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DOC: ME | Add missing output to documentation, improve boilerplate #2608

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Oct 20, 2021
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DOC: ME | Add missing output to documentation, improve boilerplate #2608

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DOC: ME | Add missing output to documentation, improve boilerplate
oesteban Oct 20, 2021
dfd99be
Update t2s.py
oesteban Oct 20, 2021
43070d6
Update t2s.py
oesteban Oct 20, 2021
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oesteban Oct 20, 2021
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22 changes: 11 additions & 11 deletions fmriprep/workflows/bold/t2s.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,15 +40,13 @@
# pylint: disable=R0914
def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,
name='bold_t2s_wf'):
"""
r"""
Combine multiple echos of :abbr:`ME-EPI (multi-echo echo-planar imaging)`.

This workflow wraps the `tedana`_ `T2* workflow`_ to optimally
combine multiple echos and derive a T2* map.
combine multiple preprocessed echos and derive a T2\ :sup:`★` map.
The following steps are performed:

#. :abbr:`HMC (head motion correction)` on individual echo files.
#. Compute the T2* map
#. Compute the T2\ :sup:`★` map
#. Create an optimally combined ME-EPI time series

.. _tedana: https://github.com/me-ica/tedana
Expand Down Expand Up @@ -76,18 +74,20 @@ def init_bold_t2s_wf(echo_times, mem_gb, omp_nthreads,
-------
bold
the optimally combined time series for all supplied echos
t2star_map
the calculated T2\ :sup:`★` map

"""
from niworkflows.engine.workflows import LiterateWorkflow as Workflow

workflow = Workflow(name=name)
workflow.__desc__ = """\
A T2\\* map was estimated from the preprocessed BOLD by fitting to a monoexponential signal
decay model with nonlinear regression, using T2\\*/S0 estimates from a log-linear
regression fit as initial values.
For each voxel, the maximal number of echoes with reliable signal in that voxel were
used to fit the model.
The calculated T2\\* map was then used to optimally combine preprocessed BOLD across
A T2<sup>★</sup> map was estimated from the preprocessed EPI echoes, by voxel-wise fitting
the maximal number of echoes with reliable signal in that voxel to a monoexponential signal
decay model with nonlinear regression.
The T2<sup>★</sup>/S<sub>0</sub> estimates from a log-linear regression fit were used for
initial values.
The calculated T2<sup>★</sup> map was then used to optimally combine preprocessed BOLD across
echoes following the method described in [@posse_t2s].
The optimally combined time series was carried forward as the *preprocessed BOLD*.
"""
Expand Down