diff --git a/docs/notebooks/data_structures.ipynb b/docs/notebooks/data_structures.ipynb
new file mode 100644
index 000000000..8b2a68f31
--- /dev/null
+++ b/docs/notebooks/data_structures.ipynb
@@ -0,0 +1,268 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/oesteban/.miniconda/envs/nifreeze/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "from dipy.data import get_fnames\n",
+    "from nireports.reportlets.modality.dwi import plot_dwi, plot_gradients\n",
+    "\n",
+    "from nifreeze.data import dmri"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get data from DIPY\n",
+    "fimg, fbvals, fbvecs = get_fnames(name=\"small_25\")\n",
+    "\n",
+    "# Load from NIfTI + b-vecs/vals\n",
+    "dwi = dmri.load(\n",
+    "    fimg,\n",
+    "    bvec_file=fbvecs,\n",
+    "    bval_file=fbvals,\n",
+    ")\n",
+    "\n",
+    "# Check number of DWIs (i.e., b > 0)\n",
+    "len(dwi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 660x350 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Select item 10 (volume, head-motion affine, gradient)\n",
+    "data, _, grad = dwi[10]\n",
+    "\n",
+    "# and plot\n",
+    "plot_dwi(data, dwi.affine, gradient=grad);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_gradients(dwi.gradients.T);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "192"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get data from DIPY\n",
+    "fimg, fbvals, fbvecs = get_fnames(name=\"sherbrooke_3shell\")\n",
+    "\n",
+    "# Load from NIfTI + b-vecs/vals\n",
+    "dwi = dmri.load(\n",
+    "    fimg,\n",
+    "    bvec_file=fbvecs,\n",
+    "    bval_file=fbvals,\n",
+    ")\n",
+    "\n",
+    "# Check number of DWIs (i.e., b > 0)\n",
+    "len(dwi)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 660x350 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Select item 10 (volume, head-motion affine, gradient)\n",
+    "data, _, grad = dwi[10]\n",
+    "\n",
+    "# and plot\n",
+    "plot_dwi(data, dwi.affine, gradient=grad);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 660x350 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Select item 80 (volume, head-motion affine, gradient)\n",
+    "data, _, grad = dwi[80]\n",
+    "\n",
+    "# and plot\n",
+    "plot_dwi(data, dwi.affine, gradient=grad);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_gradients(dwi.gradients.T);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(True, True)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Select a b-value\n",
+    "b2000_gradientmask = dwi.gradients[-1, ...] == 2000\n",
+    "\n",
+    "# Select b=2000\n",
+    "data, _, grad = dwi[b2000_gradientmask]\n",
+    "\n",
+    "# Check correct filtering\n",
+    "data.shape[-1] == b2000_gradientmask.sum(), set(grad[-1, ...]) == {2000.0}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 660x350 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot volume 10 of b=2000 shell\n",
+    "plot_dwi(data[..., 10], dwi.affine, gradient=grad[..., 10]);"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "nifreeze",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/nifreeze/data/base.py b/src/nifreeze/data/base.py
new file mode 100644
index 000000000..9eae6edd8
--- /dev/null
+++ b/src/nifreeze/data/base.py
@@ -0,0 +1,289 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# Copyright The NiPreps Developers <nipreps@gmail.com>
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# We support and encourage derived works from this project, please read
+# about our expectations at
+#
+#     https://www.nipreps.org/community/licensing/
+#
+"""Four-dimensional data representation in hard-disk and memory."""
+
+from __future__ import annotations
+
+from collections import namedtuple
+from pathlib import Path
+from tempfile import mkdtemp
+from typing import Any
+
+import attr
+import h5py
+import nibabel as nb
+import numpy as np
+from nitransforms.linear import Affine
+
+NFDH5_EXT = ".h5"
+
+
+def _data_repr(value: np.ndarray | None) -> str:
+    if value is None:
+        return "None"
+    return f"<{'x'.join(str(v) for v in value.shape)} ({value.dtype})>"
+
+
+def _cmp(lh: Any, rh: Any) -> bool:
+    if isinstance(lh, np.ndarray) and isinstance(rh, np.ndarray):
+        return np.allclose(lh, rh)
+
+    return lh == rh
+
+
+@attr.s(slots=True)
+class BaseDataset:
+    """
