workflows

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, new_vox_size, order=1, mode='constant', cval=0, num_processes=1, out_dir='', out_resliced='resliced.nii.gz')

Reslice data with new voxel resolution defined by new_vox_sz

Parameters

input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

new_vox_sizevariable float

new voxel size.

orderint, optional

order of interpolation, from 0 to 5, for resampling/reslicing, 0 nearest interpolation, 1 trilinear etc.. if you don’t want any smoothing 0 is the option you need.

modestring, optional

Points outside the boundaries of the input are filled according to the given mode ‘constant’, ‘nearest’, ‘reflect’ or ‘wrap’.

cvalfloat, optional

Value used for points outside the boundaries of the input if mode=’constant’.

num_processesint, optional

Split the calculation to a pool of children processes. This only applies to 4D data arrays. Default is 1. If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

out_dirstring, optional

Output directory. (default current directory)

out_reslicedstring, optional

Name of the resliced dataset to be saved.

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(static_files, moving_files, x0='affine', rm_small_clusters=50, qbx_thr=(40, 30, 20, 15), num_threads=None, greater_than=50, less_than=250, nb_pts=20, progressive=True, out_dir='', out_moved='moved.trk', out_affine='affine.txt', out_stat_centroids='static_centroids.trk', out_moving_centroids='moving_centroids.trk', out_moved_centroids='moved_centroids.trk')

Streamline-based linear registration.

For efficiency we apply the registration on cluster centroids and remove small clusters.

Parameters

static_files : string moving_files : string x0 : string, optional

rigid, similarity or affine transformation model.

rm_small_clustersint, optional

Remove clusters that have less than rm_small_clusters.

qbx_thrvariable int, optional

Thresholds for QuickBundlesX.

num_threadsint, optional

Number of threads to be used for OpenMP parallelization. If None (default) the value of OMP_NUM_THREADS environment variable is used if it is set, otherwise all available threads are used. If < 0 the maximal number of threads minus |num_threads + 1| is used (enter -1 to use as many threads as possible). 0 raises an error. Only metrics using OpenMP will use this variable.

greater_thanint, optional

Keep streamlines that have length greater than this value.

less_thanint, optional

Keep streamlines have length less than this value.

nb_ptsint, optional

Number of points for discretizing each streamline.

progressive : boolean, optional out_dir : string, optional

Output directory. (default current directory)

out_movedstring, optional

Filename of moved tractogram.

out_affinestring, optional

Filename of affine for SLR transformation.

out_stat_centroidsstring, optional

Filename of static centroids.

out_moving_centroidsstring, optional

Filename of moving centroids.

out_moved_centroidsstring, optional

Filename of moved centroids.

Notes

The order of operations is the following. First short or long streamlines are removed. Second the tractogram or a random selection of the tractogram is clustered with QuickBundlesX. Then SLR [Garyfallidis15] is applied.

References

[Garyfallidis15]

Garyfallidis et al. “Robust and efficient linear

registration of white-matter fascicles in the space of streamlines”, NeuroImage, 117, 124–140, 2015

[Garyfallidis14]

Garyfallidis et al., “Direct native-space fiber

bundle alignment for group comparisons”, ISMRM, 2014.

[Garyfallidis17]

Garyfallidis et al. Recognition of white matter

bundles using local and global streamline-based registration and clustering, NeuroImage, 2017.

Bases: Workflow

The registration workflow allows the user to use only one type of registration (such as center of mass or rigid body registration only).

Alternatively, a registration can be done in a progressive manner. For example, using affine registration with progressive set to ‘True’ will involve center of mass, translation, rigid body and full affine registration. Whereas, when progressive is False the registration will include only center of mass and affine registration. The progressive registration will be slower but will improve the quality.

This can be controlled by using the progressive flag (True by default).

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

run(static_image_files, moving_image_files, transform='affine', nbins=32, sampling_prop=None, metric='mi', level_iters=(10000, 1000, 100), sigmas=(3.0, 1.0, 0.0), factors=(4, 2, 1), progressive=True, save_metric=False, out_dir='', out_moved='moved.nii.gz', out_affine='affine.txt', out_quality='quality_metric.txt')

Parameters

static_image_filesstring

Path to the static image file.

moving_image_filesstring

Path to the moving image file.

transformstring, optional

com: center of mass, trans: translation, rigid: rigid body, rigid_isoscaling: rigid body + isotropic scaling, rigid_scaling: rigid body + scaling, affine: full affine including translation, rotation, shearing and scaling.

nbinsint, optional

Number of bins to discretize the joint and marginal PDF.

sampling_propint, optional

Number ([0-100]) of voxels for calculating the PDF.

