pymialsrtk.pipelines.anatomical.preprocessing module
Module for the preprocessing pipeline.
- class pymialsrtk.pipelines.anatomical.preprocessing.PreprocessingPipeline(p_bids_dir, p_output_dir, p_subject, p_ga=None, p_stacks=None, p_sr_id=1, p_session=None, p_masks_derivatives_dir=None, p_masks_desc=None, p_dict_custom_interfaces=None, p_verbose=None, p_openmp_number_of_cores=None, p_nipype_number_of_cores=None)[source]
Bases:
pymialsrtk.pipelines.anatomical.abstract.AbstractAnatomicalPipeline
Class used to represent the workflow of the Preprocessing pipeline.
- Attributes
m_bids_dir (string) – BIDS root directory (required)
m_output_dir (string) – Output derivatives directory (required)
m_subject (string) – Subject ID (in the form
sub-XX
)m_wf (nipype.pipeline.Workflow) – Nipype workflow of the preprocessing pipeline
m_sr_id (string) – ID of the preprocessing useful to distinguish when multiple preprocessing with different order of stacks are run on the same subject
m_session (string) – Session ID if applicable (in the form
ses-YY
)m_stacks (list(int)) – List of stack to be used in the preprocessing. 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_do_nlm_denoising (bool) – Whether the NLM denoising preprocessing should be performed prior to motion estimation. (default is False)
m_skip_stacks_ordering (bool (optional)) – Whether the automatic stacks ordering should be skipped. (default is False)
Examples
>>> from pymialsrtk.pipelines.anatomical.srr import PreprocessingPipeline >>> # Create a new instance >>> pipeline = PreprocessingPipeline(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", num_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)
- check_parameters_integrity(p_dict_custom_interfaces)[source]
Check parameters integrity.
This checks whether the custom interfaces dictionary contains only keys that are used in preprocessing, and raises an exception if it doesn’t.
- Parameters
p_dict_custom_interfaces (dict) – dictionary of custom inferfaces for a given subject that is to be processed.
- 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.
- m_do_nlm_denoising = None
- m_do_registration = None
- m_pipeline_name = 'preproc_pipeline'
- m_skip_stacks_ordering = None
- m_skip_svr = None