:py:mod:`AFQ.tasks.data` ======================== .. py:module:: AFQ.tasks.data Module Contents --------------- Functions ~~~~~~~~~ .. autoapisummary:: AFQ.tasks.data.get_data_gtab AFQ.tasks.data.b0 AFQ.tasks.data.b0_mask AFQ.tasks.data.dti_fit AFQ.tasks.data.dti_params AFQ.tasks.data.fwdti_fit AFQ.tasks.data.fwdti_params AFQ.tasks.data.dki_fit AFQ.tasks.data.dki_params AFQ.tasks.data.msdki_fit AFQ.tasks.data.msdki_params AFQ.tasks.data.msdki_msd AFQ.tasks.data.msdki_msk AFQ.tasks.data.csd_params AFQ.tasks.data.anisotropic_power_map AFQ.tasks.data.csd_anisotropic_index AFQ.tasks.data.gq AFQ.tasks.data.gq_pmap AFQ.tasks.data.gq_ai AFQ.tasks.data.opdt_params AFQ.tasks.data.opdt_pmap AFQ.tasks.data.opdt_ai AFQ.tasks.data.csa_params AFQ.tasks.data.csa_pmap AFQ.tasks.data.csa_ai AFQ.tasks.data.fwdti_fa AFQ.tasks.data.fwdti_md AFQ.tasks.data.fwdti_fwf AFQ.tasks.data.dti_fa AFQ.tasks.data.dti_lt AFQ.tasks.data.dti_cfa AFQ.tasks.data.dti_pdd AFQ.tasks.data.dti_md AFQ.tasks.data.dti_ga AFQ.tasks.data.dti_rd AFQ.tasks.data.dti_ad AFQ.tasks.data.dki_kt AFQ.tasks.data.dki_lt AFQ.tasks.data.dki_fa AFQ.tasks.data.dki_md AFQ.tasks.data.dki_awf AFQ.tasks.data.dki_mk AFQ.tasks.data.dki_kfa AFQ.tasks.data.dki_ga AFQ.tasks.data.dki_rd AFQ.tasks.data.dki_ad AFQ.tasks.data.dki_rk AFQ.tasks.data.dki_ak AFQ.tasks.data.brain_mask AFQ.tasks.data.get_bundle_dict AFQ.tasks.data.get_data_plan Attributes ~~~~~~~~~~ .. autoapisummary:: AFQ.tasks.data.logger AFQ.tasks.data.DIPY_GH .. py:data:: logger .. py:data:: DIPY_GH :value: 'https://github.com/dipy/dipy/blob/master/dipy/' .. py:function:: get_data_gtab(dwi_path, bval, bvec, min_bval=None, max_bval=None, filter_b=True, b0_threshold=50) DWI data as an ndarray for selected b values, A DIPY GradientTable with all the gradient information, and DWI data in a Nifti1Image, and the affine transformation of the DWI data. :Parameters: **min_bval** : float, 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_bval** : float, 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_b** : bool, optional Whether to filter the DWI data based on min or max bvals. Default: True **b0_threshold** : int, optional The value of b under which it is considered to be b0. Default: 50. .. !! processed by numpydoc !! .. py:function:: b0(dwi_path, gtab) full path to a nifti file containing the mean b0 .. !! processed by numpydoc !! .. py:function:: b0_mask(b0, brain_mask) full path to a nifti file containing the mean b0 after applying the brain mask .. !! processed by numpydoc !! .. py:function:: dti_fit(dti_params, gtab) DTI TensorFit object .. !! processed by numpydoc !! .. py:function:: dti_params(brain_mask, data, gtab, bval, bvec, b0_threshold=50, robust_tensor_fitting=False) full path to a nifti file containing parameters for the DTI fit :Parameters: **robust_tensor_fitting** : bool, optional Whether to use robust_tensor_fitting when doing dti. Only applies to dti. Default: False **b0_threshold** : int, optional The value of b under which it is considered to be b0. Default: 50. .. !! processed by numpydoc !! .. py:function:: fwdti_fit(fwdti_params, gtab) Free-water DTI TensorFit object .. !! processed by numpydoc !! .. py:function:: fwdti_params(brain_mask, data, gtab) Full path to a nifti file containing parameters for the free-water DTI fit. .. !! processed by numpydoc !! .. py:function:: dki_fit(dki_params, gtab) DKI DiffusionKurtosisFit object .. !! processed by numpydoc !! .. py:function:: dki_params(brain_mask, gtab, data) full path to a nifti file containing parameters for the DKI fit .. !! processed by numpydoc !! .. py:function:: msdki_fit(msdki_params, gtab) Mean Signal DKI DiffusionKurtosisFit object .. !! processed by numpydoc !! .. py:function:: msdki_params(brain_mask, gtab, data) full path to a nifti file containing parameters for the Mean Signal DKI fit .. !! processed by numpydoc !! .. py:function:: msdki_msd(msdki_tf) full path to a nifti file containing the MSDKI mean signal diffusivity .. !! processed by numpydoc !! .. py:function:: msdki_msk(msdki_tf) full path to a nifti file containing the MSDKI mean signal kurtosis .. !! processed by numpydoc !! .. py:function:: csd_params(dwi, brain_mask, gtab, data, csd_response=None, csd_sh_order=None, csd_lambda_=1, csd_tau=0.1) full path to a nifti file containing parameters for the CSD fit :Parameters: **csd_response** : tuple 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_order** : int 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_tau** : float, 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 [R528d7c93b1c5-1]_). Default: 0.1 .. rubric:: References .. [R528d7c93b1c5-1] Tournier, J.D., et al. NeuroImage 2007. Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution .. only:: latex [R528d7c93b1c5-1]_ .. !! processed by numpydoc !! .. py:function:: anisotropic_power_map(csd_params) full path to a nifti file containing the anisotropic power map .. !! processed by numpydoc !! .. py:function:: csd_anisotropic_index(csd_params) full path to a nifti file containing the anisotropic index .. !! processed by numpydoc !! .. py:function:: gq(base_fname, gtab, dwi_affine, data, gq_sampling_length=1.2) full path to a nifti file containing parameters for the Generalized Q-Sampling shm_coeff, full path to a nifti file containing isotropic diffusion component, full path to a nifti file containing anisotropic diffusion component :Parameters: **gq_sampling_length** : float Diffusion sampling length. Default: 1.2 .. !! processed by numpydoc !! .. py:function:: gq_pmap(gq_params) full path to a nifti file containing the anisotropic power map from GQ .. !! processed by numpydoc !! .. py:function:: gq_ai(gq_params) full path to a nifti file containing the anisotropic index from GQ .. !! processed by numpydoc !! .. py:function:: opdt_params(base_fname, data, gtab, dwi_affine, brain_mask, opdt_sh_order=8) full path to a nifti file containing parameters for the Orientation Probability Density Transform shm_coeff, full path to a nifti file containing GFA :Parameters: **opdt_sh_order** : int Spherical harmonics order for OPDT model. Must be even. Default: 8 .. !! processed by numpydoc !! .. py:function:: opdt_pmap(opdt_params) full path to a nifti file containing the anisotropic power map from OPDT .. !! processed by numpydoc !! .. py:function:: opdt_ai(opdt_params) full path to a nifti file containing the anisotropic index from OPDT .. !! processed by numpydoc !! .. py:function:: csa_params(base_fname, data, gtab, dwi_affine, brain_mask, csa_sh_order=8) full path to a nifti file containing parameters for the Constant Solid Angle shm_coeff, full path to a nifti file containing GFA :Parameters: **csa_sh_order** : int Spherical harmonics order for CSA model. Must be even. Default: 8 .. !! processed by numpydoc !! .. py:function:: csa_pmap(csa_params) full path to a nifti file containing the anisotropic power map from CSA .. !! processed by numpydoc !! .. py:function:: csa_ai(csa_params) full path to a nifti file containing the anisotropic index from CSA .. !! processed by numpydoc !! .. py:function:: fwdti_fa(fwdti_tf) full path to a nifti file containing the Free-water DTI fractional anisotropy .. !! processed by numpydoc !! .. py:function:: fwdti_md(fwdti_tf) full path to a nifti file containing the Free-water DTI mean diffusivity .. !! processed by numpydoc !! .. py:function:: fwdti_fwf(fwdti_tf) full path to a nifti file containing the Free-water DTI free water fraction .. !! processed by numpydoc !! .. py:function:: dti_fa(dti_tf) full path to a nifti file containing the DTI fractional anisotropy .. !! processed by numpydoc !! .. py:function:: dti_lt(dti_tf, dwi_affine) Image of first element in the DTI tensor according to DIPY convention i.e. Dxx (rate of diffusion from the left to right side of the brain), Image of second element in the DTI tensor according to DIPY convention i.e. Dyy (rate of diffusion from the posterior to anterior part of the brain), Image of third element in the DTI tensor according to DIPY convention i.e. Dzz (rate of diffusion from the inferior to superior part of the brain), Image of fourth element in the DTI tensor according to DIPY convention i.e. Dxy (rate of diffusion in the xy plane indicating the relationship between the x and