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

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


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


.. index:: ApplyVolTransform

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

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/freesurfer/preprocess.py#L818>`_

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
                mutually_exclusive: target_file, tal, fs_target
                requires: reg_file
        fsl_reg_file: (an existing file name)
                fslRAS-to-fslRAS matrix (FSL format)
                mutually_exclusive: reg_file, fsl_reg_file, xfm_reg_file, reg_header, subject
        reg_file: (an existing file name)
                tkRAS-to-tkRAS matrix   (tkregister2 format)
                mutually_exclusive: reg_file, fsl_reg_file, xfm_reg_file, reg_header, subject
        reg_header: (a boolean)
                ScannerRAS-to-ScannerRAS matrix = identity
                mutually_exclusive: reg_file, fsl_reg_file, xfm_reg_file, reg_header, subject
        source_file: (an existing file name)
                Input volume you wish to transform
        subject: (a string)
                set matrix = identity and use subject for any templates
                mutually_exclusive: reg_file, fsl_reg_file, xfm_reg_file, reg_header, subject
        tal: (a boolean)
                map to a sub FOV of MNI305 (with --reg only)
                mutually_exclusive: target_file, tal, fs_target
        target_file: (an existing file name)
                Output template volume
                mutually_exclusive: target_file, tal, fs_target
        xfm_reg_file: (an existing file name)
                ScannerRAS-to-ScannerRAS matrix (MNI format)
                mutually_exclusive: reg_file, fsl_reg_file, xfm_reg_file, reg_header, subject

        [Optional]
        args: (a string)
                Additional parameters to the command
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the interface fails to
                run
        interp: ('trilin' or 'nearest')
                Interpolation method (<trilin> or nearest)
        inverse: (a boolean)
                sample from target to source
        no_resample: (a boolean)
                Do not resample; just change vox2ras matrix
        subjects_dir: (an existing directory name)
                subjects directory
        transformed_file: (a file name)
                Output volume

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 <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/freesurfer/preprocess.py#L705>`_

Wraps command **bbregister**

Use FreeSurfer bbregister to register a volume to the Freesurfer anatomical.

This program performs within-subject, cross-modal registration using a
boundary-based cost function. The registration is constrained to be 6
DOF (rigid). It is required that you have an anatomical scan of the
subject that has already been recon-all-ed using freesurfer.

