pymialsrtk.workflows.srr_assessment_stage module

Module for the assessment of the super-resolution reconstruction quality with a reference.

pymialsrtk.workflows.srr_assessment_stage.create_srr_assessment_stage(p_do_multi_parameters=False, p_do_reconstruct_labels=False, p_input_srtv_node=None, p_verbose=False, p_openmp_number_of_cores=1, name='srr_assessment_stage')[source]

Create an assessment workflow to compare a SR-reconstructed image and a reference target.

Parameters

  • name (string) – Name of workflow (default: “sr_assessment_stage”)

  • p_do_multi_parameters (boolean) – whether multiple SR are to be assessed with different TV parameters (default: False)

  • p_input_srtv_node (string) – when p_do_multi_parameters is set, name of the sourcenode from which metrics must be merged

  • p_openmp_number_of_cores (integer) – number of threads possible for ants registration (default : 1)

Inputs

  • input_reference_image (pathlike object or string representing a file) – Path to the ground truth image against which the SR will be evaluated.

  • input_reference_mask (pathlike object or string representing a file) – Path to the mask of the ground truth image.

  • input_reference_labelmap (pathlike object or string representing a file) – Path to the labelmap (tissue segmentation) of the ground truth image.

  • input_sr_image (pathlike object or string representing a file) – Path to the SR reconstructed image.

  • input_sdi_image (pathlike object or string representing a file) – Path to the SDI (interpolated image) used as input to the SR.

  • input_TV_parameters (dictionary) – Dictionary of parameters that were used for the TV reconstruction.

Outputs

outputnode.output_metrics (list of float) – List of output metrics

Example

>>> from pymialsrtk.pipelines.workflows import srr_assessment_stage as srr_assessment
>>> srr_eval = srr_assessment.create_srr_assessment_stage()
>>> srr_eval.inputs.input_reference_image = 'sub-01_desc-GT_T2w.nii.gz'
>>> srr_eval.inputs.input_reference_mask = 'sub-01_desc-GT_mask.nii.gz'
>>> srr_eval.inputs.input_reference_mask = 'sub-01_desc-GT_labels.nii.gz'
>>> srr_eval.inputs.input_sr_image = 'sub-01_id-1_rec-SR_T2w.nii.gz'
>>> srr_eval.inputs.input_sdi_image = 'sub-01_id-1_desc-SDI_T2w.nii.gz'
>>> srr_eval.inputs.input_TV_parameters = {
        'in_loop': '10',
        'in_deltat': '0.01',
        'in_lambda': '0.75',
        'in_bregman_loop': '3',
        'in_iter': '50',
        'in_step_scale': '1',
        'in_gamma': '1',
        'in_inner_thresh':
        '1e-05',
        'in_outer_thresh': '1e-06'
    }
>>> srr_eval.run()