interfaces.mrtrix3.reconst¶
EstimateFOD¶
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 # doctest: +ELLIPSIS
'dwi2fod -fslgrad bvecs bvals -lmax 8 csd dwi.mif wm.txt wm.mif gm.mif csf.mif'
>>> fod.run() # doctest: +SKIP
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¶
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 # doctest: +ELLIPSIS
'dwi2tensor -fslgrad bvecs bvals -mask mask.nii.gz dwi.mif dti.mif'
>>> tsr.run() # doctest: +SKIP
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