Examples
~~~~~~~~

>>> from nipype.interfaces.freesurfer import BBRegister
>>> bbreg = BBRegister(subject_id='me', source_file='structural.nii', init='header', contrast_type='t2')
>>> bbreg.cmdline
'bbregister --t2 --init-header --reg structural_bbreg_me.dat --mov structural.nii --s me'

Inputs::

        [Mandatory]
        contrast_type: ('t1' or 't2')
                contrast type of image
        init: ('spm' or 'fsl' or 'header')
                initialize registration spm, fsl, header
                mutually_exclusive: init_reg_file
        init_reg_file: (an existing file name)
                existing registration file
                mutually_exclusive: init
        source_file: (a file name)
                source file to be registered
        subject_id: (a string)
                freesurfer subject id

        [Optional]
        args: (a string)
                Additional parameters to the command
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        epi_mask: (a boolean)
                mask out B0 regions in stages 1 and 2
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the interface fails to
                run
        intermediate_file: (an existing file name)
                Intermediate image, e.g. in case of partial FOV
        out_fsl_file: (a boolean or a file name)
                write the transformation matrix in FSL FLIRT format
        out_reg_file: (a file name)
                output registration file
        reg_frame: (an integer)
                0-based frame index for 4D source file
                mutually_exclusive: reg_middle_frame
        reg_middle_frame: (a boolean)
                Register middle frame of 4D source file
                mutually_exclusive: reg_frame
        registered_file: (a boolean or a file name)
                output warped sourcefile either True or filename
        spm_nifti: (a boolean)
                force use of nifti rather than analyze with SPM
        subjects_dir: (an existing directory name)
                subjects directory

Outputs::

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

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


.. index:: DICOMConvert

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

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/freesurfer/preprocess.py#L436>`_

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 string)
                Additional parameters to the command
        dicom_info: (an existing file name)
                File containing summary information from mri_parse_sdcmdir
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        file_mapping: (a list of items which are a tuple of the form: (a string, a 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: ('cor' or 'mgh' or 'mgz' or 'minc' or 'analyze' or 'analyze4d' or 'spm' or
                 'afni' or 'brik' or 'bshort' or 'bfloat' or 'sdt' or 'outline' or 'otl' or 'gdf' or
                 'nifti1' or 'nii' or 'niigz', nipype default value: niigz)
                defines the type of output file produced
        seq_list: (a list of items which are a string)
                list of pulse sequence names to be converted.
                requires: dicom_info
        subject_dir_template: (a string, nipype default value: S.%04d)
                template for subject directory name
        subject_id
                subject identifier to insert into template
        subjects_dir: (an existing directory name)
                subjects directory

Outputs::

        None

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


.. index:: FitMSParams

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

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/freesurfer/preprocess.py#L1092>`_

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 a file name)
                list of FLASH images (must be in mgh format)

        [Optional]
        args: (a string)
                Additional parameters to the command
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        flip_list: (a list of items which are an integer)
                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
        subjects_dir: (an existing directory name)
                subjects directory
        te_list: (a list of items which are a float)
                list of TEs of the input files (in msec)
        tr_list: (a list of items which are an integer)
                list of TRs of the input files (in msec)
        xfm_list: (a list of items which are a 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.MRIConvert:


.. index:: MRIConvert

MRIConvert
----------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/freesurfer/preprocess.py#L322>`_

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

        [Optional]
        apply_inv_transform: (an existing file name)
                apply inverse transformation xfm file
        apply_transform: (an existing file name)