+    Base dataset representation structure.
+
+    A general data structure to represent 4D images and the necessary metadata
+    for head-motion estimation (that is, potentially a brain mask and the head-motion
+    estimates).
+
+    The data structure has a direct HDF5 mapping to facilitate memory efficiency.
+    For modalities requiring additional metadata such as DWI (which requires the gradient table
+    and potentially a b=0 reference), this class may be derived to override certain behaviors
+    (in the case of DWIs, the indexed access should also return the corresponding gradient
+    specification).
+
+    """
+
+    dataobj = attr.ib(default=None, repr=_data_repr, eq=attr.cmp_using(eq=_cmp))
+    """A :obj:`~numpy.ndarray` object for the data array."""
+    affine = attr.ib(default=None, repr=_data_repr, eq=attr.cmp_using(eq=_cmp))
+    """Best affine for RAS-to-voxel conversion of coordinates (NIfTI header)."""
+    brainmask = attr.ib(default=None, repr=_data_repr, eq=attr.cmp_using(eq=_cmp))
+    """A boolean ndarray object containing a corresponding brainmask."""
+    motion_affines = attr.ib(default=None, eq=attr.cmp_using(eq=_cmp))
+    """List of :obj:`~nitransforms.linear.Affine` realigning the dataset."""
+    datahdr = attr.ib(default=None)
+    """A :obj:`~nibabel.spatialimages.SpatialHeader` header corresponding to the data."""
+
+    _filepath = attr.ib(
+        factory=lambda: Path(mkdtemp()) / "hmxfms_cache.h5",
+        repr=False,
+        eq=False,
+    )
+    """A path to an HDF5 file to store the whole dataset."""
+
+    def __len__(self) -> int:
+        """Obtain the number of volumes/frames in the dataset."""
+        if self.dataobj is None:
+            return 0
+
+        return self.dataobj.shape[-1]
+
+    def __getitem__(
+        self, idx: int | slice | tuple | np.ndarray
+    ) -> tuple[np.ndarray, np.ndarray | None]:
+        """
+        Returns volume(s) and corresponding affine(s) through fancy indexing.
+
+        Parameters
+        ----------
+        idx : :obj:`int` or :obj:`slice` or :obj:`tuple` or :obj:`~numpy.ndarray`
+            Indexer for the last dimension (or possibly other dimensions if extended).
+
+        Returns
+        -------
+        volumes : :obj:`~numpy.ndarray`
+            The selected data subset.
+            If ``idx`` is a single integer, this will have shape ``(X, Y, Z)``,
+            otherwise it may have shape ``(X, Y, Z, k)``.
+        motion_affine : :obj:`~numpy.ndarray` or ``None``
+            The corresponding per-volume motion affine(s) or ``None`` if identity transform(s).
+
+        """
+        if self.dataobj is None:
+            raise ValueError("No data available (dataobj is None).")
+
+        affine = self.motion_affines[idx] if self.motion_affines is not None else None
+        return self.dataobj[..., idx], affine
+
+    @classmethod
+    def from_filename(cls, filename: Path | str) -> BaseDataset:
+        """
+        Read an HDF5 file from disk and create a BaseDataset.
+
+        Parameters
+        ----------
+        filename : :obj:`os.pathlike`
+            The HDF5 file path to read.
+
+        Returns
+        -------
+        :obj:`~nifreeze.data.base.BaseDataset`
+            The constructed dataset with data loaded from the file.
+
+        """
+        with h5py.File(filename, "r") as in_file:
+            root = in_file["/0"]
+            data = {k: np.asanyarray(v) for k, v in root.items() if not k.startswith("_")}
+        return cls(**data)
+
+    def get_filename(self) -> Path:
+        """Get the filepath of the HDF5 file."""
+        return self._filepath
+
+    def set_transform(self, index: int, affine: np.ndarray, order: int = 3) -> None:
+        """
+        Set an affine transform for a particular index and update the data object.
+
+        Parameters
+        ----------
+        index : :obj:`int`
+            The volume index to transform.
+        affine : :obj:`numpy.ndarray`
+            The 4x4 affine matrix to be applied.
+        order : :obj:`int`, optional
+            The order of the spline interpolation.
+
+        """
+        reference = namedtuple("ImageGrid", ("shape", "affine"))(
+            shape=self.dataobj.shape[:3], affine=self.affine
+        )
+
+        xform = Affine(matrix=affine, reference=reference)
+
+        if not Path(self._filepath).exists():
