AFQ.tasks.data#
Module Contents#
Functions#
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DWI data as an ndarray for selected b values, |
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full path to a nifti file containing the mean b0 |
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full path to a nifti file containing the |
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DTI TensorFit object |
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full path to a nifti file containing parameters |
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Free-water DTI TensorFit object |
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Full path to a nifti file containing parameters |
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DKI DiffusionKurtosisFit object |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing the Free-water DTI fractional |
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full path to a nifti file containing the Free-water DTI mean diffusivity |
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full path to a nifti file containing the Free-water DTI free water fraction |
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full path to a nifti file containing |
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Image of first element in the DTI tensor, |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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Image of first element in the DKI kurtosis model, |
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Image of first element in the DTI tensor from DKI, |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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full path to a nifti file containing |
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Dictionary defining the different bundles to be segmented, |
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Attributes#
- AFQ.tasks.data.get_data_gtab(dwi, bval, bvec, min_bval=None, max_bval=None, filter_b=True, b0_threshold=50)[source]#
DWI data as an ndarray for selected b values, A DIPY GradientTable with all the gradient information, and unaltered DWI data in a Nifti1Image.
- Parameters
- min_bvalfloat, optional
Minimum b value you want to use from the dataset (other than b0), inclusive. If None, there is no minimum limit. Default: None
- max_bvalfloat, optional
Maximum b value you want to use from the dataset (other than b0), inclusive. If None, there is no maximum limit. Default: None
- filter_bbool, optional
Whether to filter the DWI data based on min or max bvals. Default: True
- b0_thresholdint, optional
The value of b under which it is considered to be b0. Default: 50.
- AFQ.tasks.data.b0_mask(base_fname, b0, brain_mask)[source]#
full path to a nifti file containing the mean b0 after applying the brain mask
- AFQ.tasks.data.dti(brain_mask, data, gtab, bval, bvec, b0_threshold=50, robust_tensor_fitting=False)[source]#
full path to a nifti file containing parameters for the DTI fit
- Parameters
- robust_tensor_fittingbool, optional
Whether to use robust_tensor_fitting when doing dti. Only applies to dti. Default: False
- b0_thresholdint, optional
The value of b under which it is considered to be b0. Default: 50.
- AFQ.tasks.data.fwdti(brain_mask, data, gtab)[source]#
Full path to a nifti file containing parameters for the free-water DTI fit.
- AFQ.tasks.data.dki(brain_mask, gtab, data)[source]#
full path to a nifti file containing parameters for the DKI fit
- AFQ.tasks.data.csd(dwi, brain_mask, gtab, data, csd_response=None, csd_sh_order=None, csd_lambda_=1, csd_tau=0.1)[source]#
full path to a nifti file containing parameters for the CSD fit
- Parameters
- csd_responsetuple or None, optional.
The response function to be used by CSD, as a tuple with two elements. The first is the eigen-values as an (3,) ndarray and the second is the signal value for the response function without diffusion-weighting (i.e. S0). If not provided, auto_response will be used to calculate these values. Default: None
- csd_sh_orderint or None, optional.
default: infer the number of parameters from the number of data volumes, but no larger than 8. Default: None
- csd_lambda_float, optional.
weight given to the constrained-positivity regularization part of the deconvolution equation. Default: 1
- csd_taufloat, optional.
threshold controlling the amplitude below which the corresponding fODF is assumed to be zero. Ideally, tau should be set to zero. However, to improve the stability of the algorithm, tau is set to tau*100 percent of the mean fODF amplitude (here, 10 percent by default) (see [1]). Default: 0.1
References
- 1
Tournier, J.D., et al. NeuroImage 2007. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution
- AFQ.tasks.data.anisotropic_power_map(csd_params)[source]#
full path to a nifti file containing the anisotropic power map
- AFQ.tasks.data.fwdti_fa(fwdti_tf)[source]#
full path to a nifti file containing the Free-water DTI fractional anisotropy
- AFQ.tasks.data.fwdti_md(fwdti_tf)[source]#
full path to a nifti file containing the Free-water DTI mean diffusivity
- AFQ.tasks.data.fwdti_fwf(fwdti_tf)[source]#
