diff --git a/dmriprep/config/emc_coarse_Affine.json b/dmriprep/config/emc_coarse_Affine.json
new file mode 100644
index 00000000..f7663871
--- /dev/null
+++ b/dmriprep/config/emc_coarse_Affine.json
@@ -0,0 +1,8 @@
+{
+    "level_iters": [1000, 100],
+    "metric": "MI",
+    "sigmas": [8.0, 2.0],
+    "factors": [2, 1],
+    "sampling_prop": 0.15,
+    "nbins": 48
+}
\ No newline at end of file
diff --git a/dmriprep/config/emc_coarse_Rigid.json b/dmriprep/config/emc_coarse_Rigid.json
new file mode 100644
index 00000000..5d23fba6
--- /dev/null
+++ b/dmriprep/config/emc_coarse_Rigid.json
@@ -0,0 +1,8 @@
+{
+    "level_iters": [100, 100],
+    "metric": "MI",
+    "sigmas": [8.0, 2.0],
+    "factors": [2, 1],
+    "sampling_prop": 0.15,
+    "nbins": 48
+}
diff --git a/dmriprep/config/emc_precise_Affine.json b/dmriprep/config/emc_precise_Affine.json
new file mode 100644
index 00000000..99fbb1e2
--- /dev/null
+++ b/dmriprep/config/emc_precise_Affine.json
@@ -0,0 +1,8 @@
+{
+    "level_iters": [1000, 1000],
+    "metric": "MI",
+    "sigmas": [8.0, 2.0],
+    "factors": [2, 1],
+    "sampling_prop": 0.15,
+    "nbins": 48
+}
\ No newline at end of file
diff --git a/dmriprep/config/emc_precise_Rigid.json b/dmriprep/config/emc_precise_Rigid.json
new file mode 100644
index 00000000..e62cd12a
--- /dev/null
+++ b/dmriprep/config/emc_precise_Rigid.json
@@ -0,0 +1,8 @@
+{
+    "level_iters": [1000, 1000],
+    "metric": "MI",
+    "sigmas": [8.0, 2.0],
+    "factors": [2, 1],
+    "sampling_prop": 0.15,
+    "nbins": 48
+}
diff --git a/dmriprep/interfaces/registration.py b/dmriprep/interfaces/registration.py
new file mode 100644
index 00000000..44f3256f
--- /dev/null
+++ b/dmriprep/interfaces/registration.py
@@ -0,0 +1,147 @@
+"""Register tools interfaces."""
+import numpy as np
+import nibabel as nb
+import dmriprep
+from nipype import logging
+from pathlib import Path
+from nipype.utils.filemanip import fname_presuffix
+from nipype.interfaces.base import (
+    traits,
+    TraitedSpec,
+    BaseInterfaceInputSpec,
+    InputMultiObject,
+    SimpleInterface,
+    File,
+)
+
+
+LOGGER = logging.getLogger("nipype.interface")
+REG_TYPES = ("c_of_mass", "translation", "rigid", "affine")
+
+
+class _ApplyAffineInputSpec(BaseInterfaceInputSpec):
+    moving_image = File(
+        exists=True, mandatory=True, desc="image that will be resampled into the reference"
+    )
+    fixed_image = File(
+        exists=True, mandatory=True, desc="image defining the reference coordinate system"
+    )
+    transform_affine = InputMultiObject(
+        File(exists=True), mandatory=True, desc="transformation affine"
+    )
+    invert_transform = traits.Bool(False, usedefault=True)
+
+
+class _ApplyAffineOutputSpec(TraitedSpec):
+    warped_image = File(exists=True, desc="Outputs warped image")
+
+
+class ApplyAffine(SimpleInterface):
+    """
+    Interface to apply an affine transformation to an image.