+            self.to_filename(self._filepath)
+
+        # read original DWI data & b-vector
+        with h5py.File(self._filepath, "r") as in_file:
+            root = in_file["/0"]
+            dataframe = np.asanyarray(root["dataobj"][..., index])
+
+        datamoving = nb.Nifti1Image(dataframe, self.affine, None)
+
+        # resample and update orientation at index
+        self.dataobj[..., index] = np.asanyarray(
+            xform.apply(datamoving, order=order).dataobj,
+            dtype=self.dataobj.dtype,
+        )
+
+        # if head motion affines are to be used, initialized to identities
+        if self.motion_affines is None:
+            self.motion_affines = np.repeat(np.zeros((4, 4))[None, ...], len(self), axis=0)
+
+        self.motion_affines[index] = xform.matrix
+
+    def to_filename(
+        self, filename: Path | str, compression: str | None = None, compression_opts: Any = None
+    ) -> None:
+        """
+        Write an HDF5 file to disk.
+
+        Parameters
+        ----------
+        filename : :obj:`os.pathlike`
+            The HDF5 file path to write to.
+        compression : :obj:`str`, optional
+            Compression strategy.
+            See :obj:`~h5py.Group.create_dataset` documentation.
+        compression_opts : :obj:`typing.Any`, optional
+            Parameters for compression
+            `filters <https://docs.h5py.org/en/stable/high/dataset.html#dataset-compression>`__.
+
+        """
+        filename = Path(filename)
+        if not filename.name.endswith(NFDH5_EXT):
+            filename = filename.parent / f"{filename.name}.h5"
+
+        with h5py.File(filename, "w") as out_file:
+            out_file.attrs["Format"] = "NFDH5"  # NiFreeze Data HDF5
+            out_file.attrs["Version"] = np.uint16(1)
+            root = out_file.create_group("/0")
+            root.attrs["Type"] = "base dataset"
+            for f in attr.fields(self.__class__):
+                if f.name.startswith("_"):
+                    continue
+
+                value = getattr(self, f.name)
+                if value is not None:
+                    root.create_dataset(
+                        f.name,
+                        data=value,
+                        compression=compression,
+                        compression_opts=compression_opts,
+                    )
+
+    def to_nifti(self, filename: Path) -> None:
+        """
+        Write a NIfTI file to disk.
+
+        Parameters
+        ----------
+        filename : :obj:`os.pathlike`
+            The output NIfTI file path.
+
+        """
+        nii = nb.Nifti1Image(self.dataobj, self.affine, self.datahdr)
+        if self.datahdr is None:
+            nii.header.set_xyzt_units("mm")
+        nii.to_filename(filename)
+
+
+def load(
+    filename: Path | str,
+    brainmask_file: Path | str | None = None,
+    motion_file: Path | str | None = None,
+) -> BaseDataset:
+    """
+    Load 4D data from a filename or an HDF5 file.
+
+    Parameters
+    ----------
+    filename : :obj:`os.pathlike`
+        The NIfTI or HDF5 file.
+    brainmask_file : :obj:`os.pathlike`, optional
+        A brainmask NIfTI file. If provided, will be loaded and
+        stored in the returned dataset.
+    motion_file : :obj:`os.pathlike`
+        A file containing head-motion affine matrices (linear).
+
+    Returns
+    -------
+    :obj:`~nifreeze.data.base.BaseDataset`
+        The loaded dataset.
+
+    Raises
+    ------
+    ValueError
+        If the file extension is not supported or the file cannot be loaded.
+
+    """
+    if motion_file:
+        raise NotImplementedError
+
+    filename = Path(filename)
+    if filename.name.endswith(NFDH5_EXT):
+        return BaseDataset.from_filename(filename)
+
+    img = nb.load(filename)
+    retval = BaseDataset(dataobj=img.dataobj, affine=img.affine)
+
+    if brainmask_file:
+        mask = nb.load(brainmask_file)
+        retval.brainmask = np.asanyarray(mask.dataobj)
+
+    return retval
diff --git a/src/nifreeze/data/dmri.py b/src/nifreeze/data/dmri.py
index 301f553a1..6a32496ca 100644
--- a/src/nifreeze/data/dmri.py
+++ b/src/nifreeze/data/dmri.py
@@ -20,11 +20,13 @@
 #
 #     https://www.nipreps.org/community/licensing/
 #
-"""Representing data in hard-disk and memory."""
+"""dMRI data representation."""
+
+from __future__ import annotations
 