                apply xfm file
        args: (a string)
                Additional parameters to the command
        ascii: (a boolean)
                save output as ascii col>row>slice>frame
        autoalign_matrix: (an existing file name)
                text file with autoalign matrix
        color_file: (an existing file name)
                color file
        conform: (a boolean)
                conform to 256^3
        conform_min: (a boolean)
                conform to smallest size
        conform_size: (a float)
                conform to size_in_mm
        crop_center: (a tuple of the form: (an integer, an integer, an integer))
                <x> <y> <z> crop to 256 around center (x, y, z)
        crop_gdf: (a boolean)
                apply GDF cropping
        crop_size: (a tuple of the form: (an integer, an integer, an integer))
                <dx> <dy> <dz> crop to size <dx, dy, dz>
        cut_ends: (an integer)
                remove ncut slices from the ends
        devolve_transform: (a string)
                subject id
        drop_n: (an integer)
                drop the last n frames
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        fill_parcellation: (a boolean)
                fill parcellation
        force_ras: (a boolean)
                use default when orientation info absent
        frame: (an integer)
                keep only 0-based frame number
        frame_subsample: (a tuple of the form: (an integer, an integer, an integer))
                start delta end : frame subsampling (end = -1 for end)
        fwhm: (a float)
                smooth input volume by fwhm mm
        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>
        in_i_dir: (a tuple of the form: (a float, a float, a float))
                <R direction> <A direction> <S direction>
        in_i_size: (an integer)
                input i size
        in_info: (a boolean)
                display input info
        in_j_dir: (a tuple of the form: (a float, a float, a float))
                <R direction> <A direction> <S direction>
        in_j_size: (an integer)
                input j size
        in_k_dir: (a tuple of the form: (a float, a float, a float))
                <R direction> <A direction> <S direction>
        in_k_size: (an integer)
                input k size
        in_like: (an existing file name)
                input looks like
        in_matrix: (a boolean)
                display input matrix
        in_orientation: ('LAI' or 'LIA' or 'ALI' or 'AIL' or 'ILA' or 'IAL' or 'LAS' or 'LSA' or
                 'ALS' or 'ASL' or 'SLA' or 'SAL' or 'LPI' or 'LIP' or 'PLI' or 'PIL' or 'ILP' or 'IPL'
                 or 'LPS' or 'LSP' or 'PLS' or 'PSL' or 'SLP' or 'SPL' or 'RAI' or 'RIA' or 'ARI' or
                 'AIR' or 'IRA' or 'IAR' or 'RAS' or 'RSA' or 'ARS' or 'ASR' or 'SRA' or 'SAR' or 'RPI'
                 or 'RIP' or 'PRI' or 'PIR' or 'IRP' or 'IPR' or 'RPS' or 'RSP' or 'PRS' or 'PSR' or
                 'SRP' or 'SPR')
                specify the input orientation
        in_scale: (a float)
                input intensity scale factor
        in_stats: (a boolean)
                display input stats
        in_type: ('cor' or 'mgh' or 'mgz' or 'minc' or 'analyze' or 'analyze4d' or 'spm' or
                 'afni' or 'brik' or 'bshort' or 'bfloat' or 'sdt' or 'outline' or 'otl' or 'gdf' or
                 'nifti1' or 'nii' or 'niigz' or 'ge' or 'gelx' or 'lx' or 'ximg' or 'siemens' or
                 'dicom' or 'siemens_dicom')
                input file type
        invert_contrast: (a float)
                threshold for inversting contrast
        midframe: (a boolean)
                keep only the middle frame
        no_change: (a boolean)
                don't change type of input to that of template
        no_scale: (a boolean)
                dont rescale values for COR
        no_translate: (a boolean)
                ~~~
        no_write: (a boolean)
                do not write output
        out_center: (a tuple of the form: (a float, a float, a float))
                <R coordinate> <A coordinate> <S coordinate>
        out_datatype: ('uchar' or 'short' or 'int' or 'float')
        out_file: (a file name)
                output filename or True to generate one
        out_i_count: (an integer)
                some count ?? in i direction
        out_i_dir: (a tuple of the form: (a float, a float, a float))
                <R direction> <A direction> <S direction>
        out_i_size: (an integer)
                output i size
        out_info: (a boolean)
                display output info
        out_j_count: (an integer)
                some count ?? in j direction
        out_j_dir: (a tuple of the form: (a float, a float, a float))
                <R direction> <A direction> <S direction>
        out_j_size: (an integer)
                output j size
