[ENH] Automatically use Nilearn's EPI mask when no explicit mask is provided

docs/approach.rst

@@ -45,6 +45,16 @@ number of echoes with "good" signal. When :math:`T_{2}^*` and :math:`S_{0}` are
 calculated below, each voxel's values are only calculated from the first :math:`n`
 echoes, where :math:`n` is the value for that voxel in the adaptive mask.
 
+.. note::
+    ``tedana`` allows users to provide their own mask. The adaptive mask will
+    be computed on this explicit mask, and may reduce it further based on the
+    data.
+    If a mask is not provided, ``tedana`` runs `nilearn.masking.compute_epi_mask`_
+    on the first echo's data to derive a mask prior to adaptive masking.
+    The workflow does this because the adaptive mask generation function
+    sometimes identifies almost the entire bounding box as "brain", and
+    ``compute_epi_mask`` restricts analysis to a more reasonable area.
+
 .. image:: /_static/03_adaptive_mask.png
   :width: 600 px
   :align: center
@@ -211,4 +221,5 @@ robust PCA.
 
 .. image:: /_static/16_t1c_denoised_data_timeseries.png
 
+.. _nilearn.masking.compute_epi_mask: https://nilearn.github.io/modules/generated/nilearn.masking.compute_epi_mask.html
 .. _Power et al. (2018): http://www.pnas.org/content/early/2018/02/07/1720985115.short

tedana/workflows/tedana.py

@@ -18,6 +18,7 @@
 import numpy as np
 import pandas as pd
 from scipy import stats
+from nilearn.masking import compute_epi_mask
 
 from tedana.workflows.parser_utils import is_valid_file
 from tedana import decay, combine, decomposition, io, model, selection, utils
@@ -60,9 +61,12 @@ def _get_parser():
                           dest='mask',
                           metavar='FILE',
                           type=lambda x: is_valid_file(parser, x),
-                          help=('Binary mask of voxels to include in TE '
-                                'Dependent ANAlysis. Must be in the same '
-                                'space as `data`.'),
+                          help=("Binary mask of voxels to include in TE "
+                                "Dependent ANAlysis. Must be in the same "
+                                "space as `data`. If an explicit mask is not "
+                                "provided, then Nilearn's compute_epi_mask "
+                                "function will be used to derive a mask "
+                                "from the first echo's data."),
                           default=None)
     optional.add_argument('--mix',
                           dest='mixm',
@@ -187,7 +191,9 @@ def tedana_workflow(data, tes, mask=None, mixm=None, ctab=None, manacc=None,
         List of echo times associated with data in milliseconds.
     mask : :obj:`str`, optional
         Binary mask of voxels to include in TE Dependent ANAlysis. Must be
-        spatially aligned with `data`.
+        spatially aligned with `data`. If an explicit mask is not provided,
+        then Nilearn's compute_epi_mask function will be used to derive a mask
+        from the first echo's data.
     mixm : :obj:`str`, optional
         File containing mixing matrix. If not provided, ME-PCA and ME-ICA are
         done.
@@ -293,7 +299,9 @@ def tedana_workflow(data, tes, mask=None, mixm=None, ctab=None, manacc=None,
         raise IOError('Argument "ctab" must be an existing file.')
 
     if mask is None:
-        LGR.info('Computing adaptive mask')
+        LGR.info('Computing EPI mask from first echo')
+        first_echo_img = io.new_nii_like(ref_img, catd[:, 0, :])
+        mask = compute_epi_mask(first_echo_img)
     else:
         # TODO: add affine check
         LGR.info('Using user-defined mask')