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_masks_desc=None, p_dict_custom_interfaces=None, openmp_number_of_cores=None, nipype_number_of_cores=None)

Bases: object

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

Attributes

• 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

• 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(bids_dir='/path/to/bids_dir',
                                  output_dir='/path/to/output_dir',
                                  subject='sub-01',
                                  p_stacks=[1,3,2,0],
                                  sr_id=1,
                                  session=None,
                                  paramTV={deltatTV = "0.001",
                                           lambdaTV = "0.75",
                                           primal_dual_loops = "20"},
                                  masks_derivatives_dir="/custom/mask_dir",
                                  masks_desc=None,
                                  p_dict_custom_interfaces=None)
>>> # Create the super resolution Nipype workflow
>>> pipeline.create_workflow()
>>> # Execute the workflow
>>> res = pipeline.run(number_of_cores=1) 

create_subject_report()

Create the HTML report

create_workflow()

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.

run(memory=None)

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

memory (int) – Maximal memory used by the workflow