 from collections import namedtuple
 from pathlib import Path
-from tempfile import mkdtemp
+from typing import Any
 from warnings import warn
 
 import attr
@@ -33,89 +35,106 @@
 import numpy as np
 from nitransforms.linear import Affine
 
-
-def _data_repr(value):
-    if value is None:
-        return "None"
-    return f"<{'x'.join(str(v) for v in value.shape)} ({value.dtype})>"
-
-
-def _cmp(lh, rh):
-    if isinstance(lh, np.ndarray) and isinstance(rh, np.ndarray):
-        return np.allclose(lh, rh)
-
-    return lh == rh
+from nifreeze.data.base import BaseDataset, _cmp, _data_repr
 
 
 @attr.s(slots=True)
-class DWI:
+class DWI(BaseDataset):
     """Data representation structure for dMRI data."""
 
-    dataobj = attr.ib(default=None, repr=_data_repr, eq=attr.cmp_using(eq=_cmp))
-    """A numpy ndarray object for the data array, without *b=0* volumes."""
-    affine = attr.ib(default=None, repr=_data_repr, eq=attr.cmp_using(eq=_cmp))
-    """Best affine for RAS-to-voxel conversion of coordinates (NIfTI header)."""
-    brainmask = attr.ib(default=None, repr=_data_repr, eq=attr.cmp_using(eq=_cmp))
-    """A boolean ndarray object containing a corresponding brainmask."""
     bzero = attr.ib(default=None, repr=_data_repr, eq=attr.cmp_using(eq=_cmp))
-    """
-    A *b=0* reference map, preferably obtained by some smart averaging.
-    If the :math:`B_0` fieldmap is set, this *b=0* reference map should also
-    be unwarped.
-    """
+    """A *b=0* reference map, preferably obtained by some smart averaging."""
     gradients = attr.ib(default=None, repr=_data_repr, eq=attr.cmp_using(eq=_cmp))
-    """A 2D numpy array of the gradient table in RAS+B format."""
-    em_affines = attr.ib(default=None, eq=attr.cmp_using(eq=_cmp))
-    """
-    List of :obj:`nitransforms.linear.Affine` objects that bring
-    DWIs (i.e., no b=0) into alignment.
-    """
-    fieldmap = attr.ib(default=None, repr=_data_repr, eq=attr.cmp_using(eq=_cmp))
-    """A 3D displacements field to unwarp susceptibility distortions."""
-    _filepath = attr.ib(
-        factory=lambda: Path(mkdtemp()) / "em_cache.h5",
-        repr=False,
-        eq=False,
-    )
-    """A path to an HDF5 file to store the whole dataset."""
+    """A 2D numpy array of the gradient table (4xN)."""
+    eddy_xfms = attr.ib(default=None)
+    """List of transforms to correct for estimatted eddy current distortions."""
+
+    def __getitem__(
+        self, idx: int | slice | tuple | np.ndarray
+    ) -> tuple[np.ndarray, np.ndarray | None, np.ndarray | None]:
+        """
+        Returns volume(s) and corresponding affine(s) and gradient(s) through fancy indexing.
+
+        Parameters
+        ----------
+        idx : :obj:`int` or :obj:`slice` or :obj:`tuple` or :obj:`~numpy.ndarray`
+            Indexer for the last dimension (or possibly other dimensions if extended).
+
+        Returns
+        -------
+        volumes : :obj:`~numpy.ndarray`
+            The selected data subset.
+            If ``idx`` is a single integer, this will have shape ``(X, Y, Z)``,
+            otherwise it may have shape ``(X, Y, Z, k)``.
+        motion_affine : :obj:`~numpy.ndarray` or ``None``
+            The corresponding per-volume motion affine(s) or ``None`` if identity transform(s).
+        gradient : :obj:`~numpy.ndarray`
+            The corresponding gradient(s), which may have shape ``(4,)`` if a single volume
+            or ``(4, k)`` if multiple volumes, or ``None`` if gradients are not available.
+
+        """
+
+        data, affine = super().__getitem__(idx)
+        return data, affine, self.gradients[..., idx]
 
-    def get_filename(self):
-        """Get the filepath of the HDF5 file."""
-        return self._filepath
+    @classmethod
+    def from_filename(cls, filename: Path | str) -> DWI:
+        """
+        Read an HDF5 file from disk and create a DWI object.
+
+        Parameters
+        ----------
+        filename : :obj:`os.pathlike`
+            The HDF5 file path to read.
+
+        Returns
+        -------
+        :obj:`~nifreeze.data.dmri.DWI`
+            The constructed dataset with data loaded from the file.
+
+        """
+        # Reuse the parent `from_filename` logic to load all attributes
+        # that do not start with '_'. Then simply return DWI(**loaded_data).
+        from attr import fields
+
+        data: dict[str, Any] = {}
+        with h5py.File(filename, "r") as in_file:
+            root = in_file["/0"]
+            for f in fields(cls):
+                if f.name.startswith("_"):
+                    continue
+                if f.name in root:
+                    data[f.name] = np.asanyarray(root[f.name])
+                else:
+                    data[f.name] = None
 
-    def __len__(self):
-        """Obtain the number of high-*b* orientations."""
-        return self.dataobj.shape[-1]
+        return cls(**data)
 
-    def set_transform(self, index, affine, order=3):
-        """Set an affine, and update data object and gradients."""
-        reference = namedtuple("ImageGrid", ("shape", "affine"))(
-            shape=self.dataobj.shape[:3], affine=self.affine
-        )
+    def set_transform(self, index: int, affine: np.ndarray, order: int = 3) -> None:
+        """
+        Set an affine transform for a particular index and update the data object.
 
-        # create a nitransforms object
-        if self.fieldmap:
-            # compose fieldmap into transform
-            raise NotImplementedError
-        else:
-            xform = Affine(matrix=affine, reference=reference)
+        The new affine is set as in :obj:`~nifreeze.data.base.BaseDataset.set_transform`,
+        and, in addition, the corresponding gradient vector is rotated.
 
+        Parameters
+        ----------
+        index : :obj:`int`
+            The volume index to transform.
+        affine : :obj:`numpy.ndarray`
+            The 4x4 affine matrix to be applied.
+        order : :obj:`int`, optional
+            The order of the spline interpolation.
+
+        """
         if not Path(self._filepath).exists():
             self.to_filename(self._filepath)
 
-        # read original DWI data & b-vector
-        with h5py.File(self._filepath, "r") as in_file:
-            root = in_file["/0"]
-            dwframe = np.asanyarray(root["dataobj"][..., index])
-            bvec = np.asanyarray(root["gradients"][:3, index])
+        ImageGrid = namedtuple("ImageGrid", ("shape", "affine"))
+        reference = ImageGrid(shape=self.dataobj.shape[:3], affine=self.affine)
 
