Merge pull request #1353 from poldracklab/revert-1283-enh/3dvolreg

Revert #1283 enh/3dvolreg

Author: oesteban

.circleci/config.yml

@@ -537,7 +537,7 @@ jobs:
                 -e FMRIPREP_DEV 1 \
                 --config $PWD/nipype.cfg -w /tmp/ds054/work \
                 /tmp/data/ds054 /tmp/ds054/derivatives participant \
-                --fs-no-reconall --sloppy --hmc-use-mcflirt \
+                --fs-no-reconall --sloppy \
                 --output-space T1w template \
                 --template-resampling-grid 2mm \
                 --mem_mb 4096 --nthreads 2 -vv

docs/citing.rst

@@ -100,8 +100,8 @@ we recommend to include in your paper.
 
    <p style="font-style: italic;">
      Functional data was <span class="slicetime_text_true">slice time corrected using
-     <code>3dTshift</code> (AFNI v16.2.07 [11, RRID:SCR_005927]) and </span>
-     motion-corrected using <code>3dVolreg</code> (AFNI v16.2.07 [11, RRID:SCR_005927]).
+     <code>3dTshift</code> from AFNI v16.2.07 [11, RRID:SCR_005927]
+     and </span>motion corrected using <code>mcflirt</code> (FSL v5.0.9 [9]).
      <span class="SDC_text_TOPUP" style="display: none">Distortion correction was performed
      using an implementation of the TOPUP technique [10] using <code>3dQwarp</code> (AFNI v16.2.07 [11]).</span>
      <span class="SDC_text_FUGUE" style="display: none">Distortion correction was performed using fieldmaps

docs/outputs.rst

@@ -77,8 +77,8 @@ Derivatives related to EPI files are in the ``func`` subfolder.
 Volumetric output spaces include ``T1w`` and ``MNI152NLin2009cAsym`` (default).
 
 - ``*bold_space-<space>_brainmask.nii.gz`` Brain mask for EPI files, calculated by nilearn on the average EPI volume, post-motion correction
-- ``*bold_space-<space>_preproc.nii.gz`` Head-motion corrected EPI file
-- (optional) ``*bold_space-<space>_variant-smoothAROMAnonaggr_preproc.nii.gz`` Head-motion corrected,
+- ``*bold_space-<space>_preproc.nii.gz`` Motion-corrected (using MCFLIRT for estimation and ANTs for interpolation) EPI file
+- (optional) ``*bold_space-<space>_variant-smoothAROMAnonaggr_preproc.nii.gz`` Motion-corrected (using MCFLIRT for estimation and ANTs for interpolation),
   smoothed (6mm), and non-aggressively denoised (using AROMA) EPI file - currently produced only for the ``MNI152NLin2009cAsym`` space
 
 Surface output spaces include ``fsnative`` (full density subject-specific mesh),

docs/workflows.rst

@@ -51,7 +51,6 @@ is presented below:
         use_aroma=False,
         aroma_melodic_dim=None,
         ignore_aroma_err=False,
-        use_mcflirt=False,
     )
 
 
@@ -274,7 +273,6 @@ BOLD preprocessing
         use_aroma=False,
         aroma_melodic_dim=None,
         ignore_aroma_err=False,
-        use_mcflirt=False,
     )
 
 Preprocessing of :abbr:`BOLD (blood-oxygen level-dependent)` files is
@@ -336,12 +334,11 @@ Head-motion estimation
 
     from fmriprep.workflows.bold import init_bold_hmc_wf
     wf = init_bold_hmc_wf(
-        use_mcflirt=False,
         mem_gb=1,
         omp_nthreads=1)
 
 Using the previously :ref:`estimated reference scan <bold_ref>`,
-AFNI's ``3dVolreg`` is used to estimate head-motion.
+FSL ``mcflirt`` is used to estimate head-motion.
 As a result, one rigid-body transform with respect to
 the reference image is written for each :abbr:`BOLD (blood-oxygen level-dependent)`
 time-step.
@@ -551,7 +548,7 @@ Confounds estimation
         metadata={"RepetitionTime": 2.0,
                   "SliceTiming": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]})
 