‘None’ implies all voxels.

metricstring, optional

Similarity metric for gathering mutual information).

level_itersvariable int, optional

The number of iterations at each scale of the scale space.

level_iters[0] corresponds to the coarsest scale, level_iters[-1] the finest, where n is the length of the

sequence.

sigmasvariable floats, optional

Custom smoothing parameter to build the scale space (one parameter

for each scale).

factorsvariable floats, optional

Custom scale factors to build the scale space (one factor for each

scale).

progressiveboolean, optional

Enable/Disable the progressive registration.

save_metricboolean, optional

If true, quality assessment metric are saved in ‘quality_metric.txt’.

out_dirstring, optional

Directory to save the transformed image and the affine matrix

(default current directory).

out_movedstring, optional

Name for the saved transformed image.

out_affinestring, optional

Name for the saved affine matrix.

out_qualitystring, optional

Name of the file containing the saved quality

metric.

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

run(static_image_files, moving_image_files, transform_map_file, transform_type='affine', out_dir='', out_file='transformed.nii.gz')

Parameters

static_image_filesstring

Path of the static image file.

moving_image_filesstring

Path of the moving image(s). It can be a single image or a folder containing multiple images.

transform_map_filestring

For the affine case, it should be a text(*.txt) file containing the affine matrix. For the diffeomorphic case, it should be a nifti file containing the mapping displacement field in each voxel with this shape (x, y, z, 3, 2).

transform_typestring, optional

Select the transformation type to apply between ‘affine’ or ‘diffeomorphic’.

out_dirstring, optional

Directory to save the transformed files (default current directory).

out_filestring, optional

Name of the transformed file. It is recommended to use the flag –mix-names to prevent the output files from being overwritten.

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

run(static_image_files, moving_image_files, prealign_file='', inv_static=False, level_iters=(10, 10, 5), metric='cc', mopt_sigma_diff=2.0, mopt_radius=4, mopt_smooth=0.0, mopt_inner_iter=0, mopt_q_levels=256, mopt_double_gradient=True, mopt_step_type='', step_length=0.25, ss_sigma_factor=0.2, opt_tol=1e-05, inv_iter=20, inv_tol=0.001, out_dir='', out_warped='warped_moved.nii.gz', out_inv_static='inc_static.nii.gz', out_field='displacement_field.nii.gz')

Parameters

static_image_filesstring

Path of the static image file.

moving_image_filesstring

Path to the moving image file.

prealign_filestring, optional

The text file containing pre alignment information via an

affine matrix.

inv_staticboolean, optional

Apply the inverse mapping to the static image.

level_itersvariable int, optional

The number of iterations at each level of the gaussian pyramid.

metricstring, optional

The metric to be used. metric available: cc (Cross Correlation), ssd (Sum Squared Difference), em (Expectation-Maximization).

mopt_sigma_difffloat, optional

Metric option applied on Cross correlation (CC). The standard deviation of the Gaussian smoothing kernel to be applied to the update field at each iteration.

mopt_radiusint, optional

Metric option applied on Cross correlation (CC). the radius of the squared (cubic) neighborhood at each voxel to be considered to compute the cross correlation.

mopt_smoothfloat, optional

Metric option applied on Sum Squared Difference (SSD) and Expectation Maximization (EM). Smoothness parameter, the larger the value the smoother the deformation field. (default 1.0 for EM, 4.0 for SSD)

mopt_inner_iterint, optional

Metric option applied on Sum Squared Difference (SSD) and Expectation Maximization (EM). This is number of iterations to be performed at each level of the multi-resolution Gauss-Seidel optimization algorithm (this is not the number of steps per Gaussian Pyramid level, that parameter must be set for the optimizer, not the metric). Default 5 for EM, 10 for SSD.

mopt_q_levelsint, optional

Metric option applied on Expectation Maximization (EM). Number of quantization levels (Default: 256 for EM)

mopt_double_gradientbool, optional

Metric option applied on Expectation Maximization (EM). if True, the gradient of the expected static image under the moving modality will be added to the gradient of the moving image, similarly, the gradient of the expected moving image under the static modality will be added to the gradient of the static image.

mopt_step_typestring, optional

Metric option applied on Sum Squared Difference (SSD) and Expectation Maximization (EM). The optimization schedule to be used in the multi-resolution Gauss-Seidel optimization algorithm (not used if Demons Step is selected). Possible value: (‘gauss_newton’, ‘demons’). default: ‘gauss_newton’ for EM, ‘demons’ for SSD.

step_lengthfloat, optional

the length of the maximum displacement vector of the update

displacement field at each iteration.

ss_sigma_factorfloat, optional

parameter of the scale-space smoothing kernel. For example, the

std. dev. of the kernel will be factor*(2^i) in the isotropic case where i = 0, 1, …, n_scales is the scale.

opt_tolfloat, optional

the optimization will stop when the estimated derivative of the

energy profile w.r.t. time falls below this threshold.

inv_iterint, optional

the number of iterations to be performed by the displacement field

inversion algorithm.

inv_tolfloat, optional

the displacement field inversion algorithm will stop iterating

when the inversion error falls below this threshold.

out_dirstring, optional

Directory to save the transformed files (default current directory).

out_warpedstring, optional

Name of the warped file.

out_inv_staticstring, optional

Name of the file to save the static image after applying the

inverse mapping.

out_fieldstring, optional

Name of the file to save the diffeomorphic map.

