[RTM] Add ICA component plots to MRIQC #412
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…enh/ica # Conflicts: # requirements.txt
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Pending merge of #431 and nipreps/niworkflows#134 |
chrisgorgo
changed the title
| @@ -97,6 +97,8 @@ def anat_qc_workflow(dataset, settings, mod='T1w', name='anatMRIQC'): | |||
| norm = pe.Node(RobustMNINormalization( | |||
| num_threads=settings.get('ants_nthreads', 6), template='mni_icbm152_nlin_asym_09c', | |||
| testing=settings.get('testing', False), generate_report=True), name='SpatialNormalization') | |||
| norm.interface.num_threads = settings.get('ants_nthreads', 6) | |||
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why is it necessary to set the num_threads at the level of the interface?
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For the MultiProc plugin to work correctly.
| @@ -362,6 +393,14 @@ def individual_reports(settings, name='ReportsWorkflow'): | |||
| (mosaic_spikes, mplots, [('out_file', 'in4')]) | |||
| ]) | |||
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| if settings.get('ica', False): | |||
| page_number = 4 | |||
| if settings.get('fft_spikes_detector', False): | |||
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Shouldn't be fft_spikes_detector independent of ica?
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They are, but one has to come in the report before the other and thus the "in" ports need adjustment.
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@oesteban (when you find a moment) Any thoughts about switching to BET for skullstripping? |
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@chrisfilo I have the feeling that AFNI is a bit more reliable. It makes other kinds of mistakes (generally leaving out portions of ventricles), but I think those are less problematic. WDYT? |
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Ventricles in a brain mask?
I had one case with weird acquisition angles and lots of neck where bot
AFNI and nilearn got confused but bet worked well:
https://cdn.rawgit.com/chrisfilo/toms_data_evaluation/8375e7b0/sub-01_task-rest_acq-09ep2d_bold.html
We should look at a few dataset before releasing the next version.
…On Wed, Apr 5, 2017 at 9:31 AM, Oscar Esteban ***@***.***> wrote:
@chrisfilo <https://github.com/chrisfilo> I have the feeling that AFNI is
a bit more reliable. It makes other kinds of mistakes (generally leaving
out portions of ventricles), but I think those are less problematic. WDYT?
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Oh sorry, for some reason I was thinking of T1w. Ok, for BOLD my impression is that BET generally works well, but we should first make some kind of normalization and maybe bias field correction. With those two we can get to a safe value for the |
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