+    """
+
+    input_spec = _ApplyAffineInputSpec
+    output_spec = _ApplyAffineOutputSpec
+
+    def _run_interface(self, runtime):
+        from dmriprep.utils.registration import apply_affine
+
+        warped_image_nifti = apply_affine(
+            nb.load(self.inputs.moving_image),
+            nb.load(self.inputs.fixed_image),
+            np.load(self.inputs.transform_affine[0]),
+            self.inputs.invert_transform,
+        )
+        cwd = Path(runtime.cwd).absolute()
+        warped_file = fname_presuffix(
+            self.inputs.moving_image,
+            use_ext=False,
+            suffix="_warped.nii.gz",
+            newpath=str(cwd),
+        )
+
+        warped_image_nifti.to_filename(warped_file)
+
+        self._results["warped_image"] = warped_file
+        return runtime
+
+
+class _RegisterInputSpec(BaseInterfaceInputSpec):
+    moving_image = File(
+        exists=True, mandatory=True, desc="image to apply transformation from"
+    )
+    fixed_image = File(
+        exists=True, mandatory=True, desc="image to apply transformation to"
+    )
+    nbins = traits.Int(default_value=32, usedefault=True)
+    sampling_prop = traits.Float(default_value=1, usedefault=True)
+    metric = traits.Str(default_value="MI", usedefault=True)
+    level_iters = traits.List(
+        trait=traits.Any(), value=[10000, 1000, 100], usedefault=True
+    )
+    sigmas = traits.List(trait=traits.Any(), value=[5.0, 2.5, 0.0], usedefault=True)
+    factors = traits.List(trait=traits.Any(), value=[4, 2, 1], usedefault=True)
+    params0 = traits.ArrayOrNone(value=None, usedefault=True)
+    pipeline = traits.List(
+        traits.Enum(*REG_TYPES),
+        value=list(REG_TYPES),
+        usedefault=True,
+    )
+
+
+class _RegisterOutputSpec(TraitedSpec):
+    forward_transforms = traits.List(
+        File(exists=True), desc="List of output transforms for forward registration"
+    )
+    warped_image = File(exists=True, desc="Outputs warped image")
+
+
+class Register(SimpleInterface):
+    """
+    Interface to perform affine registration.
+    """
+
+    input_spec = _RegisterInputSpec
+    output_spec = _RegisterOutputSpec
+
+    def _run_interface(self, runtime):
+        from dmriprep.utils.registration import affine_registration
+
+        pipeline = [
+            getattr(dmriprep.utils.register, i)
+            for i in self.inputs.pipeline
+            if i in REG_TYPES
+        ]
+
+        warped_image_nifti, forward_transform_mat = affine_registration(
+            nb.load(self.inputs.moving_image),
+            nb.load(self.inputs.fixed_image),
+            self.inputs.nbins,
+            self.inputs.sampling_prop,
+            self.inputs.metric,
+            pipeline,
+            self.inputs.level_iters,
+            self.inputs.sigmas,
+            self.inputs.factors,
+            self.inputs.params0,
+        )
+        cwd = Path(runtime.cwd).absolute()
+        warped_file = fname_presuffix(
+            self.inputs.moving_image,
+            use_ext=False,
+            suffix="_warped.nii.gz",
+            newpath=str(cwd),
+        )
+        forward_transform_file = fname_presuffix(
+            self.inputs.moving_image,
+            use_ext=False,
+            suffix="_forward_transform.npy",
+            newpath=str(cwd),
+        )
+        warped_image_nifti.to_filename(warped_file)
+
+        np.save(forward_transform_file, forward_transform_mat)
+        self._results["warped_image"] = warped_file
+        self._results["forward_transforms"] = [forward_transform_file]
+        return runtime
diff --git a/dmriprep/utils/registration.py b/dmriprep/utils/registration.py
new file mode 100644
index 00000000..56b47084
--- /dev/null
+++ b/dmriprep/utils/registration.py
@@ -0,0 +1,170 @@
+"""
+Linear affine registration tools for motion correction.
