Pipelines module¶

Module for the super-resolution reconstruction pipeline.

class pymialsrtk.pipelines.anatomical.srr.AnatomicalPipeline(bids_dir, output_dir, subject, p_stacks=None, sr_id=1, session=None, paramTV=None, p_masks_derivatives_dir=None, p_dict_custom_interfaces=None)[source]¶

Bases: object

Class used to represent the workflow of the Super-Resolution reconstruction pipeline.

bids_dir <string>: BIDS root directory (required)

output_dir <string>: Output derivatives directory (required)

subject <string>: Subject ID (in the form sub-XX)

wf <nipype.pipeline.Workflow>: Nipype workflow of the reconstruction pipeline

dictsink <nipype.interfaces.io.JSONFileSink>: Nipype node used to generate a JSON file that store provenance metadata for the SR-reconstructed images

deltatTV <string>: Super-resolution optimization time-step

lambdaTV <Float>: Regularization weight (default is 0.75)

primal_dual_loops <string>: Number of primal/dual loops used in the optimization of the total-variation super-resolution algorithm.

sr_id <string>: ID of the reconstruction useful to distinguish when multiple reconstructions with different order of stacks are run on the same subject

session <string>: Session ID if applicable (in the form ses-YY)

m_stacks list<<int>>: List of stack to be used in the reconstruction. The specified order is kept if skip_stacks_ordering is True.

m_masks_derivatives_dir <string>: directory basename in BIDS directory derivatives where to search for masks (optional)

m_skip_svr <bool>: Weither the Slice-to-Volume Registration should be skipped in the image reconstruction. (default is False)

m_do_refine_hr_mask <bool>: Weither a refinement of the HR mask should be performed. (default is False)

m_skip_nlm_denoising <bool>: Weither the NLM denoising preprocessing should be skipped. (default is False)

m_skip_stacks_ordering <bool> (optional): Weither the automatic stacks ordering should be skipped. (default is False)

Examples

>>> from pymialsrtk.pipelines.anatomical.srr import AnatomicalPipeline
>>> # Create a new instance
>>> pipeline = AnatomicalPipeline('/path/to/bids_dir',
                              '/path/to/output_dir',
                              'sub-01',
                              [1,3,2,0],
                              01,
                              None,
                              paramTV={deltatTV = "0.001",
                                       lambdaTV = "0.75",
                                       primal_dual_loops = "20"},
                              use_manual_masks=False)
>>> # Create the super resolution Nipype workflow
>>> pipeline.create_workflow()
>>> # Execute the workflow
>>> res = pipeline.run(number_of_cores=1) # doctest: +SKIP

bids_dir = None¶

create_workflow()[source]¶

Create the Niype workflow of the super-resolution pipeline.

It is composed of a succession of Nodes and their corresponding parameters, where the output of node i goes to the input of node i+1.

deltatTV = '0.75'¶

lambdaTV = '0.001'¶

m_do_refine_hr_mask = None¶

m_masks_derivatives_dir = None¶

m_skip_nlm_denoising = None¶

m_skip_stacks_ordering = None¶

m_skip_svr = None¶

m_stacks = None¶

output_dir = None¶

primal_dual_loops = '20'¶

run(number_of_cores=1)[source]¶

Execute the workflow of the super-resolution reconstruction pipeline.

Nipype execution engine will take care of the management and execution of all processing steps involved in the super-resolution reconstruction pipeline. Note that the complete execution graph is saved as a PNG image to support transparency on the whole processing.

Parameters:	<int> (number_of_cores) – Number of cores / CPUs used by the workflow

session = None¶

sr_id = 1¶

subject = None¶

use_manual_masks = False¶

wf = None¶