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Merge pull request #52 from oesteban/fix/17-redundancy-in-fieldmap-wo…
…rkflows ENH: Deduplicating magnitude handling and fieldmap postprocessing workflows
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| Original file line number | Diff line number | Diff line change |
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| # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- | ||
| # vi: set ft=python sts=4 ts=4 sw=4 et: | ||
| """Processing phase-difference (aka :abbr:`GRE (gradient-recalled echo)`) fieldmaps. | ||
| .. _gre-fieldmaps: | ||
| Workflows for processing :abbr:`GRE (gradient recalled echo)` fieldmaps | ||
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | ||
| Workflows for preparing the magnitude part of :abbr:`GRE (gradient-recalled echo)` fieldmap | ||
| images and cleaning up the fieldmaps created from the phases or phasediff. | ||
| """ | ||
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| from nipype.pipeline import engine as pe | ||
| from nipype.interfaces import utility as niu, fsl, ants | ||
| from niflow.nipype1.workflows.dmri.fsl.utils import cleanup_edge_pipeline | ||
| from niworkflows.engine.workflows import LiterateWorkflow as Workflow | ||
| from niworkflows.interfaces.images import IntraModalMerge | ||
| from niworkflows.interfaces.masks import BETRPT | ||
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| def init_magnitude_wf(omp_nthreads, name='magnitude_wf'): | ||
| """ | ||
| Prepare the magnitude part of :abbr:`GRE (gradient-recalled echo)` fieldmaps. | ||
| Average (if not done already) the magnitude part of the | ||
| :abbr:`GRE (gradient recalled echo)` images, run N4 to | ||
| correct for B1 field nonuniformity, and skull-strip the | ||
| preprocessed magnitude. | ||
| Workflow Graph | ||
| .. workflow :: | ||
| :graph2use: orig | ||
| :simple_form: yes | ||
| from sdcflows.workflows.fmap import init_magnitude_wf | ||
| wf = init_magnitude_wf(omp_nthreads=6) | ||
| Parameters | ||
| ---------- | ||
| omp_nthreads : int | ||
| Maximum number of threads an individual process may use | ||
| name : str | ||
| Name of workflow (default: ``prepare_magnitude_w``) | ||
| Inputs | ||
| ------ | ||
| magnitude : pathlike | ||
| Path to the corresponding magnitude path(s). | ||
| Outputs | ||
| ------- | ||
| fmap_ref : pathlike | ||
| Path to the fieldmap reference calculated in this workflow. | ||
| fmap_mask : pathlike | ||
| Path to a binary brain mask corresponding to the reference above. | ||
| """ | ||
| workflow = Workflow(name=name) | ||
| inputnode = pe.Node( | ||
| niu.IdentityInterface(fields=['magnitude']), name='inputnode') | ||
| outputnode = pe.Node( | ||
| niu.IdentityInterface(fields=['fmap_ref', 'fmap_mask', 'mask_report']), | ||
| name='outputnode') | ||
|
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| # Merge input magnitude images | ||
| magmrg = pe.Node(IntraModalMerge(), name='magmrg') | ||
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| # de-gradient the fields ("bias/illumination artifact") | ||
| n4_correct = pe.Node(ants.N4BiasFieldCorrection(dimension=3, copy_header=True), | ||
| name='n4_correct', n_procs=omp_nthreads) | ||
| bet = pe.Node(BETRPT(generate_report=True, frac=0.6, mask=True), | ||
| name='bet') | ||
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| workflow.connect([ | ||
| (inputnode, magmrg, [('magnitude', 'in_files')]), | ||
| (magmrg, n4_correct, [('out_file', 'input_image')]), | ||
| (n4_correct, bet, [('output_image', 'in_file')]), | ||
| (bet, outputnode, [('mask_file', 'fmap_mask'), | ||
| ('out_file', 'fmap_ref'), | ||
| ('out_report', 'mask_report')]), | ||
| ]) | ||
| return workflow | ||
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| def init_fmap_postproc_wf(omp_nthreads, fmap_bspline, median_kernel_size=3, | ||
| name='fmap_postproc_wf'): | ||
| """ | ||
| Postprocess a B0 map estimated elsewhere. | ||
| This workflow denoises (mostly via smoothing) a B0 fieldmap. | ||
| Workflow Graph | ||
| .. workflow :: | ||
| :graph2use: orig | ||
| :simple_form: yes | ||
| from sdcflows.workflows.fmap import init_fmap_postproc_wf | ||
| wf = init_fmap_postproc_wf(omp_nthreads=6, fmap_bspline=False) | ||
| Parameters | ||
| ---------- | ||
| omp_nthreads : int | ||
| Maximum number of threads an individual process may use | ||
| fmap_bspline : bool | ||
| Whether the fieldmap should be smoothed and extrapolated to off-brain regions | ||
