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# nipype.interfaces.dipy.tensors module

## DTI

Link to code

> Bases: DipyDiffusionInterface

> 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()

> in_bvala pathlike object or string representing an existing file

>   Input b-values table.

> in_bveca pathlike object or string representing an existing file

>   Input b-vectors table.

> in_filea pathlike object or string representing an existing file

>   Input diffusion data.

> b0_thresan integer

>   B0 threshold. (Nipype default value: `700`)

> mask_filea pathlike object or string representing an existing file

>   An optional white matter mask.

> out_prefixa string

>   Output prefix for file names.

> ad_file : a pathlike object or string representing an existing file color_fa_file : a pathlike object or string representing an existing file fa_file : a pathlike object or string representing an existing file md_file : a pathlike object or string representing an existing file out_file : a pathlike object or string representing an existing file rd_file : a pathlike object or string representing an existing file

## TensorMode

Link to code

> Bases: DipyDiffusionInterface

> 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 [1]. Fits the diffusion tensors and calculates tensor mode with Dipy.

> 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()

> References

> [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.

> in_bvala pathlike object or string representing an existing file

>   Input b-values table.

> in_bveca pathlike object or string representing an existing file

>   Input b-vectors table.

> in_filea pathlike object or string representing an existing file

>   Input diffusion data.

> b0_thresan integer

>   B0 threshold. (Nipype default value: `700`)

> mask_filea pathlike object or string representing an existing file

>   An optional white matter mask.

> out_prefixa string

>   Output prefix for file names.

> out_file : a pathlike object or string representing an existing file