eXtensible Connectivity Pipeline-style quality control files¶
Generate eXtensible Connectivity Pipeline-style quality control files
Columns¶
- substr
subject label [1]
- sesstr
session label [1]
- taskstr
task label [1]
- runint
run index [1]
- descstr
description [1]
- spacestr
space label [1]
- meanFDfloat
mean Jenkinson framewise displacement [2]
CPAC.generate_motion_statistics.calculate_FD_J- relMeansRMSMotionfloat
“mean value of RMS motion” [3]
- relMaxRMSMotionfloat
“maximum vaue of RMS motion” [3]
- meanDVInitfloat
“mean DVARS” [3]
- meanDVFinalfloat
“mean DVARS” [3]
- nVolCensoredint
“total number of volume(s) censored [3]
- nVolsRemovedint
number of volumes in derivative minus number of volumes in original functional scan
- motionDVCorrInitfloat
“correlation of RMS and DVARS before regresion” [3]
- motionDVCorrFinalfloat
“correlation of RMS and DVARS after regresion” [3]
- coregDicefloat
“Coregsitration of Functional and T1w:[…] Dice index” [3] [4]
- coregJaccardfloat
“Coregsitration of Functional and T1w:[…] Jaccard index” [3] [4]
- coregCrossCorrfloat
“Coregsitration of Functional and T1w:[…] cross correlation” [3] [4]
- coregCoveragfloat
“Coregsitration of Functional and T1w:[…] Coverage index” [3] [4]
- normDicefloat
“Normalization of T1w/Functional to Template:[…] Dice index” [3] [4]
- normJaccardfloat
“Normalization of T1w/Functional to Template:[…] Jaccard index” [3] [4]
- normCrossCorrfloat
“Normalization of T1w/Functional to Template:[…] cross correlation” [3] [4]
- normCoveragefloat
“Normalization of T1w/Functional to Template:[…] Coverage index” [3] [4]
- CPAC.qc.xcp.calculate_overlap(image_pair)[source]¶
Function to calculate Dice, Jaccard, CrossCorr and Coverage:cite:cite-Penn19 from a pair of arrays
- Parameters:
- image_pair2-tuple
array of which to calculate overlaps metrics
- Returns:
- coefficentsdict
- coeffiecients[‘dice’]float
Dice index
- coeffiecients[‘jaccard’]float
Jaccard index
- coeffiecients[‘cross_corr’]float
cross-correlation
- coeffiecients[‘coverage’]float
coverage index
Examples
>>> import numpy as np >>> a1 = np.array([0, 0, 0, 1, 1, 1]) >>> a2 = np.array([0, 0, 1, 1, 0, 1]) >>> tuple(calculate_overlap((a1, a2)).values()) (0.6666666666666666, 0.5, 0.33333333333333326, 0.6666666666666666) >>> tuple(calculate_overlap((a1, a1)).values()) (1.0, 1.0, 0.9999999999999998, 1.0) >>> tuple(calculate_overlap((a2, a2)).values()) (1.0, 1.0, 0.9999999999999998, 1.0)
- CPAC.qc.xcp.generate_xcp_qc(space, desc, original_anat, final_anat, original_func, final_func, space_T1w_bold, movement_parameters, dvars, censor_indices, framewise_displacement_jenkinson, dvars_after, fdj_after, template=None)[source]¶
Function to generate an RBC-style QC CSV
- Parameters:
- spacestr
‘native’ or ‘template’
- descstr
description string
- original_anatstr
path to original ‘T1w’ image
- final_anatstr
path to ‘desc-preproc_T1w’ image
- original_funcstr
path to original ‘bold’ image
- final_boldstr
path to ‘desc-preproc_bold’ image
- space_T1w_boldstr
path to ‘space-T1w_desc-mean_bold’ image
- movement_parameters: str
path to movement parameters
- dvarsstr
path to DVARS before motion correction
- censor_indiceslist
list of indices of censored volumes
- framewise_displacement_jenkinsonstr
path to framewise displacement (Jenkinson) before motion correction
- dvars_afterstr
path to DVARS on final ‘bold’ image
- fdj_afterstr
path to framewise displacement (Jenkinson) on final ‘bold’ image
- templatestr
path to template
- Returns:
- str
path to desc-xcp_quality TSV
- CPAC.qc.xcp.qc_xcp_native(wf, cfg, strat_pool, pipe_num, opt=None)[source]¶
- {‘name’: ‘qc_xcp_native’,
‘config’: [‘pipeline_setup’, ‘output_directory’, ‘quality_control’], ‘switch’: [‘generate_xcpqc_files’], ‘option_key’: ‘None’, ‘option_val’: ‘None’, ‘inputs’: [(‘bold’, ‘subject’, ‘scan’, ‘T1w’, ‘max-displacement’, ‘dvars’,
‘censor-indices’, ‘desc-preproc_bold’, ‘desc-preproc_T1w’, ‘space-T1w_desc-mean_bold’, ‘space-bold_desc-brain_mask’, ‘movement-parameters’, ‘framewise-displacement-jenkinson’, ‘rels-displacement’, ‘coordinate-transformation’)],
‘outputs’: [‘desc-xcp_quality’]}
- CPAC.qc.xcp.qc_xcp_skullstripped(wf, cfg, strat_pool, pipe_num, opt=None)[source]¶
Same as
qc_xcp_nativeexcept no motion inputs. Node Block: {‘name’: ‘qc_xcp_skullstripped’,‘config’: [‘pipeline_setup’, ‘output_directory’, ‘quality_control’], ‘switch’: [‘generate_xcpqc_files’], ‘option_key’: ‘None’, ‘option_val’: ‘None’, ‘inputs’: [(‘bold’, ‘subject’, ‘scan’, ‘T1w’, ‘desc-preproc_bold’,
‘desc-preproc_T1w’, ‘space-T1w_desc-mean_bold’, ‘space-bold_desc-brain_mask’)],
‘outputs’: [‘desc-xcp_quality’]}
- CPAC.qc.xcp.qc_xcp_template(wf, cfg, strat_pool, pipe_num, opt=None)[source]¶
- {‘name’: ‘qc_xcp_template’,
‘config’: [‘pipeline_setup’, ‘output_directory’, ‘quality_control’], ‘switch’: [‘generate_xcpqc_files’], ‘option_key’: ‘None’, ‘option_val’: ‘None’, ‘inputs’: [(‘bold’, ‘subject’, ‘scan’, ‘T1w’,
‘T1w-brain-template-funcreg’, ‘space-T1w_desc-mean_bold’, ‘space-template_desc-preproc_bold’, ‘desc-preproc_T1w’, ‘space-template_desc-bold_mask’)],
‘outputs’: [‘space-template_desc-xcp_quality’]}
- CPAC.qc.xcp.strings_from_bids(final_func)[source]¶
Function to gather BIDS entities into a dictionary
- Parameters:
- final_funcstr
- Returns:
- dict
Examples
>>> fake_path = ( ... '/path/to/sub-fakeSubject_ses-fakeSession_task-peer_run-3_' ... 'atlas-Schaefer400_space-MNI152NLin6_res-1x1x1_' ... 'desc-NilearnPearson_connectome.tsv') >>> strings_from_bids(fake_path)['desc'] 'NilearnPearson' >>> strings_from_bids(fake_path)['space'] 'MNI152NLin6'
References
BIDS-contributors. 2021. Appendix IX: Entities. The Brain Imaging Data Structure (BIDS) Specification, 1.6.0
Jenkinson, M., Bannister, P., Brady, M., and Smith, S. 2002 October. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17 :2 , pp. 825–841 doi:10.1006/nimg.2002.1132
