pymialsrtk.pipelines.anatomical.srr module
Module for the super-resolution reconstruction pipeline.
- class pymialsrtk.pipelines.anatomical.srr.SRReconPipeline(p_bids_dir, p_output_dir, p_subject, p_ga=None, p_stacks=None, p_sr_id=1, p_session=None, p_paramTV=None, p_masks_derivatives_dir=None, p_labels_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, p_all_outputs=None)[source]
Bases:
pymialsrtk.pipelines.anatomical.abstract.AbstractAnatomicalPipeline
Class used to represent the workflow of the Super-Resolution reconstruction 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 reconstruction pipeline
m_paramTV (
dict
) – Dictionary of parameters for the super-resolution reconstruction. Contains: - deltatTV : stringSuper-resolution optimization time-step
- lambdaTVfloat
Regularization weight (default is 0.75)
- num_iterationsstring
Number of iterations in the primal/dual loops used in the optimization of the total-variation super-resolution algorithm.
- num_primal_dual_loopsstring
Number of primal/dual (inner) loops used in the optimization of the total-variation super-resolution algorithm.
- num_bregman_loopsstring
Number of Bregman (outer) loops used in the optimization of the total-variation super-resolution algorithm.
- step_scalestring
Step scale parameter used in the optimization of the total- variation super-resolution algorithm.
- gammastring
Gamma parameter used in the optimization of the total-variation super-resolution algorithm.
m_sr_id (string) – ID of the reconstruction useful to distinguish when multiple reconstructions with different order of stacks are run on the same subject
m_keep_all_outputs (bool) – Whether intermediate outputs must be issued. (default: False)
m_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) – Whether the Slice-to-Volume Registration should be skipped in the image reconstruction. (default is False)
m_do_refine_hr_mask (bool) – Whether a refinement of the HR mask should be performed. (default is False)
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 SRReconPipeline >>> # Create a new instance >>> pipeline = SRReconPipeline(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)
- 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_anat_orientation = None
- m_do_multi_parameters = None
- m_do_nlm_denoising = None
- m_do_refine_hr_mask = None
- m_do_srr_assessment = None
- m_keep_all_outputs = None
- m_labels_derivatives_dir = None
- m_paramTV = None
- m_pipeline_name = 'srr_pipeline'
- m_skip_stacks_ordering = None
- m_skip_svr = None
- run(memory=None, logger=None)[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
memory (int) – Maximal memory used by the workflow