Bases: Workflow

The Motion Correction workflow allows the user to align between-volumes DWI dataset.

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

run(input_files, bvalues_files, bvectors_files, b0_threshold=50, bvecs_tol=0.01, out_dir='', out_moved='moved.nii.gz', out_affine='affine.txt')

Parameters

input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Threshold used to check that norm(bvec) = 1 +/- bvecs_tol b-vectors are unit vectors

out_dirstring, optional

Directory to save the transformed image and the affine matrix

(default current directory).

out_movedstring, optional

Name for the saved transformed image.

out_affinestring, optional

Name for the saved affine matrix.

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(static_file, moving_file, dist=None, alpha=0.3, beta=20, max_iter=15, affine=True, out_dir='', out_linear_moved='linearly_moved.trk', out_nonlinear_moved='nonlinearly_moved.trk', out_warp_transform='warp_transform.npy', out_warp_kernel='warp_kernel.npy', out_dist='distance_matrix.npy', out_matched_pairs='matched_pairs.npy')

BundleWarp: streamline-based nonlinear registration.

BundleWarp is nonrigid registration method for deformable registration of white matter tracts.

Parameters

static_filestring

Path to the static (reference) .trk file.

moving_filestring

Path to the moving (target to be registered) .trk file.

diststring, optional

Path to the precalculated distance matrix file.

alphafloat, optional

Represents the trade-off between regularizing the deformation and having points match very closely. Lower value of alpha means high deformations. It is represented with λ in BundleWarp paper. NOTE: setting alpha<=0.01 will result in highly deformable registration that could extremely modify the original anatomy of the moving bundle. (default 0.3)

betaint, optional

Represents the strength of the interaction between points Gaussian kernel size. (default 20)

max_iterint, optional

Maximum number of iterations for deformation process in ml-CPD method. (default 15)

affineboolean, optional

If False, use rigid registration as starting point. (default True)

out_dirstring, optional

Output directory. (default current directory)

out_linear_movedstring, optional

Filename of linearly moved bundle.

out_nonlinear_movedstring, optional

Filename of nonlinearly moved (warped) bundle.

out_warp_transformstring, optional

Filename of warp transformations generated by BundleWarp.

out_warp_kernelstring, optional

Filename of regularization gaussian kernel generated by BundleWarp.

out_diststring, optional

Filename of MDF distance matrix.

out_matched_pairsstring, optional

Filename of matched pairs; treamline correspondences between two bundles.

References

[Chandio2023]

Chandio et al. “BundleWarp, streamline-based nonlinear registration of white matter tracts.” bioRxiv (2023): 2023-01.

Bases: ArgumentParser

__init__(prog=None, usage=None, description=None, epilog=None, parents=(), formatter_class=<class 'argparse.RawTextHelpFormatter'>, prefix_chars='-', fromfile_prefix_chars=None, argument_default=None, conflict_handler='resolve', add_help=True)

Augmenting the argument parser to allow automatic creation of arguments from workflows

Parameters

progNone

The name of the program. (default: sys.argv[0])

usageNone

A usage message. (default: auto-generated from arguments)

descriptionstr

A description of what the program does.

epilogstr

Text following the argument descriptions.

parentslist

Parsers whose arguments should be copied into this one.

formatter_classobj

HelpFormatter class for printing help messages.

prefix_charsstr

Characters that prefix optional arguments.

fromfile_prefix_charsNone

Characters that prefix files containing additional arguments.

argument_defaultNone

The default value for all arguments.

conflict_handlerstr

String indicating how to handle conflicts.

add_helpbool

Add a -h/-help option.

add_description()

add_epilogue()

add_sub_flow_args(sub_flows)

Take an array of workflow objects and use introspection to extract the parameters, types and docstrings of their run method. Only the optional input parameters are extracted for these as they are treated as sub workflows.

Parameters

sub_flowsarray of dipy.workflows.workflow.Workflow

Workflows to inspect.

Returns

sub_flow_optionals : dictionary of all sub workflow optional parameters

add_workflow(workflow)

Take a workflow object and use introspection to extract the parameters, types and docstrings of its run method. Then add these parameters to the current arparser’s own params to parse. If the workflow is of type combined_workflow, the optional input parameters of its sub workflows will also be added.

Parameters

workflowdipy.workflows.workflow.Workflow

Workflow from which to infer parameters.

