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ENH: Minor refactor reorganizing base workflows, in prep for #97 #110

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36 changes: 25 additions & 11 deletions dmriprep/workflows/base.py
Original file line number Diff line number Diff line change
Expand Up @@ -12,9 +12,9 @@
BIDSInfo, BIDSFreeSurferDir
)
from niworkflows.utils.misc import fix_multi_T1w_source_name
from niworkflows.utils.spaces import Reference
from smriprep.workflows.anatomical import init_anat_preproc_wf

from niworkflows.utils.spaces import Reference
from ..interfaces import DerivativesDataSink, BIDSDataGrabber
from ..interfaces.reports import SubjectSummary, AboutSummary
from ..utils.bids import collect_data
Expand Down Expand Up @@ -253,21 +253,28 @@ def init_single_subject_wf(subject_id):
anat_preproc_wf.__postdesc__ = (anat_preproc_wf.__postdesc__ or "") + f"""
Diffusion data preprocessing

: For each of the {len(subject_data["dwi"])} dwi scans found per subject
(across all sessions), the following preprocessing was performed."""
: For each of the {len(subject_data["dwi"])} DWI scans found per subject
(across all sessions), the gradient table was vetted and converted into the *RASb*
format (i.e., given in RAS+ scanner coordinates, normalized b-vectors and scaled b-values),
and a *b=0* average for reference to the subsequent steps of preprocessing was calculated.
"""

layout = config.execution.layout
dwi_data = tuple([
(dwi, layout.get_metadata(dwi), layout.get_bvec(dwi), layout.get_bval(dwi))
for dwi in subject_data["dwi"]
])

inputnode = pe.Node(niu.IdentityInterface(fields=["dwi_data"]),
name="inputnode")
inputnode.iterables = [(
"dwi_data", tuple([
(dwi, layout.get_bvec(dwi), layout.get_bval(dwi),
layout.get_metadata(dwi)["PhaseEncodingDirection"])
for dwi in subject_data["dwi"]
])
)]
inputnode.iterables = [("dwi_data", dwi_data)]

referencenode = pe.JoinNode(niu.IdentityInterface(
fields=["dwi_file", "metadata", "dwi_reference", "dwi_mask", "gradients_rasb"]),
name="referencenode", joinsource="inputnode", run_without_submitting=True)

split_info = pe.Node(niu.Function(
function=_unpack, output_names=["dwi_file", "bvec", "bval", "pedir"]),
function=_unpack, output_names=["dwi_file", "metadata", "bvec", "bval"]),
name="split_info", run_without_submitting=True)

early_b0ref_wf = init_early_b0ref_wf()
Expand All @@ -276,6 +283,13 @@ def init_single_subject_wf(subject_id):
(split_info, early_b0ref_wf, [("dwi_file", "inputnode.dwi_file"),
("bvec", "inputnode.in_bvec"),
("bval", "inputnode.in_bval")]),
(split_info, referencenode, [("dwi_file", "dwi_file"),
("metadata", "metadata")]),
(early_b0ref_wf, referencenode, [
("outputnode.dwi_reference", "dwi_reference"),
("outputnode.dwi_mask", "dwi_mask"),
("outputnode.gradients_rasb", "gradients_rasb"),
]),
])

return workflow
Expand Down
7 changes: 1 addition & 6 deletions dmriprep/workflows/dwi/base.py
Original file line number Diff line number Diff line change
Expand Up @@ -53,11 +53,6 @@ def init_early_b0ref_wf(
"""
# Build workflow
workflow = Workflow(name=name)
workflow.__postdesc__ = """
For every run and subject, the gradient table was vetted and converted into the *RASb*
format (i.e., given in RAS+ scanner coordinates, normalized b-vectors and scaled b-values),
and a *b=0* average for reference to the subsequent steps of preprocessing was calculated.
"""

inputnode = pe.Node(niu.IdentityInterface(
fields=['dwi_file', 'in_bvec', 'in_bval']),
Expand All @@ -84,7 +79,7 @@ def init_early_b0ref_wf(
(dwi_reference_wf, outputnode, [
('outputnode.ref_image', 'dwi_reference'),
('outputnode.dwi_mask', 'dwi_mask')]),
(gradient_table, outputnode, [('out_rasb', 'out_rasb')])
(gradient_table, outputnode, [('out_rasb', 'gradients_rasb')])
])

# REPORTING ############################################################
Expand Down
30 changes: 26 additions & 4 deletions dmriprep/workflows/dwi/util.py
Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
from niworkflows.engine.workflows import LiterateWorkflow as Workflow
from niworkflows.interfaces.images import ValidateImage
from niworkflows.interfaces.fixes import FixN4BiasFieldCorrection as N4BiasFieldCorrection
from niworkflows.interfaces.nibabel import ApplyMask
from niworkflows.interfaces.utils import CopyXForm

from ...interfaces.images import ExtractB0, RescaleB0
Expand Down Expand Up @@ -212,8 +213,10 @@ def init_enhance_and_skullstrip_dwi_wf(
combine_masks = pe.Node(fsl.BinaryMaths(operation='mul'),
name='combine_masks')

normalize = pe.Node(niu.Function(function=_normalize), name="normalize")

# Compute masked brain
apply_mask = pe.Node(fsl.ApplyMask(), name='apply_mask')
apply_mask = pe.Node(ApplyMask(), name='apply_mask')

workflow.connect([
(inputnode, n4_correct, [('in_file', 'input_image'),
Expand All @@ -230,11 +233,30 @@ def init_enhance_and_skullstrip_dwi_wf(
(skullstrip_first_pass, combine_masks, [('mask_file', 'in_file')]),
(skullstrip_second_pass, fixhdr_skullstrip2, [('out_file', 'in_file')]),
(fixhdr_skullstrip2, combine_masks, [('out_file', 'operand_file')]),
(fixhdr_unifize, apply_mask, [('out_file', 'in_file')]),
(combine_masks, apply_mask, [('out_file', 'mask_file')]),
(combine_masks, apply_mask, [('out_file', 'in_mask')]),
(combine_masks, outputnode, [('out_file', 'mask_file')]),
(n4_correct, normalize, [('output_image', 'in_file')]),
(normalize, apply_mask, [('out', 'in_file')]),
(normalize, outputnode, [('out', 'bias_corrected_file')]),
(apply_mask, outputnode, [('out_file', 'skull_stripped_file')]),
(n4_correct, outputnode, [('output_image', 'bias_corrected_file')]),
])

return workflow


def _normalize(in_file, newmax=2000, perc=98.0):
from pathlib import Path
import numpy as np
import nibabel as nb

nii = nb.load(in_file)
data = nii.get_fdata()
data[data < 0] = 0
if data.max() >= 2**15 - 1:
data *= newmax / np.percentile(data.reshape(-1), perc)

out_file = str(Path("normalized.nii.gz").absolute())
hdr = nii.header.copy()
hdr.set_data_dtype('int16')
nii.__class__(data.astype('int16'), nii.affine, hdr).to_filename(out_file)
return out_file