-Given a motion-corrected fMRI, a brain mask, head-motion parameters and a
+Given a motion-corrected fMRI, a brain mask, ``mcflirt`` movement parameters and a
 segmentation, the `discover_wf` sub-workflow calculates potential
 confounds per volume.
 

fmriprep/__about__.py

@@ -105,7 +105,6 @@
     'scikit-image',
     'versioneer',
     'pyyaml',
-    'transforms3d',
 ]
 
 LINKS_REQUIRES = [

fmriprep/cli/run.py

@@ -160,10 +160,6 @@ def get_parser():
         '--medial-surface-nan', required=False, action='store_true', default=False,
         help='Replace medial wall values with NaNs on functional GIFTI files. Only '
         'performed for GIFTI files mapped to a freesurfer subject (fsaverage or fsnative).')
-    g_conf.add_argument(
-        '--hmc-use-mcflirt', required=False, action='store_true', default=False,
-        help='Head-Motion Correction (HMC) - use FSL\'s ``mcflirt`` instead '
-        'of AFNI\'s ``3dVolreg``.')
 
     # ICA_AROMA options
     g_aroma = parser.add_argument_group('Specific options for running ICA_AROMA')
@@ -558,7 +554,6 @@ def build_workflow(opts, retval):
         use_aroma=opts.use_aroma,
         aroma_melodic_dim=opts.aroma_melodic_dimensionality,
         ignore_aroma_err=opts.ignore_aroma_denoising_errors,
-        use_mcflirt=opts.hmc_use_mcflirt,
     )
     retval['return_code'] = 0
 

fmriprep/interfaces/__init__.py

@@ -16,5 +16,5 @@
 from .utils import TPM2ROI, AddTPMs, AddTSVHeader, ConcatAffines, JoinTSVColumns
 from .fmap import FieldEnhance, FieldToRadS, FieldToHz, Phasediff2Fieldmap
 from .confounds import GatherConfounds, ICAConfounds, FMRISummary
-from .itk import MCFLIRT2ITK, Volreg2ITK, MultiApplyTransforms
+from .itk import MCFLIRT2ITK, MultiApplyTransforms
 from .multiecho import FirstEcho

fmriprep/interfaces/images.py

@@ -21,7 +21,6 @@
     traits, TraitedSpec, BaseInterfaceInputSpec, SimpleInterface,
     File, InputMultiPath, OutputMultiPath)
 from nipype.interfaces import fsl
-from fmriprep.utils.misc import remove_rotation_and_shear
 
 LOGGER = logging.getLogger('nipype.interface')
 
@@ -294,7 +293,6 @@ def _run_interface(self, runtime):
 
 class ValidateImageInputSpec(BaseInterfaceInputSpec):
     in_file = File(exists=True, mandatory=True, desc='input image')
-    remove_rotation_and_shear = traits.Bool(False, usedefault=True)
 
 
 class ValidateImageOutputSpec(TraitedSpec):
@@ -344,7 +342,6 @@ class ValidateImage(SimpleInterface):
     output_spec = ValidateImageOutputSpec
 
     def _run_interface(self, runtime):
-
         img = nb.load(self.inputs.in_file)
         out_report = os.path.join(runtime.cwd, 'report.html')
 
@@ -373,14 +370,7 @@ def _run_interface(self, runtime):
 