Returns

sub_flow_optionals : dictionary of all sub workflow optional parameters

get_flow_args(args=None, namespace=None)

Return the parsed arguments as a dictionary that will be used as a workflow’s run method arguments.

property optional_parameters

property output_parameters

property positional_parameters

show_argument(dest)

update_argument(*args, **kargs)

Bases: Workflow

__init__(output_strategy='append', mix_names=False, force=False, skip=False)

Workflow that combines multiple workflows. The workflow combined together are referred as sub flows in this class.

get_optionals(flow, **kwargs)

Returns the sub flow’s optional arguments merged with those passed as params in kwargs.

get_sub_runs()

Returns a list of tuples (sub flow name, sub flow run method, sub flow short name) to be used in the sub flow parameters extraction.

run_sub_flow(flow, *args, **kwargs)

Runs the sub flow with the optional parameters passed via the command line. This is a convenience method to make sub flow running more intuitive on the concrete CombinedWorkflow side.

set_sub_flows_optionals(opts)

Sets the self._optionals variable with all sub flow arguments that were passed in the commandline.

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bval_files, model='ols', b0_threshold=50, alpha=1.0, verbose=False, b0_denoising=True, clip_negative_vals=False, shift_intensity=True, out_dir='', out_denoised='dwi_patch2self.nii.gz')

Workflow for Patch2Self denoising method.

It applies patch2self denoising on each file found by ‘globing’ input_file and bval_file. It saves the results in a directory specified by out_dir.

Parameters

input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bval_filesstring

bval file associated with the diffusion data.

modelstring, or initialized linear model object.

This will determine the algorithm used to solve the set of linear equations underlying this model. If it is a string it needs to be one of the following: {‘ols’, ‘ridge’, ‘lasso’}. Otherwise, it can be an object that inherits from dipy.optimize.SKLearnLinearSolver or an object with a similar interface from Scikit-Learn: sklearn.linear_model.LinearRegression, sklearn.linear_model.Lasso or sklearn.linear_model.Ridge and other objects that inherit from sklearn.base.RegressorMixin. Default: ‘ols’.

b0_thresholdint, optional

Threshold for considering volumes as b0.

alphafloat, optional

Regularization parameter only for ridge regression model.

verbosebool, optional

Show progress of Patch2Self and time taken.

b0_denoisingbool, optional

Skips denoising b0 volumes if set to False.

clip_negative_valsbool, optional

Sets negative values after denoising to 0 using np.clip.

shift_intensitybool, optional

Shifts the distribution of intensities per volume to give non-negative values

out_dirstring, optional

Output directory (default current directory)

out_denoisedstring, optional

Name of the resulting denoised volume (default: dwi_patch2self.nii.gz)

References

[Fadnavis20]

S. Fadnavis, J. Batson, E. Garyfallidis, Patch2Self: Denoising Diffusion MRI with Self-supervised Learning, Advances in Neural Information Processing Systems 33 (2020)

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, sigma=0, patch_radius=1, block_radius=5, rician=True, out_dir='', out_denoised='dwi_nlmeans.nii.gz')

Workflow wrapping the nlmeans denoising method.

It applies nlmeans denoise on each file found by ‘globing’ input_files and saves the results in a directory specified by out_dir.

Parameters

input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

sigmafloat, optional

Sigma parameter to pass to the nlmeans algorithm.

patch_radiusint, optional

patch size is 2 x patch_radius + 1.

block_radiusint, optional

block size is 2 x block_radius + 1.

ricianbool, optional

If True the noise is estimated as Rician, otherwise Gaussian noise is assumed.

out_dirstring, optional

Output directory. (default current directory)

out_denoisedstring, optional

Name of the resulting denoised volume.

References

[Descoteaux08]

Descoteaux, Maxime and Wiest-Daesslé, Nicolas and

Prima, Sylvain and Barillot, Christian and Deriche, Rachid. Impact of Rician Adapted Non-Local Means Filtering on HARDI, MICCAI 2008

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bvalues_files, bvectors_files, sigma=0, b0_threshold=50, bvecs_tol=0.01, patch_radius=2, pca_method='eig', tau_factor=2.3, out_dir='', out_denoised='dwi_lpca.nii.gz')

Workflow wrapping LPCA denoising method.

Parameters

input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

sigmafloat, optional

Standard deviation of the noise estimated from the data. Default 0: it means sigma value estimation with the Manjon2013 algorithm [3]_.

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Threshold used to check that norm(bvec) = 1 +/- bvecs_tol b-vectors are unit vectors.

patch_radiusint, optional

The radius of the local patch to be taken around each voxel (in voxels) For example, for a patch radius with value 2, and assuming the input image is a 3D image, the denoising will take place in blocks of 5x5x5 voxels.

pca_methodstring, optional

Use either eigenvalue decomposition (‘eig’) or singular value decomposition (‘svd’) for principal component analysis. The default method is ‘eig’ which is faster. However, occasionally ‘svd’ might be more accurate.

tau_factorfloat, optional

Thresholding of PCA eigenvalues is done by nulling out eigenvalues that are smaller than:

\[\tau = (\tau_{factor} \sigma)^2\]

tau_{factor} can be change to adjust the relationship between the noise standard deviation and the threshold tau. If tau_{factor} is set to None, it will be automatically calculated using the Marcenko-Pastur distribution [2]_.

out_dirstring, optional

Output directory. (default current directory)

out_denoisedstring, optional

Name of the resulting denoised volume.

