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# interfaces.mrtrix3.reconst

## EstimateFOD

Link to code

Wraps command dwi2fod

Estimate fibre orientation distributions from diffusion data using spherical deconvolution

### Example

    >>> import nipype.interfaces.mrtrix3 as mrt
    >>> fod = mrt.EstimateFOD()
    >>> fod.inputs.algorithm = 'csd'
    >>> fod.inputs.in_file = 'dwi.mif'
    >>> fod.inputs.wm_txt = 'wm.txt'
    >>> fod.inputs.grad_fsl = ('bvecs', 'bvals')
    >>> fod.cmdline
    'dwi2fod -fslgrad bvecs bvals csd dwi.mif wm.txt wm.mif'
    >>> fod.run()

Inputs:

    [Mandatory]
    algorithm: (u'csd' or u'msmt_csd')
            FOD algorithm
            flag: %s, position: -8
    in_file: (an existing file name)
            input DWI image
            flag: %s, position: -7
    wm_odf: (a file name, nipype default value: wm.mif)
            output WM ODF
            flag: %s, position: -5
    wm_txt: (a file name)
            WM response text file
            flag: %s, position: -6

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    bval_scale: (u'yes' or u'no')
            specifies whether the b - values should be scaled by the square of
            the corresponding DW gradient norm, as often required for multishell
            or DSI DW acquisition schemes. The default action can also be set in
            the MRtrix config file, under the BValueScaling entry. Valid choices
            are yes / no, true / false, 0 / 1 (default: true).
            flag: -bvalue_scaling %s
    csf_odf: (a file name)
            output CSF ODF
            flag: %s, position: -1
    csf_txt: (a file name)
            CSF response text file
            flag: %s, position: -2
    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
    gm_odf: (a file name)
            output GM ODF
            flag: %s, position: -3
    gm_txt: (a file name)
            GM response text file
            flag: %s, position: -4
    grad_file: (an existing file name)
            dw gradient scheme (MRTrix format
            flag: -grad %s
    grad_fsl: (a tuple of the form: (an existing file name, an existing
             file name))
            (bvecs, bvals) dw gradient scheme (FSL format
            flag: -fslgrad %s %s
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    in_bval: (an existing file name)
            bvals file in FSL format
    in_bvec: (an existing file name)
            bvecs file in FSL format
            flag: -fslgrad %s %s
    in_dirs: (an existing file name)
            specify the directions over which to apply the non-negativity
            constraint (by default, the built-in 300 direction set is used).
            These should be supplied as a text file containing the [ az el ]
            pairs for the directions.
            flag: -directions %s
    mask_file: (an existing file name)
            mask image
            flag: -mask %s
    max_sh: (an integer (int or long))
            maximum harmonic degree of response function
            flag: -lmax %d
    nthreads: (an integer (int or long))
            number of threads. if zero, the number of available cpus will be
            used
            flag: -nthreads %d
    shell: (a list of items which are a float)
            specify one or more dw gradient shells
            flag: -shell %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

Outputs:

    csf_odf: (a file name)
            output CSF ODF
            flag: %s
    gm_odf: (a file name)
            output GM ODF
            flag: %s
    wm_odf: (a file name)
            output WM ODF
            flag: %s

## FitTensor

Link to code

Wraps command dwi2tensor

Convert diffusion-weighted images to tensor images

### Example

    >>> import nipype.interfaces.mrtrix3 as mrt
    >>> tsr = mrt.FitTensor()
    >>> tsr.inputs.in_file = 'dwi.mif'
    >>> tsr.inputs.in_mask = 'mask.nii.gz'
    >>> tsr.inputs.grad_fsl = ('bvecs', 'bvals')
    >>> tsr.cmdline
    'dwi2tensor -fslgrad bvecs bvals -mask mask.nii.gz dwi.mif dti.mif'
    >>> tsr.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input diffusion weighted images
            flag: %s, position: -2
    out_file: (a file name, nipype default value: dti.mif)
            the output diffusion tensor image
            flag: %s, position: -1

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    bval_scale: (u'yes' or u'no')
            specifies whether the b - values should be scaled by the square of
            the corresponding DW gradient norm, as often required for multishell
            or DSI DW acquisition schemes. The default action can also be set in
            the MRtrix config file, under the BValueScaling entry. Valid choices
            are yes / no, true / false, 0 / 1 (default: true).
            flag: -bvalue_scaling %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
    grad_file: (an existing file name)
            dw gradient scheme (MRTrix format
            flag: -grad %s
    grad_fsl: (a tuple of the form: (an existing file name, an existing
             file name))
            (bvecs, bvals) dw gradient scheme (FSL format
            flag: -fslgrad %s %s
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    in_bval: (an existing file name)
            bvals file in FSL format
    in_bvec: (an existing file name)
            bvecs file in FSL format
            flag: -fslgrad %s %s
    in_mask: (an existing file name)
            only perform computation within the specified binary brain mask
            image
            flag: -mask %s
    method: (u'nonlinear' or u'loglinear' or u'sech' or u'rician')
            select method used to perform the fitting
            flag: -method %s
    nthreads: (an integer (int or long))
            number of threads. if zero, the number of available cpus will be
            used
            flag: -nthreads %d
    reg_term: (a float)
            specify the strength of the regularisation term on the magnitude of
            the tensor elements (default = 5000). This only applies to the non-
            linear methods
            flag: -regularisation %f
    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)
            the output DTI file