-        dwmoving = nb.Nifti1Image(dwframe, self.affine, None)
-
-        # resample and update orientation at index
-        self.dataobj[..., index] = np.asanyarray(
-            xform.apply(dwmoving, order=order).dataobj,
-            dtype=self.dataobj.dtype,
-        )
+        xform = Affine(matrix=affine, reference=reference)
+        bvec = self.gradients[:3, index]
 
         # invert transform transform b-vector and origin
         r_bvec = (~xform).map([bvec, (0.0, 0.0, 0.0)])
@@ -124,129 +143,197 @@ def set_transform(self, index, affine, order=3):
         # Normalize and update
         self.gradients[:3, index] = new_bvec / np.linalg.norm(new_bvec)
 
-        # update transform
-        if self.em_affines is None:
-            self.em_affines = np.zeros((self.dataobj.shape[-1], 4, 4))
+        super().set_transform(index, affine, order)
+
+    def to_filename(
+        self,
+        filename: Path | str,
+        compression: str | None = None,
+        compression_opts: Any = None,
+    ) -> None:
+        """
+        Write the dMRI dataset to an HDF5 file on disk.
+
+        Parameters
+        ----------
+        filename : :obj:`os.pathlike`
+            The HDF5 file path to write to.
+        compression : :obj:`str`, optional
+            Compression strategy.
+            See :obj:`~h5py.Group.create_dataset` documentation.
+        compression_opts : :obj:`typing.Any`, optional
+            Parameters for compression
+            `filters <https://docs.h5py.org/en/stable/high/dataset.html#dataset-compression>`__.
+
+        """
+        super().to_filename(filename, compression=compression, compression_opts=compression_opts)
+        # Overriding if you'd like to set a custom attribute, for example:
+        with h5py.File(filename, "r+") as out_file:
+            out_file.attrs["Type"] = "dmri"
+
+    def to_nifti(self, filename: Path | str, insert_b0: bool = False) -> None:
+        """
+        Write a NIfTI file to disk.
+
+        Parameters
+        ----------
+        filename : :obj:`os.pathlike`
+            The output NIfTI file path.
+        insert_b0 : :obj:`bool`
+            Insert a :math:`b=0` at the front of the output NIfTI.
+
+        """
+        if not insert_b0:
+            # Parent's to_nifti to handle the primary NIfTI export.
+            super().to_nifti(filename)
+        else:
+            data = np.concatenate((self.bzero[..., np.newaxis], self.dataobj), axis=-1)
+            nii = nb.Nifti1Image(data, self.affine, self.datahdr)
+            if self.datahdr is None:
+                nii.header.set_xyzt_units("mm")
+            nii.to_filename(filename)
 
-        self.em_affines[index] = xform.matrix
+        # Convert filename to a Path object.
+        out_root = Path(filename).absolute()
 
-    def to_filename(self, filename, compression=None, compression_opts=None):
-        """Write an HDF5 file to disk."""
-        filename = Path(filename)
-        if not filename.name.endswith(".h5"):
-            filename = filename.parent / f"{filename.name}.h5"
+        # Remove .gz if present, then remove .nii if present.
+        # This yields the base stem for writing .bvec / .bval.
+        if out_root.suffix == ".gz":
+            out_root = out_root.with_suffix("")  # remove '.gz'
+        if out_root.suffix == ".nii":
+            out_root = out_root.with_suffix("")  # remove '.nii'
 
-        with h5py.File(filename, "w") as out_file:
-            out_file.attrs["Format"] = "EMC/DWI"
-            out_file.attrs["Version"] = np.uint16(1)
-            root = out_file.create_group("/0")
-            root.attrs["Type"] = "dwi"
-            for f in attr.fields(self.__class__):
-                if f.name.startswith("_"):
-                    continue
+        # Construct sidecar file paths.
+        bvecs_file = out_root.with_suffix(".bvec")
+        bvals_file = out_root.with_suffix(".bval")
 
-                value = getattr(self, f.name)
-                if value is not None:
-                    root.create_dataset(
-                        f.name,
-                        data=value,
-                        compression=compression,
-                        compression_opts=compression_opts,
-                    )
-
-    def to_nifti(self, filename, **kwargs):
-        """Write a NIfTI 1.0 file to disk."""
-        insert_b0 = kwargs.get("insert_b0", False)
-        data = (
-            self.dataobj
-            if not insert_b0
-            else np.concatenate((self.bzero[..., np.newaxis], self.dataobj), axis=-1)
-        )
-        nii = nb.Nifti1Image(data, self.affine, None)
-        nii.header.set_xyzt_units("mm")
-        nii.to_filename(filename)
-
-    def plot_mosaic(self, index=None, **kwargs):
-        """Visualize one direction of the dMRI dataset."""
-        from nireports.reportlets.modality.dwi import plot_dwi
-
-        return plot_dwi(
-            self.bzero if index is None else self.dataobj[..., index],
-            self.affine,
-            gradient=self.gradients[..., index] if index is not None else None,
-            **kwargs,
-        )
+        # Save bvecs and bvals to text files
+        # Each row of bvecs is one direction (3 rows, N columns).
+        np.savetxt(bvecs_file, self.gradients[:3, ...].T, fmt="%.6f")
+        np.savetxt(bvals_file, self.gradients[:3, ...], fmt="%.6f")
 