        out_k_count: (an integer)
                some count ?? in k direction
        out_k_dir: (a tuple of the form: (a float, a float, a float))
                <R direction> <A direction> <S direction>
        out_k_size: (an integer)
                output k size
        out_matrix: (a boolean)
                display output matrix
        out_orientation: ('LAI' or 'LIA' or 'ALI' or 'AIL' or 'ILA' or 'IAL' or 'LAS' or 'LSA' or
                 'ALS' or 'ASL' or 'SLA' or 'SAL' or 'LPI' or 'LIP' or 'PLI' or 'PIL' or 'ILP' or 'IPL'
                 or 'LPS' or 'LSP' or 'PLS' or 'PSL' or 'SLP' or 'SPL' or 'RAI' or 'RIA' or 'ARI' or
                 'AIR' or 'IRA' or 'IAR' or 'RAS' or 'RSA' or 'ARS' or 'ASR' or 'SRA' or 'SAR' or 'RPI'
                 or 'RIP' or 'PRI' or 'PIR' or 'IRP' or 'IPR' or 'RPS' or 'RSP' or 'PRS' or 'PSR' or
                 'SRP' or 'SPR')
                specify the output orientation
        out_scale: (a float)
                output intensity scale factor
        out_stats: (a boolean)
                display output stats
        out_type: ('cor' or 'mgh' or 'mgz' or 'minc' or 'analyze' or 'analyze4d' or 'spm' or
                 'afni' or 'brik' or 'bshort' or 'bfloat' or 'sdt' or 'outline' or 'otl' or 'gdf' or
                 'nifti1' or 'nii' or 'niigz')
                output file type
        parse_only: (a boolean)
                parse input only
        read_only: (a boolean)
                read the input volume
        reorder: (a tuple of the form: (an integer, an integer, an integer))
                olddim1 olddim2 olddim3
        resample_type: ('interpolate' or 'weighted' or 'nearest' or 'sinc' or 'cubic')
                <interpolate|weighted|nearest|sinc|cubic> (default is interpolate)
        reslice_like: (an existing file name)
                reslice output to match file
        sdcm_list: (an existing file name)
                list of DICOM files for conversion
        skip_n: (an integer)
                skip the first n frames
        slice_bias: (a float)
                apply half-cosine bias field
        slice_crop: (a tuple of the form: (an integer, an integer))
                s_start s_end : keep slices s_start to s_end
        slice_reverse: (a boolean)
                reverse order of slices, update vox2ras
        smooth_parcellation: (a boolean)
                smooth parcellation
        sphinx: (a boolean)
                change orientation info to sphinx
        split: (a boolean)
                split output frames into separate output files.
        status_file: (a file name)
                status file for DICOM conversion
        subject_name: (a string)
                subject name ???
        subjects_dir: (an existing directory name)
                subjects directory
        template_info: (a boolean)
                dump info about template
        template_type: ('cor' or 'mgh' or 'mgz' or 'minc' or 'analyze' or 'analyze4d' or 'spm' or
                 'afni' or 'brik' or 'bshort' or 'bfloat' or 'sdt' or 'outline' or 'otl' or 'gdf' or
                 'nifti1' or 'nii' or 'niigz' or 'ge' or 'gelx' or 'lx' or 'ximg' or 'siemens' or
                 'dicom' or 'siemens_dicom')
                template file type
        unwarp_gradient: (a boolean)
                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
        zero_ge_z_offset: (a boolean)
                zero ge z offset ???
        zero_outlines: (a boolean)
                zero outlines

Outputs::

        out_file: (an existing file name)
                converted output file

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


.. index:: ParseDICOMDir

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

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/freesurfer/preprocess.py#L46>`_

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

        [Optional]
        args: (a string)
                Additional parameters to the command
        dicom_info_file: (a file name, nipype default value: dicominfo.txt)
                file to which results are written
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the interface fails to
                run
        sortbyrun: (a boolean)
                assign run numbers
        subjects_dir: (an existing directory name)
                subjects directory
        summarize: (a boolean)
                only print out info for run leaders

Outputs::

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

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


.. index:: ReconAll

ReconAll
--------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/freesurfer/preprocess.py#L610>`_

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 -i structural.nii -all -subjid foo -sd .'

Inputs::

        [Mandatory]

        [Optional]
        T1_files: (an existing file name)
                name of T1 file to process
        args: (a string)
