cmtklib.interfaces.dipy module

The Dipy module provides Nipype interfaces to the algorithms in dipy.

CSD

Link to code

Bases: nipype.interfaces.dipy.base.DipyDiffusionInterface

Uses CSD [Tournier2007] to generate the fODF of DWIs.

The interface uses dipy, as explained in dipy’s CSD example.

References

Tournier2007

Tournier, J.D., et al. NeuroImage 2007. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution

Example

>>> from cmtklib.interfaces.dipy import CSD
>>> csd = CSD()
>>> csd.inputs.in_file = '4d_dwi.nii'
>>> csd.inputs.in_bval = 'bvals'
>>> csd.inputs.in_bvec = 'bvecs'
>>> res = csd.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)

fa_thresha float

FA threshold used for response estimation. (Nipype default value: 0.7)

in_maska pathlike object or string representing an existing file

Input mask in which compute tensors.

out_fodsa pathlike object or string representing a file

FODFs output file name.

out_prefixa string

Output prefix for file names.

out_shm_coeffa pathlike object or string representing a file

Spherical Harmonics Coefficients output file name.

responsea pathlike object or string representing an existing file

Single fiber estimated response.

save_fodsa boolean

Save fODFs in file. (Nipype default value: True)

save_shm_coeffa boolean

Save Spherical Harmonics Coefficients in file. (Nipype default value: True)

sh_orderan integer

Maximal shperical harmonics order. (Nipype default value: 8)

tracking_processing_tool : ‘mrtrix’ or ‘dipy’

modela pathlike object or string representing a file

Python pickled object of the CSD model fitted.

out_fodsa pathlike object or string representing a file

FODFs output file name.

out_shm_coeffa pathlike object or string representing a file

Spherical Harmonics Coefficients output file name.

DTIEstimateResponseSH

Link to code

Bases: nipype.interfaces.dipy.base.DipyDiffusionInterface

Uses dipy to compute the single fiber response to be used by spherical deconvolution methods.

The single fiber response is computed in a similar way to MRTrix’s command estimate_response.

Example

>>> from cmtklib.interfaces.dipy import DTIEstimateResponseSH
>>> dti = DTIEstimateResponseSH()
>>> dti.inputs.in_file = '4d_dwi.nii'
>>> dti.inputs.in_bval = 'bvals'
>>> dti.inputs.in_bvec = 'bvecs'
>>> res = 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.

autoa boolean

Use the auto_response estimator from dipy. Mutually exclusive with inputs: recursive.

b0_thresan integer

B0 threshold. (Nipype default value: 700)

fa_thresha float

FA threshold. (Nipype default value: 0.7)

in_maska pathlike object or string representing an existing file

Input mask in which we find single fibers.

out_maska pathlike object or string representing a file

Computed wm mask. (Nipype default value: wm_mask.nii.gz)

out_prefixa string

Output prefix for file names.

recursivea boolean

Use the recursive response estimator from dipy. Mutually exclusive with inputs: auto.

responsea pathlike object or string representing a file

The output response file. (Nipype default value: response.txt)

roi_radiusan integer

ROI radius to be used in auto_response. (Nipype default value: 10)

ad_file : a pathlike object or string representing an existing file dti_model : a pathlike object or string representing an existing file

DTI model object.

fa_file : a pathlike object or string representing an existing file md_file : a pathlike object or string representing an existing file out_mask : a pathlike object or string representing an existing file

Output wm mask.

rd_file : a pathlike object or string representing an existing file response : a pathlike object or string representing an existing file

The response file.

DirectionGetterTractography

Link to code

Bases: nipype.interfaces.dipy.base.DipyBaseInterface

Streamline tractography using Dipy Deterministic Maximum Direction Getter.