         # Both match, qform valid (implicit with match), codes okay -> do nothing, empty report
         if matching_affines and qform_code > 0 and sform_code > 0:
-            if self.inputs.remove_rotation_and_shear:
-                out_fname = fname_presuffix(self.inputs.in_file, suffix='_valid',
-                                            newpath=runtime.cwd)
-                img = remove_rotation_and_shear(img)
-                img.to_filename(out_fname)
-                self._results['out_file'] = out_fname
-            else:
-                self._results['out_file'] = self.inputs.in_file
+            self._results['out_file'] = self.inputs.in_file
             open(out_report, 'w').close()
             self._results['out_report'] = out_report
             return runtime
@@ -428,9 +418,6 @@ def _run_interface(self, runtime):
 </p>
 """
         snippet = '<h3 class="elem-title">%s</h3>\n%s\n' % (warning_txt, description)
-
-        if self.inputs.remove_rotation_and_shear:
-            img = remove_rotation_and_shear(img)
         # Store new file and report
         img.to_filename(out_fname)
         with open(out_report, 'w') as fobj:

fmriprep/interfaces/itk.py

@@ -9,7 +9,6 @@
 
 """
 import os
-from pathlib import Path
 from mimetypes import guess_type
 from tempfile import TemporaryDirectory
 import numpy as np
@@ -79,49 +78,6 @@ def _run_interface(self, runtime):
         return runtime
 
 
-class Volreg2ITKInputSpec(BaseInterfaceInputSpec):
-    in_file = File(exists=True, mandatory=True,
-                   desc='mat file generated by AFNI\'s 3dVolreg')
-
-
-class Volreg2ITKOutputSpec(TraitedSpec):
-    out_file = File(desc='the output ITKTransform file')
-
-
-class Volreg2ITK(SimpleInterface):
-
-    """
-    Convert an AFNI's mat file into an ITK Transform file.
-    """
-    input_spec = Volreg2ITKInputSpec
-    output_spec = Volreg2ITKOutputSpec
-
-    def _run_interface(self, runtime):
-        # Load AFNI mat entries and reshape appropriately
-        orig_afni_mat = np.loadtxt(self.inputs.in_file)
-        afni_affines = [mat.reshape(3, 4, order='C') for mat in orig_afni_mat]
-
-        out_file = Path(fname_presuffix(self.inputs.in_file, use_ext=False,
-                        suffix='_mc4d_itk.txt', newpath=runtime.cwd))
-
-        fixed_params = 'FixedParameters: 0 0 0'  # Center of rotation does not change
-        lines = ["#Insight Transform File V1.0"]
-        for i, affine in enumerate(afni_affines):
-            lines.append("#Transform %d" % i)
-            lines.append("Transform: AffineTransform_double_3_3")
-
-            ants_affine_2d = np.hstack((affine[:3, :3].reshape(1, -1),
-                                        affine[:3, 3].reshape(1, -1)))
-            params = ants_affine_2d.reshape(-1).astype('float64')
-            params_list = ["%g" % i for i in params.tolist()]
-            lines.append("Parameters: %s" % ' '.join(params_list))
-            lines.append(fixed_params)
-
-        out_file.write_text('\n'.join(lines))
-        self._results['out_file'] = str(out_file)
-        return runtime
-
-
 class MultiApplyTransformsInputSpec(ApplyTransformsInputSpec):
     input_image = InputMultiPath(File(exists=True), mandatory=True,
                                  desc='input time-series as a list of volumes after splitting'

fmriprep/utils/misc.py

@@ -7,30 +7,6 @@
 """
 
 
-def remove_rotation_and_shear(img):
-    from transforms3d.affines import decompose, compose
-    import numpy as np
-
-    T, _, Z, _ = decompose(img.affine)
-    affine = compose(T=T, R=np.diag([1, 1, 1]), Z=Z)
-    return img.__class__(np.asanyarray(img.dataobj), affine, img.header)
-
-
-def split_and_rm_rotshear_func(in_file):
-    import os
-    import nibabel as nb
-    from fmriprep.utils.misc import remove_rotation_and_shear
-    out_files = []
-    imgs = nb.four_to_three(nb.load(in_file))
-    for i, img in enumerate(imgs):
-        out_file = os.path.abspath('vol%04d.nii.gz' % i)
-        img = remove_rotation_and_shear(
-            nb.as_closest_canonical(img))
-        img.to_filename(out_file)
-        out_files.append(out_file)
-    return out_files
-
-
 def fix_multi_T1w_source_name(in_files):
     """
     Make up a generic source name when there are multiple T1s