                Additional parameters to the command
        directive: ('all' or 'autorecon1' or 'autorecon2' or 'autorecon2-cp' or 'autorecon2-wm'
                 or 'autorecon2-inflate1' or 'autorecon2-perhemi' or 'autorecon3' or 'localGI' or
                 'qcache', nipype default value: all)
                process directive
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        flags: (a string)
                additional parameters
        hemi: ('lh' or 'rh')
                hemisphere to process
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the interface fails to
                run
        subject_id: (a string, nipype default value: recon_all)
                subject name
        subjects_dir: (an existing directory name)
                path to subjects directory

Outputs::

        BA_stats: (an existing file name)
                Brodmann Area statistics files
        T1: (an existing file name)
                Intensity normalized whole-head volume
        annot: (an existing file name)
                Surface annotation files
        aparc_a2009s_stats: (an existing file name)
                Aparc a2009s parcellation statistics files
        aparc_aseg: (an existing file name)
                Aparc parcellation projected into aseg volume
        aparc_stats: (an existing file name)
                Aparc parcellation statistics files
        aseg: (an existing file name)
                Volumetric map of regions from automatic segmentation
        aseg_stats: (an existing file name)
                Automated segmentation statistics file
        brain: (an existing file name)
                Intensity normalized brain-only volume
        brainmask: (an existing file name)
                Skull-stripped (brain-only) volume
        curv: (an existing file name)
                Maps of surface curvature
        curv_stats: (an existing file name)
                Curvature statistics files
        entorhinal_exvivo_stats: (an existing file name)
                Entorhinal exvivo statistics files
        filled: (an existing file name)
                Subcortical mass volume
        inflated: (an existing file name)
                Inflated surface meshes
        label: (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: (an existing file name)
                Gray matter/pia mater surface meshes
        rawavg: (an existing file name)
                Volume formed by averaging input images
        ribbon: (an existing file name)
                Volumetric maps of cortical ribbons
        smoothwm: (an existing file name)
                Smoothed original surface meshes
        sphere: (an existing file name)
                Spherical surface meshes
        sphere_reg: (an existing file name)
                Spherical registration file
        subject_id: (a string)
                Subject name for whom to retrieve data
        subjects_dir: (an existing directory name)
                Freesurfer subjects directory.
        sulc: (an existing file name)
                Surface maps of sulcal depth
        thickness: (an existing file name)
                Surface maps of cortical thickness
        volume: (an existing file name)
                Surface maps of cortical volume
        white: (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: (an existing file name)
                White matter parcellation statistics file

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


.. index:: Resample

Resample
--------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/freesurfer/preprocess.py#L550>`_

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
        voxel_size: (a tuple of the form: (a float, a float, a float))
                triplet of output voxel sizes

        [Optional]
        args: (a string)
                Additional parameters to the command
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the interface fails to
                run
        resampled_file: (a file name)
                output filename
        subjects_dir: (an existing directory name)
                subjects directory

Outputs::

        resampled_file: (an existing file name)
                output filename

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


.. index:: RobustRegister

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

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/freesurfer/preprocess.py#L1004>`_

Wraps command **mri_robust_register**

Perform intramodal linear registration (translation and rotation) using robust statistics.

Examples
~~~~~~~~
>>> from nipype.interfaces.freesurfer import RobustRegister
>>> reg = RobustRegister()
>>> reg.inputs.source_file = 'structural.nii'
>>> reg.inputs.target_file = 'T1.nii'
>>> reg.inputs.auto_sens = True
>>> reg.inputs.init_orient = True
>>> reg.cmdline
'mri_robust_register --satit --initorient --lta structural_robustreg.lta --mov structural.nii --dst T1.nii'

