interfaces.dipy.tensors¶
DTI¶
Calculates the diffusion tensor model parameters
Example¶
>>> import nipype.interfaces.dipy as dipy
>>> dti = dipy.DTI()
>>> dti.inputs.in_file = 'diffusion.nii'
>>> dti.inputs.in_bvec = 'bvecs'
>>> dti.inputs.in_bval = 'bvals'
>>> dti.run()
Inputs:
[Mandatory]
in_bval: (an existing file name)
input b-values table
in_bvec: (an existing file name)
input b-vectors table
in_file: (an existing file name)
input diffusion data
[Optional]
b0_thres: (an integer (int or long), nipype default value: 700)
b0 threshold
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
mask_file: (an existing file name)
An optional white matter mask
out_prefix: (a unicode string)
output prefix for file names
Outputs:
ad_file: (an existing file name)
fa_file: (an existing file name)
md_file: (an existing file name)
out_file: (an existing file name)
rd_file: (an existing file name)
TensorMode¶
Creates a map of the mode of the diffusion tensors given a set of diffusion-weighted images, as well as their associated b-values and b-vectors. Fits the diffusion tensors and calculates tensor mode with Dipy.
[1] | Daniel B. Ennis and G. Kindlmann, “Orthogonal Tensor Invariants and the Analysis of Diffusion Tensor Magnetic Resonance Images”, Magnetic Resonance in Medicine, vol. 55, no. 1, pp. 136-146, 2006. |
Example¶
>>> import nipype.interfaces.dipy as dipy
>>> mode = dipy.TensorMode()
>>> mode.inputs.in_file = 'diffusion.nii'
>>> mode.inputs.in_bvec = 'bvecs'
>>> mode.inputs.in_bval = 'bvals'
>>> mode.run()
Inputs:
[Mandatory]
in_bval: (an existing file name)
input b-values table
in_bvec: (an existing file name)
input b-vectors table
in_file: (an existing file name)
input diffusion data
[Optional]
b0_thres: (an integer (int or long), nipype default value: 700)
b0 threshold
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
mask_file: (an existing file name)
An optional white matter mask
out_prefix: (a unicode string)
output prefix for file names
Outputs:
out_file: (an existing file name)