Example

>>> from cmtklib.interfaces import dipy as ndp
>>> track = ndp.DirectionGetterTractography()
>>> track.inputs.in_file = '4d_dwi.nii'
>>> track.inputs.in_model = 'model.pklz'
>>> track.inputs.tracking_mask = 'dilated_wm_mask.nii'
>>> res = track.run() 
fa_thresha float

FA threshold to build the tissue classifier. (Nipype default value: 0.2)

in_faa pathlike object or string representing an existing file

Input FA.

in_filea pathlike object or string representing an existing file

Input diffusion data.

in_modela pathlike object or string representing an existing file

Input f/d-ODF model extracted from.

max_anglea float

Maximum angle. (Nipype default value: 25.0)

multiprocessa boolean

Use multiprocessing. (Nipype default value: True)

num_seedsan integer

Desired number of tracks in tractography. (Nipype default value: 10000)

save_seedsa boolean

Save seeding voxels coordinates. (Nipype default value: False)

seed_maska list of items which are a pathlike object or string representing an existing file

ROI files registered to diffusion space.

step_sizea float

Step size. (Nipype default value: 0.5)

tracking_maska pathlike object or string representing an existing file

Input mask within which perform tracking.

algo‘deterministic’ or ‘probabilistic’

Use either deterministic maximum (default) or probabilistic direction getter tractography. (Nipype default value: deterministic)

fod_filea pathlike object or string representing an existing file

Input fod file (if SHORE).

gmwmi_filea pathlike object or string representing an existing file

Input Gray Matter / White Matter interface image.

in_partial_volume_filesa list of items which are a pathlike object or string representing an existing file

Partial volume estimation result files (required if performing ACT).

out_prefixa string

Output prefix for file names.

recon_model‘CSD’ or ‘SHORE’

Use either fODFs from CSD (default) or SHORE models. (Nipype default value: CSD)

recon_orderan integer

Spherical harmonics order.

seed_densitya float

Density of seeds. (Nipype default value: 1)

seed_from_gmwmia boolean

Seed from the Gray Matter / White Matter interface.

use_acta boolean

Use FAST for partial volume estimation and Anatomically-Constrained Tractography (ACT) tissue classifier.

out_seedsa pathlike object or string representing a file

File containing the (N,3) voxel coordinates used in seeding.

streamlinesa pathlike object or string representing a file

Numpy array of streamlines.

tracksa pathlike object or string representing a file

TrackVis file containing extracted streamlines.

tracks2a pathlike object or string representing a file

TrackVis file containing extracted streamlines.

tracks3a pathlike object or string representing a file

TrackVis file containing extracted streamlines.

MAPMRI

Link to code

Bases: nipype.interfaces.dipy.base.DipyDiffusionInterface

Computes the MAP MRI model.

for reference on the settings

Example

>>> from cmtklib.interfaces.dipy import MAPMRI
>>> mapmri = MAPMRI()
>>> mapmri.inputs.in_file = '4d_dwi.nii'
>>> mapmri.inputs.in_bval = 'bvals'
>>> mapmri.inputs.in_bvec = 'bvecs'
>>> res = mapmri.run() 
big_deltaa float

Small data for gradient table.

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.

small_deltaa float

Small data for gradient table.

b0_thresan integer

B0 threshold. (Nipype default value: 700)

laplacian_regularizationa boolean

Apply laplacian regularization. (Nipype default value: True)

laplacian_weightinga float

Regularization weight. (Nipype default value: 0.05)

out_prefixa string

Output prefix for file names.

positivity_constrainta boolean

Apply positivity constraint. (Nipype default value: True)

radial_orderan integer

Maximal shperical harmonics order. (Nipype default value: 8)

modela pathlike object or string representing a file

Python pickled object of the MAP-MRI model fitted.

msd_filea pathlike object or string representing a file

Msd output file name.

ng_filea pathlike object or string representing a file

Ng output file name.

ng_para_filea pathlike object or string representing a file

Ng parallel output file name.

ng_perp_filea pathlike object or string representing a file

Ng perpendicular output file name.

qiv_filea pathlike object or string representing a file

Qiv output file name.

rtap_filea pathlike object or string representing a file

Rtap output file name.

rtop_filea pathlike object or string representing a file

Rtop output file name.

rtpp_filea pathlike object or string representing a file

Rtpp output file name.