References

Fieremans E, 2016. Denoising of Diffusion MRI using random matrix theory. Neuroimage 142:394-406. doi: 10.1016/j.neuroimage.2016.08.016

mapping using random matrix theory. Magnetic Resonance in Medicine. doi: 10.1002/mrm.26059.

Diffusion Weighted Image Denoising Using Overcomplete Local PCA. PLoS ONE 8(9): e73021. https://doi.org/10.1371/journal.pone.0073021

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, patch_radius=2, pca_method='eig', return_sigma=False, out_dir='', out_denoised='dwi_mppca.nii.gz', out_sigma='dwi_sigma.nii.gz')

Workflow wrapping Marcenko-Pastur PCA denoising method.

Parameters

input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

patch_radiusvariable int, optional

The radius of the local patch to be taken around each voxel (in voxels) For example, for a patch radius with value 2, and assuming the input image is a 3D image, the denoising will take place in blocks of 5x5x5 voxels.

pca_methodstring, optional

Use either eigenvalue decomposition (‘eig’) or singular value decomposition (‘svd’) for principal component analysis. The default method is ‘eig’ which is faster. However, occasionally ‘svd’ might be more accurate.

return_sigmabool, optional

If true, a noise standard deviation estimate based on the Marcenko-Pastur distribution is returned [2]_.

out_dirstring, optional

Output directory. (default current directory)

out_denoisedstring, optional

Name of the resulting denoised volume.

out_sigmastring, optional

Name of the resulting sigma volume.

References

Fieremans E, 2016. Denoising of Diffusion MRI using random matrix theory. Neuroimage 142:394-406. doi: 10.1016/j.neuroimage.2016.08.016

mapping using random matrix theory. Magnetic Resonance in Medicine. doi: 10.1002/mrm.26059.

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, slice_axis=2, n_points=3, num_processes=1, out_dir='', out_unring='dwi_unring.nii.gz')

Workflow for applying Gibbs Ringing method.

Parameters

input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

slice_axisint, optional

Data axis corresponding to the number of acquired slices. Could be (0, 1, or 2): for example, a value of 2 would mean the third axis.

n_pointsint, optional

Number of neighbour points to access local TV (see note).

num_processesint or None, optional

Split the calculation to a pool of children processes. Only applies to 3D or 4D data arrays. Default is 1. If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

out_dirstring, optional

Output directory. (default current directory)

out_unringstring, optional

Name of the resulting denoised volume.

References

Data Analysis and their Application to the Healthy Ageing Brain (Doctoral thesis). https://doi.org/10.17863/CAM.29356

artifact removal based on local subvoxel-shifts. Magn Reson Med. 2016 doi: 10.1002/mrm.26054.

Bases: object

A line-based string reader.

__init__(data)

datastr

String with lines separated by ‘

‘.

eof()

is_empty()

peek(n=0)

read()

read_to_condition(condition_func)

read_to_next_empty_line()

read_to_next_unindented_line()

reset()

seek_next_non_empty_line()

Bases: object

__init__(docstring, config={})

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, b0_threshold=50, bvecs_tol=0.01, bshell_thr=100)

Provides useful information about different files used in medical imaging. Any number of input files can be provided. The program identifies the type of file by its extension.

Parameters

input_filesvariable string

Any number of Nifti1, bvals or bvecs files.

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Threshold used to check that norm(bvec) = 1 +/- bvecs_tol b-vectors are unit vectors.

bshell_thrfloat, optional

Threshold for distinguishing b-values in different shells.

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

static get_fetcher_datanames()

Gets available dataset and function names.

Returns

available_data: dict

Available dataset and function names.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

static load_module(module_path)

Load / reload an external module.

Parameters

module_path: string

the path to the module relative to the main script

Returns

module: module object

run(data_names, out_dir='')

Download files to folder and check their md5 checksums.

To see all available datasets, please type “list” in data_names.

Parameters

data_namesvariable string

Any number of Nifti1, bvals or bvecs files.

out_dirstring, optional

Output directory. (default current directory)

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, vol_idx=0, out_dir='', out_split='split.nii.gz')

Splits the input 4D file and extracts the required 3D volume.