-    def plot_gradients(self, **kwargs):
-        """Visualize diffusion gradient."""
-        from nireports.reportlets.modality.dwi import plot_gradients as rpt_plot_gradients
 
-        return rpt_plot_gradients(self.gradients.T, **kwargs)
+def load(
+    filename: Path | str,
+    brainmask_file: Path | str | None = None,
+    motion_file: Path | str | None = None,
+    gradients_file: Path | str | None = None,
+    bvec_file: Path | str | None = None,
+    bval_file: Path | str | None = None,
+    b0_file: Path | str | None = None,
+    b0_thres: float = 50.0,
+) -> DWI:
+    """
+    Load DWI data and construct a DWI object.
+
+    This function can load data from either an HDF5 file (if the filename ends with ``.h5``)
+    or from a NIfTI file, optionally loading a gradient table from either a separate gradients
+    file or from .bvec / .bval files.
+
+    Parameters
+    ----------
+    filename : :obj:`os.pathlike`
+        The main DWI data file (NIfTI or HDF5).
+    brainmask_file : :obj:`os.pathlike`, optional
+        A brainmask NIfTI file. If provided, will be loaded and
+        stored in the returned dataset.
+    motion_file : :obj:`os.pathlike`
+        A file containing head-motion affine matrices (linear)
+    gradients_file : :obj:`os.pathlike`, optional
+        A text file containing the gradients table, shape (4, N) or (N, 4).
+        If provided, it supersedes any .bvec / .bval combination.
+    bvec_file : :obj:`os.pathlike`, optional
+        A text file containing b-vectors, shape (3, N).
+    bval_file : :obj:`os.pathlike`, optional
+        A text file containing b-values, shape (N,).
+    b0_file : :obj:`os.pathlike`, optional
+        A NIfTI file containing a b=0 volume (possibly averaged or reference).
+        If not provided, and the data contains at least one b=0 volume, one will be computed.
+    b0_thres : float, optional
+        Threshold for determining which volumes are considered DWI vs. b=0
+        if you combine them in the same file.
+
+    Returns
+    -------
+    dwi : :obj:`~nifreeze.data.dmri.DWI`
+        A DWI object containing the loaded data, gradient table, and optional
+        b-zero volume, and brainmask.
+
+    Raises
+    ------
+    RuntimeError
+        If no gradient information is provided (neither ``gradients_file`` nor
+        ``bvec_file`` + ``bval_file``).
 
-    @classmethod
-    def from_filename(cls, filename):
-        """Read an HDF5 file from disk."""
-        with h5py.File(filename, "r") as in_file:
-            root = in_file["/0"]
-            data = {k: np.asanyarray(v) for k, v in root.items() if not k.startswith("_")}
-        return cls(**data)
+    """
 
+    if motion_file:
+        raise NotImplementedError
 
-def load(
-    filename,
-    gradients_file=None,
-    b0_file=None,
-    brainmask_file=None,
-    fmap_file=None,
-    bvec_file=None,
-    bval_file=None,
-    b0_thres=50,
-):
-    """Load DWI data."""
     filename = Path(filename)
-    if filename.name.endswith(".h5"):
+    # 1) If this is an HDF5 file, just load via the DWI.from_filename method
+    if filename.suffix == ".h5":
         return DWI.from_filename(filename)
 
-    if gradients_file:
-        grad = np.loadtxt(gradients_file, dtype="float32").T
+    # 2) Otherwise, load a NIfTI
+    img = nb.load(str(filename))
+    fulldata = img.get_fdata(dtype=np.float32)
+    affine = img.affine
 
+    # 3) Determine the gradients array from either gradients_file or bvec/bval
+    if gradients_file:
+        grad = np.loadtxt(gradients_file, dtype="float32")
         if bvec_file and bval_file:
             warn(
-                "Gradients table file and b-vec/val files are defined; "
-                "dismissing b-vec/val files.",
+                "Both a gradients table file and b-vec/val files are defined; "
+                "ignoring b-vec/val files in favor of the gradients_file.",
                 stacklevel=2,
             )
     elif bvec_file and bval_file:
-        grad = np.vstack(
-            (
-                np.loadtxt(bvec_file, dtype="float32"),
-                np.loadtxt(bval_file, dtype="float32"),
-            )
-        )
+        bvecs = np.loadtxt(bvec_file, dtype="float32")  # shape (3, N)
+        if bvecs.shape[0] != 3 and bvecs.shape[1] == 3:
+            bvecs = bvecs.T
+
+        bvals = np.loadtxt(bval_file, dtype="float32")  # shape (N,)
+        # Stack to shape (4, N)
+        grad = np.vstack((bvecs, bvals))
     else:
-        raise RuntimeError("A gradients file is necessary")
+        raise RuntimeError(
+            "No gradient data provided. "
+            "Please specify either a gradients_file or (bvec_file & bval_file)."
+        )
+
+    # 4) Create the DWI instance. We'll filter out volumes where b-value > b0_thres
+    #    as "DW volumes" if the user wants to store only the high-b volumes here
+    gradmsk = grad[-1] > b0_thres if grad.shape[0] == 4 else grad[:, -1] > b0_thres
 
