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# interfaces.freesurfer.preprocess

## ApplyVolTransform

Link to code

Wraps command mri_vol2vol

Use FreeSurfer mri_vol2vol to apply a transform.

### Examples

    >>> from nipype.interfaces.freesurfer import ApplyVolTransform
    >>> applyreg = ApplyVolTransform()
    >>> applyreg.inputs.source_file = 'structural.nii'
    >>> applyreg.inputs.reg_file = 'register.dat'
    >>> applyreg.inputs.transformed_file = 'struct_warped.nii'
    >>> applyreg.inputs.fs_target = True
    >>> applyreg.cmdline
    'mri_vol2vol --fstarg --reg register.dat --mov structural.nii --o struct_warped.nii'

Inputs:

    [Mandatory]
    fs_target: (a boolean)
            use orig.mgz from subject in regfile as target
            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

## BBRegister

Link to code

Wraps command bbregister

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

This program performs within-subject, cross-modal registration using a boundary-based cost function. 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

## DICOMConvert

Link to code

Wraps command mri_convert

use fs mri_convert to convert dicom files

### Examples

    >>> from nipype.interfaces.freesurfer import DICOMConvert
    >>> cvt = DICOMConvert()
    >>> cvt.inputs.dicom_dir = 'dicomdir'
    >>> cvt.inputs.file_mapping = [('nifti', '*.nii'), ('info', 'dicom*.txt'), ('dti', '*dti.bv*')]

Inputs:

    [Mandatory]
    base_output_dir: (a directory name)
            directory in which subject directories are created
    dicom_dir: (an existing directory name)
            dicom directory from which to convert dicom files

    [Optional]
    args: (a 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

## FitMSParams

Link to code

Wraps command mri_ms_fitparms

Estimate tissue paramaters from a set of FLASH images.

### Examples

    >>> from nipype.interfaces.freesurfer import FitMSParams
    >>> msfit = FitMSParams()
    >>> msfit.inputs.in_files = ['flash_05.mgz', 'flash_30.mgz']
    >>> msfit.inputs.out_dir = 'flash_parameters'
    >>> msfit.cmdline
    'mri_ms_fitparms  flash_05.mgz flash_30.mgz flash_parameters'

Inputs:

    [Mandatory]
    in_files: (a list of items which are 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

## MRIConvert

Link to code

Wraps command mri_convert

use fs mri_convert to manipulate files

Note

Adds niigz as an output type option

### Examples

    >>> mc = MRIConvert()
    >>> mc.inputs.in_file = 'structural.nii'
    >>> mc.inputs.out_file = 'outfile.mgz'
    >>> mc.inputs.out_type = 'mgz'
    >>> mc.cmdline
    'mri_convert --out_type mgz --input_volume structural.nii --output_volume outfile.mgz'

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            File to read/convert

    [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

## ParseDICOMDir

Link to code

Wraps command mri_parse_sdcmdir

Uses mri_parse_sdcmdir to get information from dicom directories

### Examples

    >>> from nipype.interfaces.freesurfer import ParseDICOMDir
    >>> dcminfo = ParseDICOMDir()
    >>> dcminfo.inputs.dicom_dir = '.'
    >>> dcminfo.inputs.sortbyrun = True
    >>> dcminfo.inputs.summarize = True
    >>> dcminfo.cmdline
    'mri_parse_sdcmdir --d . --o dicominfo.txt --sortbyrun --summarize'

Inputs:

    [Mandatory]
    dicom_dir: (an existing directory name)
            path to siemens dicom directory

    [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

## ReconAll

Link to code

Wraps command recon-all

Uses recon-all to generate surfaces and parcellations of structural data from anatomical images of a subject.

### Examples

    >>> from nipype.interfaces.freesurfer import ReconAll
    >>> reconall = ReconAll()
    >>> reconall.inputs.subject_id = 'foo'
    >>> reconall.inputs.directive = 'all'
    >>> reconall.inputs.subjects_dir = '.'
    >>> reconall.inputs.T1_files = 'structural.nii'
    >>> reconall.cmdline
    'recon-all -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

## Resample

Link to code

Wraps command mri_convert

Use FreeSurfer mri_convert to up or down-sample image files

### Examples

    >>> from nipype.interfaces import freesurfer
    >>> resampler = freesurfer.Resample()
    >>> resampler.inputs.in_file = 'structural.nii'
    >>> resampler.inputs.resampled_file = 'resampled.nii'
    >>> resampler.inputs.voxel_size = (2.1, 2.1, 2.1)
    >>> resampler.cmdline
    'mri_convert -vs 2.10 2.10 2.10 -i structural.nii -o resampled.nii'

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            file to resample
    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

## RobustRegister

Link to code

Wraps command mri_robust_register

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

### Examples

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

### References

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

Inputs:

    [Mandatory]
    auto_sens: (a boolean)
            auto-detect good sensitivity
            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

## Smooth

Link to code

Wraps command mris_volsmooth

Use FreeSurfer mris_volsmooth to smooth a volume

This function smoothes cortical regions on a surface and non-cortical regions in volume.

Note

Cortical voxels are mapped to the surface (3D->2D) and then the smoothed values from the surface are put back into the volume to fill the cortical ribbon. If data is smoothed with this algorithm, one has to be careful about how further processing is interpreted.

### Examples

    >>> from nipype.interfaces.freesurfer import Smooth
    >>> smoothvol = Smooth(in_file='functional.nii', smoothed_file = 'foo_out.nii', reg_file='register.dat', surface_fwhm=10, vol_fwhm=6)
    >>> smoothvol.cmdline
    'mris_volsmooth --i functional.nii --reg register.dat --o foo_out.nii --fwhm 10 --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

## SynthesizeFLASH

Link to code

Wraps command mri_synthesize

Synthesize a FLASH acquisition from T1 and proton density maps.

### Examples

    >>> from nipype.interfaces.freesurfer import SynthesizeFLASH
    >>> syn = SynthesizeFLASH(tr=20, te=3, flip_angle=30)
    >>> syn.inputs.t1_image = 'T1.mgz'
    >>> syn.inputs.pd_image = 'PD.mgz'
    >>> syn.inputs.out_file = 'flash_30syn.mgz'
    >>> syn.cmdline
    'mri_synthesize 20.00 30.00 3.000 T1.mgz PD.mgz flash_30syn.mgz'

Inputs:

    [Mandatory]
    flip_angle: (a float)
            flip angle (in degrees)
    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

## UnpackSDICOMDir

Link to code

Wraps command unpacksdcmdir

Use unpacksdcmdir to convert dicom files

Call unpacksdcmdir -help from the command line to see more information on using this command.

### Examples

    >>> from nipype.interfaces.freesurfer import UnpackSDICOMDir
    >>> unpack = UnpackSDICOMDir()
    >>> unpack.inputs.source_dir = '.'
    >>> unpack.inputs.output_dir = '.'
    >>> unpack.inputs.run_info = (5, 'mprage', 'nii', 'struct')
    >>> unpack.inputs.dir_structure = 'generic'
    >>> unpack.cmdline
    'unpacksdcmdir -generic -targ . -run 5 mprage nii struct -src .'

Inputs:

    [Mandatory]
    config: (an existing file name)
            specify unpacking rules in file
            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