full path to a nifti file containing the Free-water DTI free water fraction
- AFQ.tasks.data.dti_fa(dti_tf)[source]#
full path to a nifti file containing the DTI fractional anisotropy
- AFQ.tasks.data.dti_lt(dti_tf, dwi_affine)[source]#
Image of first element in the DTI tensor, Image of second element in the DTI tensor, Image of third element in the DTI tensor, Image of fourth element in the DTI tensor, Image of fifth element in the DTI tensor, Image of sixth element in the DTI tensor
- AFQ.tasks.data.dti_cfa(dti_tf)[source]#
full path to a nifti file containing the DTI color fractional anisotropy
- AFQ.tasks.data.dti_pdd(dti_tf)[source]#
full path to a nifti file containing the DTI principal diffusion direction
- AFQ.tasks.data.dti_md(dti_tf)[source]#
full path to a nifti file containing the DTI mean diffusivity
- AFQ.tasks.data.dti_ga(dti_tf)[source]#
full path to a nifti file containing the DTI geodesic anisotropy
- AFQ.tasks.data.dti_rd(dti_tf)[source]#
full path to a nifti file containing the DTI radial diffusivity
- AFQ.tasks.data.dti_ad(dti_tf)[source]#
full path to a nifti file containing the DTI axial diffusivity
- AFQ.tasks.data.dki_kt(dki_tf, dwi_affine)[source]#
Image of first element in the DKI kurtosis model, Image of second element in the DKI kurtosis model, Image of third element in the DKI kurtosis model, Image of fourth element in the DKI kurtosis model, Image of fifth element in the DKI kurtosis model, Image of sixth element in the DKI kurtosis model, Image of seventh element in the DKI kurtosis model, Image of eighth element in the DKI kurtosis model, Image of ninth element in the DKI kurtosis model, Image of tenth element in the DKI kurtosis model, Image of eleventh element in the DKI kurtosis model, Image of twelf element in the DKI kurtosis model, Image of thirteenth element in the DKI kurtosis model, Image of fourteenth element in the DKI kurtosis model, Image of fifteenth element in the DKI kurtosis model
- AFQ.tasks.data.dki_lt(dki_tf, dwi_affine)[source]#
Image of first element in the DTI tensor from DKI, Image of second element in the DTI tensor from DKI, Image of third element in the DTI tensor from DKI, Image of fourth element in the DTI tensor from DKI, Image of fifth element in the DTI tensor from DKI, Image of sixth element in the DTI tensor from DKI
- AFQ.tasks.data.dki_fa(dki_tf)[source]#
full path to a nifti file containing the DKI fractional anisotropy
- AFQ.tasks.data.dki_md(dki_tf)[source]#
full path to a nifti file containing the DKI mean diffusivity
- AFQ.tasks.data.dki_awf(dki_params, sphere='repulsion100', gtol=0.01)[source]#
full path to a nifti file containing the DKI axonal water fraction
- Parameters
- sphereSphere class instance, optional
The sphere providing sample directions for the initial search of the maximal value of kurtosis. Default: ‘repulsion100’
- gtolfloat, optional
This input is to refine kurtosis maxima under the precision of the directions sampled on the sphere class instance. The gradient of the convergence procedure must be less than gtol before successful termination. If gtol is None, fiber direction is directly taken from the initial sampled directions of the given sphere object. Default: 1e-2
- AFQ.tasks.data.dki_mk(dki_tf)[source]#
full path to a nifti file containing the DKI mean kurtosis file
- AFQ.tasks.data.dki_ga(dki_tf)[source]#
full path to a nifti file containing the DKI geodesic anisotropy
- AFQ.tasks.data.dki_rd(dki_tf)[source]#
full path to a nifti file containing the DKI radial diffusivity
- AFQ.tasks.data.dki_ad(dki_tf)[source]#
full path to a nifti file containing the DKI axial diffusivity
- AFQ.tasks.data.dki_ak(dki_tf)[source]#
full path to a nifti file containing the DKI axial kurtosis file
- AFQ.tasks.data.brain_mask(base_fname, dwi, b0, bids_info, brain_mask_definition=None)[source]#
full path to a nifti file containing the brain mask
- Parameters
- brain_mask_definitioninstance from AFQ.definitions.image, optional
This will be used to create the brain mask, which gets applied before registration to a template. If you want no brain mask to be applied, use FullImage. If None, use B0Image() Default: None
- AFQ.tasks.data.get_bundle_dict(base_fname, dwi, segmentation_params, brain_mask, bids_info, b0, bundle_info=None, reg_template_spec='mni_T1')[source]#
Dictionary defining the different bundles to be segmented, and a Nifti1Image containing the template for registration
- Parameters
- bundle_infolist of strings, dict, or BundleDict, optional
List of bundle names to include in segmentation, or a bundle dictionary (see BundleDict for inspiration), or a BundleDict. See Defining Custom Bundle Dictionaries in the usage section of pyAFQ’s documentation for details. If None, will get all appropriate bundles for the chosen segmentation algorithm. Default: None
- reg_template_specstr, or Nifti1Image, optional
The target image data for registration. Can either be a Nifti1Image, a path to a Nifti1Image, or if “mni_T2”, “dti_fa_template”, “hcp_atlas”, or “mni_T1”, image data will be loaded automatically. If “hcp_atlas” is used, slr registration will be used and reg_subject should be “subject_sls”. Default: “mni_T1”