-    img = nb.load(filename)
-    fulldata = img.get_fdata(dtype="float32")
-    retval = DWI(
-        affine=img.affine,
+    # The shape checking is somewhat flexible: (4, N) or (N, 4)
+    dwi_obj = DWI(
+        dataobj=fulldata[..., gradmsk],
+        affine=affine,
+        # We'll assign the filtered gradients below.
     )
-    gradmsk = grad[-1] > b0_thres
-    retval.gradients = grad[..., gradmsk]
-    retval.dataobj = fulldata[..., gradmsk]
 
-    if b0_file:
-        b0img = nb.load(b0_file)
-        retval.bzero = np.asanyarray(b0img.dataobj)
-    elif not np.all(gradmsk):
-        retval.bzero = np.median(fulldata[..., ~gradmsk], axis=3)
+    dwi_obj.gradients = grad[:, gradmsk] if grad.shape[0] == 4 else grad[gradmsk, :].T
 
+    # 6) b=0 volume (bzero)
+    #    If the user provided a b0_file, load it
+    if b0_file:
+        b0img = nb.load(str(b0_file))
+        b0vol = np.asanyarray(b0img.dataobj)
+        # We'll assume your DWI class has a bzero: np.ndarray | None attribute
+        dwi_obj.bzero = b0vol
+    # Otherwise, if any volumes remain outside gradmsk, compute a median B0:
+    elif np.any(~gradmsk):
+        # The b=0 volumes are those that did NOT pass b0_thres
+        b0_volumes = fulldata[..., ~gradmsk]
+        # A simple approach is to take the median across that last dimension
+        # Note that axis=3 is valid only if your data is 4D (x, y, z, volumes).
+        dwi_obj.bzero = np.median(b0_volumes, axis=3)
+
+    # 7) If a brainmask_file was provided, load it
     if brainmask_file:
-        mask = nb.load(brainmask_file)
-        retval.brainmask = np.asanyarray(mask.dataobj)
-
-    if fmap_file:
-        fmapimg = nb.load(fmap_file)
-        retval.fieldmap = fmapimg.get_fdata(dtype="float32")
+        mask_img = nb.load(str(brainmask_file))
+        dwi_obj.brainmask = np.asanyarray(mask_img.dataobj, dtype=bool)
 
-    return retval
+    return dwi_obj
diff --git a/src/nifreeze/data/pet.py b/src/nifreeze/data/pet.py
index 89e1c8c04..dd0e45437 100644
--- a/src/nifreeze/data/pet.py
+++ b/src/nifreeze/data/pet.py
@@ -22,157 +22,203 @@
 #
 """PET data representation."""
 
-from collections import namedtuple
+from __future__ import annotations
+
 from pathlib import Path
-from tempfile import mkdtemp
+from typing import Any, Union
 
 import attr
 import h5py
 import nibabel as nb
 import numpy as np
-from nitransforms.linear import Affine
-
 
-def _data_repr(value):
-    if value is None:
-        return "None"
-    return f"<{'x'.join(str(v) for v in value.shape)} ({value.dtype})>"
+from nifreeze.data.base import BaseDataset, _cmp, _data_repr
 
 
 @attr.s(slots=True)
-class PET:
+class PET(BaseDataset):
     """Data representation structure for PET data."""
 