Parameters

input_filesvariable string

Any number of Nifti1 files

vol_idx : int, optional out_dir : string, optional

Output directory. (default current directory)

out_splitstring, optional

Name of the resulting split volume

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, lb, ub=inf, out_dir='', out_mask='mask.nii.gz')

Workflow for creating a binary mask

Parameters

input_filesstring

Path to image to be masked.

lbfloat

Lower bound value.

ubfloat, optional

Upper bound value.

out_dirstring, optional

Output directory. (default current directory)

out_maskstring, optional

Name of the masked file.

Bases: object

Create output filenames that work nicely with multiple input files from multiple directories (processing multiple subjects with one command)

Use information from input files, out_dir and out_fnames to generate correct outputs which can come from long lists of multiple or single inputs.

__init__(output_strategy='absolute', mix_names=False)

create_directories()

create_outputs()

file_existence_check(args)

set_inputs(*args)

set_out_dir(out_dir)

set_out_fnames(*args)

set_output_keys(*args)

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(data_files, bvals_files, bvecs_files, small_delta, big_delta, b0_threshold=50.0, laplacian=True, positivity=True, bval_threshold=2000, save_metrics=(), laplacian_weighting=0.05, radial_order=6, out_dir='', out_rtop='rtop.nii.gz', out_lapnorm='lapnorm.nii.gz', out_msd='msd.nii.gz', out_qiv='qiv.nii.gz', out_rtap='rtap.nii.gz', out_rtpp='rtpp.nii.gz', out_ng='ng.nii.gz', out_perng='perng.nii.gz', out_parng='parng.nii.gz')

Workflow for fitting the MAPMRI model (with optional Laplacian regularization). Generates rtop, lapnorm, msd, qiv, rtap, rtpp, non-gaussian (ng), parallel ng, perpendicular ng saved in a nifti format in input files provided by data_files and saves the nifti files to an output directory specified by out_dir.

In order for the MAPMRI workflow to work in the way intended either the Laplacian or positivity or both must be set to True.

Parameters

data_filesstring

Path to the input volume.

bvals_filesstring

Path to the bval files.

bvecs_filesstring

Path to the bvec files.

small_deltafloat

Small delta value used in generation of gradient table of provided bval and bvec.

big_deltafloat

Big delta value used in generation of gradient table of provided bval and bvec.

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

laplacianbool, optional

Regularize using the Laplacian of the MAP-MRI basis.

positivitybool, optional

Constrain the propagator to be positive.

bval_thresholdfloat, optional

Sets the b-value threshold to be used in the scale factor estimation. In order for the estimated non-Gaussianity to have meaning this value should set to a lower value (b<2000 s/mm^2) such that the scale factors are estimated on signal points that reasonably represent the spins at Gaussian diffusion.

save_metricsvariable string, optional

List of metrics to save. Possible values: rtop, laplacian_signal, msd, qiv, rtap, rtpp, ng, perng, parng

laplacian_weightingfloat, optional

Weighting value used in fitting the MAPMRI model in the Laplacian and both model types.

radial_orderunsigned int, optional

Even value used to set the order of the basis.

out_dirstring, optional

Output directory. (default: current directory)

out_rtopstring, optional

Name of the rtop to be saved.

out_lapnormstring, optional

Name of the norm of Laplacian signal to be saved.

out_msdstring, optional

Name of the msd to be saved.

out_qivstring, optional

Name of the qiv to be saved.

out_rtapstring, optional

Name of the rtap to be saved.

out_rtppstring, optional

Name of the rtpp to be saved.

out_ngstring, optional

Name of the Non-Gaussianity to be saved.

out_perngstring, optional

Name of the Non-Gaussianity perpendicular to be saved.

out_parngstring, optional

Name of the Non-Gaussianity parallel to be saved.

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

get_fitted_tensor(data, mask, bval, bvec, b0_threshold=50, bvecs_tol=0.01, fit_method='WLS', optional_args=None)

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bvalues_files, bvectors_files, mask_files, fit_method='WLS', b0_threshold=50, bvecs_tol=0.01, sigma=None, save_metrics=None, out_dir='', out_tensor='tensors.nii.gz', out_fa='fa.nii.gz', out_ga='ga.nii.gz', out_rgb='rgb.nii.gz', out_md='md.nii.gz', out_ad='ad.nii.gz', out_rd='rd.nii.gz', out_mode='mode.nii.gz', out_evec='evecs.nii.gz', out_eval='evals.nii.gz', nifti_tensor=True)

Workflow for tensor reconstruction and for computing DTI metrics. using Weighted Least-Squares. Performs a tensor reconstruction on the files by ‘globing’ input_files and saves the DTI metrics in a directory specified by out_dir.

