSL)
                flag: --fsl-dof %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
        init: (u'coreg' or u'rr' or u'spm' or u'fsl' or u'header' or u'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
        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::

        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

.. _nipype.interfaces.freesurfer.preprocess.CALabel:


.. index:: CALabel

CALabel
-------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L2564>`__

Wraps command **mri_ca_label**

For complete details, see the `FS Documentation <http://surfer.nmr.mgh.harvard.edu/fswiki/mri_ca_register>`_

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

.. _nipype.interfaces.freesurfer.preprocess.CANormalize:


.. index:: CANormalize

CANormalize
-----------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L2390>`__

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 <http://surfer.nmr.mgh.harvard.edu/fswiki/mri_ca_normalize>`_

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

.. _nipype.interfaces.freesurfer.preprocess.CARegister:


.. index:: CARegister

CARegister
----------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L2469>`__

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 <http://surfer.nmr.mgh.harvard.edu/fswiki/mri_ca_register>`_

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

.. _nipype.interfaces.freesurfer.preprocess.ConcatenateLTA:


.. index:: ConcatenateLTA

ConcatenateLTA
--------------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L3015>`__

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 u'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 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_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: (u'VOX2VOX' or u'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
        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

.. _nipype.interfaces.freesurfer.preprocess.DICOMConvert:


.. index:: DICOMConvert

DICOMConvert
------------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L564>`__

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

.. _nipype.interfaces.freesurfer.preprocess.EditWMwithAseg:


.. index:: EditWMwithAseg

EditWMwithAseg
--------------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L2931>`__

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

.. _nipype.interfaces.freesurfer.preprocess.FitMSParams:


.. index:: FitMSParams

FitMSParams
-----------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L2004>`__

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

.. _nipype.interfaces.freesurfer.preprocess.MNIBiasCorrection:


.. index:: MNIBiasCorrection

MNIBiasCorrection
-----------------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L2187>`__

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: (an existing file name)
                output volume

.. _nipype.interfaces.freesurfer.preprocess.MRIConvert:


.. index:: MRIConvert

MRIConvert
----------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L428>`__

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

.. _nipype.interfaces.freesurfer.preprocess.MRIsCALabel:


.. index:: MRIsCALabel

MRIsCALabel
-----------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L2658>`__

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

.. _nipype.interfaces.freesurfer.preprocess.Normalize:


.. index:: Normalize

Normalize
---------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L2322>`__

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

.. _nipype.interfaces.freesurfer.preprocess.ParseDICOMDir:


.. index:: ParseDICOMDir

ParseDICOMDir
-------------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L64>`__

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

.. _nipype.interfaces.freesurfer.preprocess.ReconAll:


.. index:: ReconAll

ReconAll
--------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L887>`__

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: (u'all' or u'autorecon1' or u'autorecon2' or
                 u'autorecon2-volonly' or u'autorecon2-perhemi' or
                 u'autorecon2-inflate1' or u'autorecon2-cp' or u'autorecon2-wm' or
                 u'autorecon3' or u'autorecon3-T2pial' or u'autorecon-pial' or u
                 'autorecon-hemi' 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
        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: (u'lh' or u'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
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the
                interface fails to run
        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
        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_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: (u'use' or u'clean' or u'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

.. _nipype.interfaces.freesurfer.preprocess.Resample:


.. index:: Resample

Resample
--------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L687>`__

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

.. _nipype.interfaces.freesurfer.preprocess.RobustRegister:


.. index:: RobustRegister

RobustRegister
--------------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L1909>`__

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 # doctest: +ELLIPSIS
'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 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 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
        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: (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

.. _nipype.interfaces.freesurfer.preprocess.SegmentCC:


.. index:: SegmentCC

SegmentCC
---------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L2768>`__

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

.. _nipype.interfaces.freesurfer.preprocess.SegmentWM:


.. index:: SegmentWM

SegmentWM
---------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L2870>`__

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

.. _nipype.interfaces.freesurfer.preprocess.Smooth:


.. index:: Smooth

Smooth
------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L1740>`__

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

.. _nipype.interfaces.freesurfer.preprocess.SynthesizeFLASH:


.. index:: SynthesizeFLASH

SynthesizeFLASH
---------------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L2093>`__

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

.. _nipype.interfaces.freesurfer.preprocess.UnpackSDICOMDir:


.. index:: UnpackSDICOMDir

UnpackSDICOMDir
---------------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L142>`__

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

.. _nipype.interfaces.freesurfer.preprocess.WatershedSkullStrip:


.. index:: WatershedSkullStrip

WatershedSkullStrip
-------------------

`Link to code <file:///build/nipype-fj7ofr/nipype-1.0.0+git69-gdb2670326/nipype/interfaces/freesurfer/preprocess.py#L2249>`__

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