References
~~~~~~~~~~
Reuter, M, Rosas, HD, and Fischl, B, (2010). Highly Accurate Inverse Consistent Registration:
A Robust Approach.  Neuroimage 53(4) 1181-96.

Inputs::

        [Mandatory]
        auto_sens: (a boolean)
                auto-detect good sensitivity
                mutually_exclusive: outlier_sens
        outlier_sens: (a float)
                set outlier sensitivity explicitly
                mutually_exclusive: auto_sens
        source_file: (a file name)
                volume to be registered
        target_file: (a file name)
                target volume for the registration

        [Optional]
        args: (a string)
                Additional parameters to the command
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        est_int_scale: (a boolean)
                estimate intensity scale (recommended for unnormalized images)
        force_double: (a boolean)
                use double-precision intensities
        force_float: (a boolean)
                use float intensities
        half_source: (a boolean or a file name)
                write source volume mapped to halfway space
        half_source_xfm: (a boolean or a file name)
                write transform from source to halfway space
        half_targ: (a boolean or a file name)
                write target volume mapped to halfway space
        half_targ_xfm: (a boolean or a file name)
                write transform from target to halfway space
        half_weights: (a boolean or a file name)
                write weights volume mapped to halfway space
        high_iterations: (an integer)
                max # of times on highest resolution
        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
        init_orient: (a boolean)
                use moments for initial orient (recommended for stripped brains)
        iteration_thresh: (a float)
                stop iterations when below threshold
        least_squares: (a boolean)
                use least squares instead of robust estimator
        mask_source: (an existing file name)
                image to mask source volume with
        mask_target: (an existing file name)
                image to mask target volume with
        max_iterations: (an integer)
                maximum # of times on each resolution
        no_init: (a boolean)
                skip transform init
        no_multi: (a boolean)
                work on highest resolution
        out_reg_file: (a file name)
                registration file to write
        outlier_limit: (a float)
                set maximal outlier limit in satit
        registered_file: (a boolean or a file name)
                registered image; either True or filename
        subjects_dir: (an existing directory name)
                subjects directory
        subsample_thresh: (an integer)
                subsample if dimension is above threshold size
        trans_only: (a boolean)
                find 3 parameter translation only
        weights_file: (a boolean or a file name)
                weights image to write; either True or filename
        write_vo2vox: (a boolean)
                output vox2vox matrix (default is RAS2RAS)

Outputs::

        half_source: (a file name)
                source image mapped to halfway space
        half_source_xfm: (a file name)
                transform file to map source image to halfway space
        half_targ: (a file name)
                target image mapped to halfway space
        half_targ_xfm: (a file name)
                transform file to map target image to halfway space
        half_weights: (a file name)
                weights image mapped to halfway space
        out_reg_file: (an existing file name)
                output registration file
        registered_file: (a file name)
                output image with registration applied
        weights_file: (a file name)
                image of weights used

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


.. index:: Smooth

Smooth
------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/freesurfer/preprocess.py#L893>`_

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 --vol-fwhm 6'

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                source volume
        num_iters: (an integer)
                number of iterations instead of fwhm
                mutually_exclusive: surface_fwhm
        reg_file: (an existing file name)
                registers volume to surface anatomical
        surface_fwhm: (a float)
                surface FWHM in mm
                mutually_exclusive: num_iters
                requires: reg_file

        [Optional]
        args: (a string)
                Additional parameters to the command
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the interface fails to
                run
        proj_frac: (a float)
                project frac of thickness a long surface normal
                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
                mutually_exclusive: proj_frac
        smoothed_file: (a file name)
                output volume
        subjects_dir: (an existing directory name)
                subjects directory
        vol_fwhm: (a float)
                volumesmoothing outside of surface

Outputs::

        smoothed_file: (a file name)
                smoothed input volume

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


.. index:: SynthesizeFLASH

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

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/freesurfer/preprocess.py#L1164>`_

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)
        pd_image: (an existing file name)
                image of proton density values
        t1_image: (an existing file name)
                image of T1 values
        te: (a float)
                echo time (in msec)
        tr: (a float)
                repetition time (in msec)

        [Optional]
        args: (a string)
                Additional parameters to the command
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        fixed_weighting: (a boolean)
                use a fixed weighting to generate optimal gray/white contrast
        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
        subjects_dir: (an existing directory name)
                subjects directory

Outputs::

        out_file: (an existing file name)
                synthesized FLASH acquisition

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


.. index:: UnpackSDICOMDir

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

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/freesurfer/preprocess.py#L106>`_

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
                mutually_exclusive: run_info, config, seq_config
        run_info: (a tuple of the form: (an integer, a string, a string, a string))
                runno subdir format name : spec unpacking rules on cmdline
                mutually_exclusive: run_info, config, seq_config
        seq_config: (an existing file name)
                specify unpacking rules based on sequence
                mutually_exclusive: run_info, config, seq_config
        source_dir: (an existing directory name)
                directory with the DICOM files

        [Optional]
        args: (a string)
                Additional parameters to the command
        dir_structure: ('fsfast' or 'generic')
                unpack to specified directory structures
        environ: (a dictionary with keys which are a value of type 'str' and with values which
                 are a value of type 'str', nipype default value: {})
                Environment variables
        ignore_exception: (a boolean, nipype default value: False)
                Print an error message instead of throwing an exception in case the interface fails to
                run
        log_file: (an existing file name)
                explicilty set log file
        no_info_dump: (a boolean)
                do not create infodump file
        no_unpack_err: (a boolean)
                do not try to unpack runs with errors
        output_dir: (a directory name)
                top directory into which the files will be unpacked
        scan_only: (an existing file name)
                only scan the directory and put result in file
        spm_zeropad: (an integer)
                set frame number zero padding width for SPM
        subjects_dir: (an existing directory name)
                subjects directory

Outputs::

        None