+"""
+import numpy as np
+import nibabel as nb
+from dipy.align.metrics import CCMetric, EMMetric, SSDMetric
+from dipy.align.imaffine import (
+    transform_centers_of_mass,
+    AffineMap,
+    MutualInformationMetric,
+    AffineRegistration,
+)
+from dipy.align.transforms import (
+    TranslationTransform3D,
+    RigidTransform3D,
+    AffineTransform3D,
+)
+from nipype.utils.filemanip import fname_presuffix
+
+syn_metric_dict = {"CC": CCMetric, "EM": EMMetric, "SSD": SSDMetric}
+
+__all__ = [
+    "c_of_mass",
+    "translation",
+    "rigid",
+    "affine",
+    "affine_registration",
+]
+
+
+def apply_affine(moving, static, transform_affine, invert=False):
+    """Apply an affine to transform an image from one space to another.
+
+    Parameters
+    ----------
+    moving : array
+       The image to be resampled
+
+    static : array
+
+    Returns
+    -------
+    warped_img : the moving array warped into the static array's space.
+
+    """
+    affine_map = AffineMap(
+        transform_affine, static.shape, static.affine, moving.shape, moving.affine
+    )
+    if invert is True:
+        warped_arr = affine_map.transform_inverse(np.asarray(moving.dataobj))
+    else:
+        warped_arr = affine_map.transform(np.asarray(moving.dataobj))
+
+    return nb.Nifti1Image(warped_arr, static.affine)
+
+
+def average_affines(transforms):
+    affine_list = [np.load(aff) for aff in transforms]
+    average_affine_file = fname_presuffix(
+        transforms[0], use_ext=False, suffix="_average.npy"
+    )
+    np.save(average_affine_file, np.mean(affine_list, axis=0))
+    return average_affine_file
+
+
+# Affine registration pipeline:
+affine_metric_dict = {"MI": MutualInformationMetric, "CC": CCMetric}
+
+
+def c_of_mass(
+    moving, static, static_affine, moving_affine, reg, starting_affine, params0=None
+):
+    transform = transform_centers_of_mass(static, static_affine, moving, moving_affine)
+    transformed = transform.transform(moving)
+    return transformed, transform.affine
+
+
+def translation(
+    moving, static, static_affine, moving_affine, reg, starting_affine, params0=None
+):
+    transform = TranslationTransform3D()
+    translation = reg.optimize(
+        static,
+        moving,
+        transform,
+        params0,
+        static_affine,
+        moving_affine,
+        starting_affine=starting_affine,
+    )
+
+    return translation.transform(moving), translation.affine
+
+
+def rigid(
+    moving, static, static_affine, moving_affine, reg, starting_affine, params0=None
+):
+    transform = RigidTransform3D()
+    rigid = reg.optimize(
+        static,
+        moving,
+        transform,
+        params0,
+        static_affine,
+        moving_affine,
+        starting_affine=starting_affine,
+    )
+    return rigid.transform(moving), rigid.affine
+
+
+def affine(
+    moving, static, static_affine, moving_affine, reg, starting_affine, params0=None
+):
+    transform = AffineTransform3D()
+    affine = reg.optimize(
+        static,
+        moving,
+        transform,
+        params0,
+        static_affine,
+        moving_affine,
+        starting_affine=starting_affine,
+    )
+
+    return affine.transform(moving), affine.affine
+
+
+def affine_registration(
+    moving,
+    static,
+    nbins,
+    sampling_prop,
+    metric,
+    pipeline,
+    level_iters,
+    sigmas,
+    factors,
+    params0,
+):
+
+    """
+    Find the affine transformation between two 3D images.