SHORE

Link to code

Bases: nipype.interfaces.dipy.base.DipyDiffusionInterface

Uses SHORE [Merlet13] to generate the fODF of DWIs.

The interface uses dipy, as explained in dipy’s SHORE example.

References

Merlet2013

Merlet S. et. al, Medical Image Analysis, 2013.

“Continuous diffusion signal, EAP and ODF estimation via Compressive Sensing in diffusion MRI”

Example

>>> from cmtklib.interfaces.dipy import SHORE
>>> asm = SHORE(radial_order=6,zeta=700, lambda_n=1e-8, lambda_l=1e-8)
>>> asm.inputs.in_file = '4d_dwi.nii'
>>> asm.inputs.in_bval = 'bvals'
>>> asm.inputs.in_bvec = 'bvecs'
>>> res = asm.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)

constrain_e0a boolean

Constrain the optimization such that E(0) = 1. (Nipype default value: False)

in_maska pathlike object or string representing an existing file

Input mask in which compute SHORE solution.

lambda_la float

Angular regularisation constant. (Nipype default value: 1e-08)

lambda_na float

Radial regularisation constant. (Nipype default value: 1e-08)

out_prefixa string

Output prefix for file names.

positive_constrainta boolean

Constrain the optimization such that E(0) = 1. (Nipype default value: False)

radial_orderan integer

Even number that represents the order of the basis. (Nipype default value: 6)

responsea pathlike object or string representing an existing file

Single fiber estimated response.

taua float

Diffusion time. By default the value that makes q equal to the square root of the b-value.

tracking_processing_tool : ‘mrtrix’ or ‘dipy’ zeta : an integer

Scale factor. (Nipype default value: 700)

GFAa pathlike object or string representing a file

Generalized Fractional Anisotropy output file name.

MSDa pathlike object or string representing a file

Mean Square Displacement output file name.

RTOPa pathlike object or string representing a file

Return To Origin Probability output file name.

dodfa pathlike object or string representing a file

Fiber Orientation Distribution Function output file name.

fodfa pathlike object or string representing a file

Fiber Orientation Distribution Function output file name.

modela pathlike object or string representing a file

Python pickled object of the SHORE model fitted.

TensorInformedEudXTractography

Link to code

Bases: nipype.interfaces.dipy.base.DipyBaseInterface

Streamline tractography using Dipy Deterministic Maximum Direction Getter.

Example

>>> from cmtklib.interfaces import dipy as ndp
>>> track = ndp.TensorInformedEudXTractography()
>>> track.inputs.in_file = '4d_dwi.nii'
>>> track.inputs.in_model = 'model.pklz'
>>> track.inputs.tracking_mask = 'dilated_wm_mask.nii'
>>> res = track.run() 
fa_thresha float

FA threshold to build the tissue classifier. (Nipype default value: 0.2)

in_faa pathlike object or string representing an existing file

Input FA.

in_filea pathlike object or string representing an existing file

Input diffusion data.

in_modela pathlike object or string representing an existing file

Input Tensor model extracted from.

max_anglea float

Maximum angle. (Nipype default value: 25.0)

multiprocessa boolean

Use multiprocessing. (Nipype default value: True)

num_seedsan integer

Desired number of tracks in tractography. (Nipype default value: 10000)

save_seedsa boolean

Save seeding voxels coordinates. (Nipype default value: False)

seed_maska list of items which are a pathlike object or string representing an existing file

ROI files registered to diffusion space.

step_sizea float

Step size. (Nipype default value: 0.5)

tracking_maska pathlike object or string representing an existing file

Input mask within which perform tracking.

out_prefixa string

Output prefix for file names.

out_seedsa pathlike object or string representing a file

File containing the (N,3) voxel coordinates used in seeding.

tracksa pathlike object or string representing a file

TrackVis file containing extracted streamlines.