| using B-Spline basis. | ||
| median_kernel_size : int | ||
| Size of the kernel when smoothing is done with a median filter. | ||
| name : str | ||
| Name of workflow (default: ``fmap_postproc_wf``) | ||
| Inputs | ||
| ------ | ||
| fmap_mask : pathlike | ||
| A brain binary mask corresponding to this fieldmap. | ||
| fmap_ref : pathlike | ||
| A preprocessed magnitude/reference image for the fieldmap. | ||
| fmap : pathlike | ||
| A B0-field nonuniformity map (aka fieldmap) estimated elsewhere. | ||
| Outputs | ||
| ------- | ||
| out_fmap : pathlike | ||
| Postprocessed fieldmap. | ||
| """ | ||
| workflow = Workflow(name=name) | ||
| inputnode = pe.Node(niu.IdentityInterface( | ||
| fields=['fmap_mask', 'fmap_ref', 'fmap']), name='inputnode') | ||
| outputnode = pe.Node(niu.IdentityInterface(fields=['out_fmap']), | ||
| name='outputnode') | ||
| if fmap_bspline: | ||
| from ..interfaces.fmap import FieldEnhance | ||
| # despike_threshold=1.0, mask_erode=1), | ||
| fmapenh = pe.Node( | ||
| FieldEnhance(unwrap=False, despike=False), | ||
| name='fmapenh', mem_gb=4, n_procs=omp_nthreads) | ||
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| workflow.connect([ | ||
| (inputnode, fmapenh, [('fmap_mask', 'in_mask'), | ||
| ('fmap_ref', 'in_magnitude'), | ||
| ('fmap_hz', 'in_file')]), | ||
| (fmapenh, outputnode, [('out_file', 'out_fmap')]), | ||
| ]) | ||
|
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||
| else: | ||
| recenter = pe.Node(niu.Function(function=_recenter), | ||
| name='recenter', run_without_submitting=True) | ||
| denoise = pe.Node(fsl.SpatialFilter( | ||
| operation='median', kernel_shape='sphere', | ||
| kernel_size=median_kernel_size), name='denoise') | ||
| demean = pe.Node(niu.Function(function=_demean), name='demean') | ||
| cleanup_wf = cleanup_edge_pipeline(name="cleanup_wf") | ||
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| workflow.connect([ | ||
| (inputnode, cleanup_wf, [('fmap_mask', 'inputnode.in_mask')]), | ||
| (inputnode, recenter, [('fmap', 'in_file')]), | ||
| (recenter, denoise, [('out', 'in_file')]), | ||
| (denoise, demean, [('out_file', 'in_file')]), | ||
| (demean, cleanup_wf, [('out', 'inputnode.in_file')]), | ||
| (cleanup_wf, outputnode, [('outputnode.out_file', 'out_fmap')]), | ||
| ]) | ||
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| return workflow | ||
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| def _recenter(in_file): | ||
| """Recenter the phase-map distribution to the -pi..pi range.""" | ||
| from os import getcwd | ||
| import numpy as np | ||
| import nibabel as nb | ||
| from nipype.utils.filemanip import fname_presuffix | ||
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| nii = nb.load(in_file) | ||
| data = nii.get_fdata(dtype='float32') | ||
| msk = data != 0 | ||
| msk[data == 0] = False | ||
| data[msk] -= np.median(data[msk]) | ||
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| out_file = fname_presuffix(in_file, suffix='_recentered', | ||
| newpath=getcwd()) | ||
| nb.Nifti1Image(data, nii.affine, nii.header).to_filename(out_file) | ||
| return out_file | ||
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| def _demean(in_file, in_mask=None, usemode=True): | ||
| """ | ||
| Subtract the median (since it is robuster than the mean) from a map. | ||
| Parameters | ||
| ---------- | ||
| usemode : bool | ||
| Use the mode instead of the median (should be even more robust | ||
| against outliers). | ||
| """ | ||
| from os import getcwd | ||
| import numpy as np | ||
| import nibabel as nb | ||
| from nipype.utils.filemanip import fname_presuffix | ||
|
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| nii = nb.load(in_file) | ||
| data = nii.get_fdata(dtype='float32') | ||
|
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| msk = np.ones_like(data, dtype=bool) | ||
| if in_mask is not None: | ||
| msk[nb.load(in_mask).get_fdata(dtype='float32') < 1e-4] = False | ||
|
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| if usemode: | ||
| from scipy.stats import mode | ||
| data[msk] -= mode(data[msk], axis=None)[0][0] | ||
| else: | ||
| data[msk] -= np.median(data[msk], axis=None) | ||
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| out_file = fname_presuffix(in_file, suffix='_demean', | ||
| newpath=getcwd()) | ||
| nb.Nifti1Image(data, nii.affine, nii.header).to_filename(out_file) | ||
| return out_file |
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