+
+    Parameters
+    ----------
+
+    """
+    # Define the Affine registration object we'll use with the chosen metric:
+    use_metric = affine_metric_dict[metric](nbins, sampling_prop)
+    affreg = AffineRegistration(
+        metric=use_metric, level_iters=level_iters, sigmas=sigmas, factors=factors
+    )
+
+    if not params0:
+        starting_affine = np.eye(4)
+    else:
+        starting_affine = params0
+
+    # Go through the selected transformation:
+    for func in pipeline:
+        transformed, starting_affine = func(
+            np.asarray(moving.dataobj),
+            np.asarray(static.dataobj),
+            static.affine,
+            moving.affine,
+            affreg,
+            starting_affine,
+            params0,
+        )
+    return nb.Nifti1Image(np.array(transformed), static.affine), starting_affine
diff --git a/dmriprep/utils/tests/test_registration.py b/dmriprep/utils/tests/test_registration.py
new file mode 100644
index 00000000..926ebd12
--- /dev/null
+++ b/dmriprep/utils/tests/test_registration.py
@@ -0,0 +1,66 @@
+import pytest
+import numpy as np
+import numpy.testing as npt
+import nibabel as nb
+import dipy.data as dpd
+from dmriprep.utils.registration import affine_registration
+
+
+def setup_module():
+    global subset_b0, subset_dwi_data, subset_t2, subset_b0_img, \
+        subset_t2_img, gtab, hardi_affine, MNI_T2_affine
+    MNI_T2 = dpd.read_mni_template()
+    hardi_img, gtab = dpd.read_stanford_hardi()
+    MNI_T2_data = MNI_T2.get_fdata()
+    MNI_T2_affine = MNI_T2.affine
+    hardi_data = hardi_img.get_fdata()
+    hardi_affine = hardi_img.affine
+    b0 = hardi_data[..., gtab.b0s_mask]
+    mean_b0 = np.mean(b0, -1)
+
+    # Select some arbitrary chunks of data so this goes quicker
+    subset_b0 = mean_b0[40:50, 40:50, 40:50]
+    subset_dwi_data = nb.Nifti1Image(hardi_data[40:50, 40:50, 40:50], hardi_affine)
+    subset_t2 = MNI_T2_data[40:60, 40:60, 40:60]
+    subset_b0_img = nb.Nifti1Image(subset_b0, hardi_affine)
+    subset_t2_img = nb.Nifti1Image(subset_t2, MNI_T2_affine)
+
+
+@pytest.mark.parametrize("nbins", [32, 22])
+@pytest.mark.parametrize("sampling_prop", [1, 2])
+@pytest.mark.parametrize("metric", ["MI", "CC"])
+@pytest.mark.parametrize("level_iters", [[10000, 100], [1]])
+@pytest.mark.parametrize("sigmas", [[5.0, 2.5], [0.0]])
+@pytest.mark.parametrize("factors", [[4, 2], [1]])
+@pytest.mark.parametrize("params0", [np.eye(4), None])
+@pytest.mark.parametrize("pipeline", [["rigid"], ["affine"], ["rigid", "affine"]])
+def test_affine_registration(
+    nbins, sampling_prop, metric, level_iters, sigmas, factors, params0, pipeline
+):
+    moving = subset_b0
+    static = subset_b0
+    moving_affine = static_affine = np.eye(4)
+    xformed, affine = affine_registration(moving, static)
+    # We don't ask for much:
+    npt.assert_almost_equal(affine[:3, :3], np.eye(3), decimal=1)
+
+    with pytest.raises(ValueError):
+        # For array input, must provide affines:
+        xformed, affine = affine_registration(moving, static)
+
+    # If providing nifti image objects, don't need to provide affines:
+    moving_img = nb.Nifti1Image(moving, moving_affine)
+    static_img = nb.Nifti1Image(static, static_affine)
+    xformed, affine = affine_registration(
+        moving_img,
+        static_img,
+        nbins,
+        sampling_prop,
+        metric,
+        pipeline,
+        level_iters,
+        sigmas,
+        factors,
+        params0,
+    )
+    npt.assert_almost_equal(affine[:3, :3], np.eye(3), decimal=1)