interfaces.mrtrix3.reconst

EstimateFOD

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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 -lmax 8 csd dwi.mif wm.txt wm.mif gm.mif csf.mif'
>>> fod.run()                                 

Inputs:

[Mandatory]
algorithm: ('csd' or '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: ('yes' or '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, nipype default value: csf.mif)
        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 bytes or None or a value
         of class 'str' and with values which are a bytes or None or a value
         of class 'str', nipype default value: {})
        Environment variables
gm_odf: (a file name, nipype default value: gm.mif)
        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
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), nipype default value: 8)
        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

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

Outputs:

out_file: (an existing file name)
        the output DTI file