y directions), Image of fifth element in the DTI tensor according to DIPY convention i.e. Dxz (rate of diffusion in the xz plane indicating the relationship between the x and z directions), Image of sixth element in the DTI tensor according to DIPY convention i.e. Dyz (rate of diffusion in the yz plane indicating the relationship between the y and z directions) .. !! processed by numpydoc !! .. py:function:: dti_cfa(dti_tf) full path to a nifti file containing the DTI color fractional anisotropy .. !! processed by numpydoc !! .. py:function:: dti_pdd(dti_tf) full path to a nifti file containing the DTI principal diffusion direction .. !! processed by numpydoc !! .. py:function:: dti_md(dti_tf) full path to a nifti file containing the DTI mean diffusivity .. !! processed by numpydoc !! .. py:function:: dti_ga(dti_tf) full path to a nifti file containing the DTI geodesic anisotropy .. !! processed by numpydoc !! .. py:function:: dti_rd(dti_tf) full path to a nifti file containing the DTI radial diffusivity .. !! processed by numpydoc !! .. py:function:: dti_ad(dti_tf) full path to a nifti file containing the DTI axial diffusivity .. !! processed by numpydoc !! .. py:function:: dki_kt(dki_tf, dwi_affine) 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 twelfth 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 .. !! processed by numpydoc !! .. py:function:: dki_lt(dki_tf, dwi_affine) 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 .. !! processed by numpydoc !! .. py:function:: dki_fa(dki_tf) full path to a nifti file containing the DKI fractional anisotropy .. !! processed by numpydoc !! .. py:function:: dki_md(dki_tf) full path to a nifti file containing the DKI mean diffusivity .. !! processed by numpydoc !! .. py:function:: dki_awf(dki_params, sphere='repulsion100', gtol=0.01) full path to a nifti file containing the DKI axonal water fraction :Parameters: **sphere** : Sphere class instance, optional The sphere providing sample directions for the initial search of the maximal value of kurtosis. Default: 'repulsion100' **gtol** : float, 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 .. !! processed by numpydoc !! .. py:function:: dki_mk(dki_tf) full path to a nifti file containing the DKI mean kurtosis file .. !! processed by numpydoc !! .. py:function:: dki_kfa(dki_tf) full path to a nifti file containing the DKI kurtosis FA file .. rubric:: References .. [R146e7c91c7e8-Hansen2019] Hansen B. An Introduction to Kurtosis Fractional Anisotropy. AJNR Am J Neuroradiol. 2019 Oct;40(10):1638-1641. doi: 10.3174/ajnr.A6235. Epub 2019 Sep 26. PMID: 31558496; PMCID: PMC7028548. .. only:: latex [R146e7c91c7e8-Hansen2019]_ .. !! processed by numpydoc !! .. py:function:: dki_ga(dki_tf) full path to a nifti file containing the DKI geodesic anisotropy .. !! processed by numpydoc !! .. py:function:: dki_rd(dki_tf) full path to a nifti file containing the DKI radial diffusivity .. !! processed by numpydoc !! .. py:function:: dki_ad(dki_tf) full path to a nifti file containing the DKI axial diffusivity .. !! processed by numpydoc !! .. py:function:: dki_rk(dki_tf) full path to a nifti file containing the DKI radial kurtosis .. !! processed by numpydoc !! .. py:function:: dki_ak(dki_tf) full path to a nifti file containing the DKI axial kurtosis file .. !! processed by numpydoc !! .. py:function:: brain_mask(b0, brain_mask_definition=None) full path to a nifti file containing the brain mask :Parameters: **brain_mask_definition** : instance 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 .. !! processed by numpydoc !! .. py:function:: get_bundle_dict(segmentation_params, brain_mask, bids_info, b0, bundle_info=None, reg_template_spec='mni_T1') Dictionary defining the different bundles to be segmented, and a Nifti1Image containing the template for registration :Parameters: **bundle_info** : dict or BundleDict, optional A dictionary or BundleDict for use in segmentation. 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_spec** : str, 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" .. !! processed by numpydoc !! .. py:function:: get_data_plan(kwargs)