Parameters

input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

mask_filesstring

Path to the input masks. This path may contain wildcards to use multiple masks at once.

fit_methodstring, optional

can be one of the following: ‘WLS’ for weighted least squares ‘LS’ or ‘OLS’ for ordinary least squares ‘NLLS’ for non-linear least-squares ‘RT’ or ‘restore’ or ‘RESTORE’ for RESTORE robust tensor fitting

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Threshold used to check that norm(bvec) = 1 +/- bvecs_tol

sigmafloat, optional

An estimate of the variance. [5] recommend to use 1.5267 * std(background_noise), where background_noise is estimated from some part of the image known to contain no signal (only noise) b-vectors are unit vectors.

save_metricsvariable string, optional

List of metrics to save. Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval

out_dirstring, optional

Output directory. (default current directory)

out_tensorstring, optional

Name of the tensors volume to be saved. Per default, this will be saved following the nifti standard: with the tensor elements as Dxx, Dxy, Dyy, Dxz, Dyz, Dzz on the last (5th) dimension of the volume (shape: (i, j, k, 1, 6)). If nifti_tensor is False, this will be saved in an alternate format that is used by other software (e.g., FSL): a 4-dimensional volume (shape (i, j, k, 6)) with Dxx, Dxy, Dxz, Dyy, Dyz, Dzz on the last dimension.

out_fastring, optional

Name of the fractional anisotropy volume to be saved.

out_gastring, optional

Name of the geodesic anisotropy volume to be saved.

out_rgbstring, optional

Name of the color fa volume to be saved.

out_mdstring, optional

Name of the mean diffusivity volume to be saved.

out_adstring, optional

Name of the axial diffusivity volume to be saved.

out_rdstring, optional

Name of the radial diffusivity volume to be saved.

out_modestring, optional

Name of the mode volume to be saved.

out_evecstring, optional

Name of the eigenvectors volume to be saved.

out_evalstring, optional

Name of the eigenvalues to be saved.

nifti_tensorbool, optional

Whether the tensor is saved in the standard Nifti format or in an alternate format that is used by other software (e.g., FSL): a 4-dimensional volume (shape (i, j, k, 6)) with Dxx, Dxy, Dxz, Dyy, Dyz, Dzz on the last dimension.

References

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bvalues_files, bvectors_files, mask_files, b0_threshold=50.0, bvecs_tol=0.01, roi_center=None, roi_radii=10, fa_thr=0.7, frf=None, extract_pam_values=False, sh_order=8, odf_to_sh_order=8, parallel=False, num_processes=None, out_dir='', out_pam='peaks.pam5', out_shm='shm.nii.gz', out_peaks_dir='peaks_dirs.nii.gz', out_peaks_values='peaks_values.nii.gz', out_peaks_indices='peaks_indices.nii.gz', out_gfa='gfa.nii.gz')

Constrained spherical deconvolution

Parameters

input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

mask_filesstring

Path to the input masks. This path may contain wildcards to use multiple masks at once. (default: No mask used)

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Bvecs should be unit vectors.

roi_centervariable int, optional

Center of ROI in data. If center is None, it is assumed that it is the center of the volume with shape data.shape[:3].

roi_radiiint or array-like, optional

radii of cuboid ROI in voxels.

fa_thrfloat, optional

FA threshold for calculating the response function.

frfvariable float, optional

Fiber response function can be for example inputted as 15 4 4 (from the command line) or [15, 4, 4] from a Python script to be converted to float and multiplied by 10**-4 . If None the fiber response function will be computed automatically.

extract_pam_valuesbool, optional

Save or not to save pam volumes as single nifti files.

sh_orderint, optional

Spherical harmonics order used in the CSA fit.

odf_to_sh_orderint, optional

Spherical harmonics order used for peak_from_model to compress the ODF to spherical harmonics coefficients.

parallelbool, optional

Whether to use parallelization in peak-finding during the calibration procedure.

num_processesint, optional

If parallel is True, the number of subprocesses to use (default multiprocessing.cpu_count()). If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

out_dirstring, optional

Output directory. (default current directory)

out_pamstring, optional

Name of the peaks volume to be saved.

out_shmstring, optional

Name of the spherical harmonics volume to be saved.

out_peaks_dirstring, optional

Name of the peaks directions volume to be saved.

out_peaks_valuesstring, optional

Name of the peaks values volume to be saved.

out_peaks_indicesstring, optional

Name of the peaks indices volume to be saved.

out_gfastring, optional

Name of the generalized FA volume to be saved.

References

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bvalues_files, bvectors_files, mask_files, sh_order=6, odf_to_sh_order=8, b0_threshold=50.0, bvecs_tol=0.01, extract_pam_values=False, parallel=False, num_processes=None, out_dir='', out_pam='peaks.pam5', out_shm='shm.nii.gz', out_peaks_dir='peaks_dirs.nii.gz', out_peaks_values='peaks_values.nii.gz', out_peaks_indices='peaks_indices.nii.gz', out_gfa='gfa.nii.gz')

Constant Solid Angle.

