interfaces.mrtrix3.reconst¶
EstimateFOD¶
Wraps the executable 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
argument: ``%s``, position: -8
in_file: (an existing file name)
input DWI image
argument: ``%s``, position: -7
wm_txt: (a file name)
WM response text file
argument: ``%s``, position: -6
wm_odf: (a file name, nipype default value: wm.mif)
output WM ODF
argument: ``%s``, position: -5
[Optional]
gm_txt: (a file name)
GM response text file
argument: ``%s``, position: -4
gm_odf: (a file name, nipype default value: gm.mif)
output GM ODF
argument: ``%s``, position: -3
csf_txt: (a file name)
CSF response text file
argument: ``%s``, position: -2
csf_odf: (a file name, nipype default value: csf.mif)
output CSF ODF
argument: ``%s``, position: -1
mask_file: (an existing file name)
mask image
argument: ``-mask %s``
shell: (a list of items which are a float)
specify one or more dw gradient shells
argument: ``-shell %s``
max_sh: (an integer (int or long), nipype default value: 8)
maximum harmonic degree of response function
argument: ``-lmax %d``
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.
argument: ``-directions %s``
nthreads: (an integer (int or long))
number of threads. if zero, the number of available cpus will be
used
argument: ``-nthreads %d``
grad_file: (an existing file name)
dw gradient scheme (MRTrix format
argument: ``-grad %s``
grad_fsl: (a tuple of the form: (an existing file name, an existing
file name))
(bvecs, bvals) dw gradient scheme (FSL format
argument: ``-fslgrad %s %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).
argument: ``-bvalue_scaling %s``
in_bvec: (an existing file name)
bvecs file in FSL format
argument: ``-fslgrad %s %s``
in_bval: (an existing file name)
bvals file in FSL format
args: (a unicode string)
Additional parameters to the command
argument: ``%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
Outputs:
wm_odf: (a file name)
output WM ODF
argument: ``%s``
gm_odf: (a file name)
output GM ODF
argument: ``%s``
csf_odf: (a file name)
output CSF ODF
argument: ``%s``
FitTensor¶
Wraps the executable 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
argument: ``%s``, position: -2
out_file: (a file name, nipype default value: dti.mif)
the output diffusion tensor image
argument: ``%s``, position: -1
[Optional]
in_mask: (an existing file name)
only perform computation within the specified binary brain mask
image
argument: ``-mask %s``
method: ('nonlinear' or 'loglinear' or 'sech' or 'rician')
select method used to perform the fitting
argument: ``-method %s``
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
argument: ``-regularisation %f``
nthreads: (an integer (int or long))
number of threads. if zero, the number of available cpus will be
used
argument: ``-nthreads %d``
grad_file: (an existing file name)
dw gradient scheme (MRTrix format
argument: ``-grad %s``
grad_fsl: (a tuple of the form: (an existing file name, an existing
file name))
(bvecs, bvals) dw gradient scheme (FSL format
argument: ``-fslgrad %s %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).
argument: ``-bvalue_scaling %s``
in_bvec: (an existing file name)
bvecs file in FSL format
argument: ``-fslgrad %s %s``
in_bval: (an existing file name)
bvals file in FSL format
args: (a unicode string)
Additional parameters to the command
argument: ``%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
Outputs:
out_file: (an existing file name)
the output DTI file