Parameters

input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvectors files. This path may contain wildcards to use multiple bvectors files at once.

mask_filesstring

Path to the input masks. This path may contain wildcards to use multiple masks at once. (default: No mask used)

sh_orderint, optional

Spherical harmonics order used in the CSA fit.

odf_to_sh_orderint, optional

Spherical harmonics order used for peak_from_model to compress the ODF to spherical harmonics coefficients.

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

bvecs_tolfloat, optional

Threshold used so that norm(bvec)=1.

extract_pam_valuesbool, optional

Whether or not to save pam volumes as single nifti files.

parallelbool, optional

Whether to use parallelization in peak-finding during the calibration procedure.

num_processesint, optional

If parallel is True, the number of subprocesses to use (default multiprocessing.cpu_count()). If < 0 the maximal number of cores minus num_processes + 1 is used (enter -1 to use as many cores as possible). 0 raises an error.

out_dirstring, optional

Output directory. (default current directory)

out_pamstring, optional

Name of the peaks volume to be saved.

out_shmstring, optional

Name of the spherical harmonics volume to be saved.

out_peaks_dirstring, optional

Name of the peaks directions volume to be saved.

out_peaks_valuesstring, optional

Name of the peaks values volume to be saved.

out_peaks_indicesstring, optional

Name of the peaks indices volume to be saved.

out_gfastring, optional

Name of the generalized FA volume to be saved.

References

Bases: Workflow

__init__(output_strategy='absolute', mix_names=False, force=False, skip=False)

Initialize the basic workflow object.

This object takes care of any workflow operation that is common to all the workflows. Every new workflow should extend this class.

get_fitted_tensor(data, mask, bval, bvec, b0_threshold=50, fit_method='WLS', optional_args=None)

classmethod get_short_name()

Return A short name for the workflow used to subdivide.

The short name is used by CombinedWorkflows and the argparser to subdivide the commandline parameters avoiding the trouble of having subworkflows parameters with the same name.

For example, a combined workflow with dti reconstruction and csd reconstruction might en up with the b0_threshold parameter. Using short names, we will have dti.b0_threshold and csd.b0_threshold available.

Returns class name by default but it is strongly advised to set it to something shorter and easier to write on commandline.

run(input_files, bvalues_files, bvectors_files, mask_files, fit_method='WLS', b0_threshold=50.0, sigma=None, save_metrics=None, out_dir='', out_dt_tensor='dti_tensors.nii.gz', out_fa='fa.nii.gz', out_ga='ga.nii.gz', out_rgb='rgb.nii.gz', out_md='md.nii.gz', out_ad='ad.nii.gz', out_rd='rd.nii.gz', out_mode='mode.nii.gz', out_evec='evecs.nii.gz', out_eval='evals.nii.gz', out_dk_tensor='dki_tensors.nii.gz', out_mk='mk.nii.gz', out_ak='ak.nii.gz', out_rk='rk.nii.gz')

Workflow for Diffusion Kurtosis reconstruction and for computing DKI metrics. Performs a DKI reconstruction on the files by ‘globing’ input_files and saves the DKI metrics in a directory specified by out_dir.

Parameters

input_filesstring

Path to the input volumes. This path may contain wildcards to process multiple inputs at once.

bvalues_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

bvectors_filesstring

Path to the bvalues files. This path may contain wildcards to use multiple bvalues files at once.

mask_filesstring

Path to the input masks. This path may contain wildcards to use multiple masks at once. (default: No mask used)

fit_methodstring, optional

can be one of the following: ‘OLS’ or ‘ULLS’ for ordinary least squares ‘WLS’ or ‘UWLLS’ for weighted ordinary least squares

b0_thresholdfloat, optional

Threshold used to find b0 volumes.

sigmafloat, optional

An estimate of the variance. [3]_ recommend to use 1.5267 * std(background_noise), where background_noise is estimated from some part of the image known to contain no signal (only noise)

save_metricsvariable string, optional

List of metrics to save. Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval

out_dirstring, optional

Output directory. (default current directory)

out_dt_tensorstring, optional

Name of the tensors volume to be saved.

out_dk_tensorstring, optional

Name of the tensors volume to be saved.

out_fastring, optional

Name of the fractional anisotropy volume to be saved.

out_gastring, optional

Name of the geodesic anisotropy volume to be saved.

out_rgbstring, optional

Name of the color fa volume to be saved.

out_mdstring, optional

Name of the mean diffusivity volume to be saved.

out_adstring, optional

Name of the axial diffusivity volume to be saved.

out_rdstring, optional

Name of the radial diffusivity volume to be saved.

out_modestring, optional

Name of the mode volume to be saved.

out_evecstring, optional

Name of the eigenvectors volume to be saved.

out_evalstring, optional

Name of the eigenvalues to be saved.

out_mkstring, optional

Name of the mean kurtosis to be saved.

out_akstring, optional

Name of the axial kurtosis to be saved.

out_rkstring, optional

Name of the radial kurtosis to be saved.

References