-    dataobj = attr.ib(default=None, repr=_data_repr)
-    """A numpy ndarray object for the data array, without *b=0* volumes."""
-    affine = attr.ib(default=None, repr=_data_repr)
-    """Best affine for RAS-to-voxel conversion of coordinates (NIfTI header)."""
-    brainmask = attr.ib(default=None, repr=_data_repr)
-    """A boolean ndarray object containing a corresponding brainmask."""
-    frame_time = attr.ib(default=None, repr=_data_repr)
-    """A 1D numpy array with the midpoint timing of each sample."""
-    total_duration = attr.ib(default=None, repr=_data_repr)
-    """A float number representing the total duration of acquisition."""
-
-    em_affines = attr.ib(default=None)
-    """
-    List of :obj:`nitransforms.linear.Affine` objects that bring
-    PET timepoints into alignment.
-    """
-    _filepath = attr.ib(
-        factory=lambda: Path(mkdtemp()) / "em_cache.h5",
-        repr=False,
+    frame_time: np.ndarray | None = attr.ib(
+        default=None, repr=_data_repr, eq=attr.cmp_using(eq=_cmp)
     )
-    """A path to an HDF5 file to store the whole dataset."""
-
-    def __len__(self):
-        """Obtain the number of high-*b* orientations."""
-        return self.dataobj.shape[-1]
-
-    def set_transform(self, index, affine, order=3):
-        """Set an affine, and update data object and gradients."""
-        reference = namedtuple("ImageGrid", ("shape", "affine"))(
-            shape=self.dataobj.shape[:3], affine=self.affine
-        )
-        xform = Affine(matrix=affine, reference=reference)
-
-        if not Path(self._filepath).exists():
-            self.to_filename(self._filepath)
+    """A (N,) numpy array specifying the midpoint timing of each sample or frame."""
+    total_duration: float | None = attr.ib(default=None, repr=True)
+    """A float representing the total duration of the dataset."""
+
+    def __getitem__(
+        self, idx: int | slice | tuple | np.ndarray
+    ) -> tuple[np.ndarray, np.ndarray | None, np.ndarray | None]:
+        """
+        Returns volume(s) and corresponding affine(s) and timing(s) through fancy indexing.
+
+        Parameters
+        ----------
+        idx : :obj:`int` or :obj:`slice` or :obj:`tuple` or :obj:`~numpy.ndarray`
+            Indexer for the last dimension (or possibly other dimensions if extended).
+
+        Returns
+        -------
+        volumes : :obj:`~numpy.ndarray`
+            The selected data subset.
+            If ``idx`` is a single integer, this will have shape ``(X, Y, Z)``,
+            otherwise it may have shape ``(X, Y, Z, k)``.
+        motion_affine : :obj:`~numpy.ndarray` or ``None``
+            The corresponding per-volume motion affine(s) or ``None`` if identity transform(s).
+        time : :obj:`float` or ``None``
+            The frame time corresponding to the index(es).
+
+        """
+
+        data, affine = super().__getitem__(idx)
+        return data, affine, self.frame_time[idx]
 
-        # read original PET
-        with h5py.File(self._filepath, "r") as in_file:
-            root = in_file["/0"]
-            dframe = np.asanyarray(root["dataobj"][..., index])
-
-        dmoving = nb.Nifti1Image(dframe, self.affine, None)
+    @classmethod
+    def from_filename(cls, filename: Union[str, Path]) -> PET:
+        """
+        Read an HDF5 file from disk and create a PET object.
 
-        # resample and update orientation at index
-        self.dataobj[..., index] = np.asanyarray(
-            xform.apply(dmoving, order=order).dataobj,
-            dtype=self.dataobj.dtype,
-        )
+        Parameters
+        ----------
+        filename : :obj:`os.pathlike`
+            The HDF5 file path to read.
 
-        # update transform
-        if self.em_affines is None:
-            self.em_affines = [None] * len(self)
+        Returns
+        -------
+        :obj:`~nifreeze.data.pet.PET`
+            A PET dataset with data loaded from the specified file.
 
-        self.em_affines[index] = xform
+        """
+        import attr
 
-    def to_filename(self, filename, compression=None, compression_opts=None):
-        """Write an HDF5 file to disk."""
         filename = Path(filename)
-        if not filename.name.endswith(".h5"):
-            filename = filename.parent / f"{filename.name}.h5"
-
-        with h5py.File(filename, "w") as out_file:
-            out_file.attrs["Format"] = "EMC/PET"
-            out_file.attrs["Version"] = np.uint16(1)
-            root = out_file.create_group("/0")
-            root.attrs["Type"] = "pet"
-            for f in attr.fields(self.__class__):
-                if f.name.startswith("_"):
-                    continue
+        data: dict[str, Any] = {}
 
-                value = getattr(self, f.name)
-                if value is not None:
-                    root.create_dataset(
-                        f.name,
-                        data=value,
-                        compression=compression,
-                        compression_opts=compression_opts,
-                    )
-
-    def to_nifti(self, filename, *_):
-        """Write a NIfTI 1.0 file to disk."""
-        nii = nb.Nifti1Image(self.dataobj, self.affine, None)
-        nii.header.set_xyzt_units("mm")
-        nii.to_filename(filename)
-
-    @classmethod
-    def from_filename(cls, filename):
-        """Read an HDF5 file from disk."""
         with h5py.File(filename, "r") as in_file:
             root = in_file["/0"]
-            data = {k: np.asanyarray(v) for k, v in root.items() if not k.startswith("_")}
+            for f in attr.fields(cls):
+                # skip private attributes (start with '_')
+                if f.name.startswith("_"):
+                    continue
+                if f.name in root:
+                    data[f.name] = np.asanyarray(root[f.name])
+                else:
+                    data[f.name] = None
+
         return cls(**data)
 
+    def to_filename(
+        self,
+        filename: Path | str,
+        compression: str | None = None,
+        compression_opts: Any = None,
+    ) -> None:
+        """
+        Write the PET dataset to an HDF5 file on disk.
+
+        Parameters
+        ----------
+        filename : :obj:`os.pathlike`
+            The HDF5 file path to write to.
+        compression : :obj:`str`, optional
+            Compression strategy.
+            See :obj:`~h5py.Group.create_dataset` documentation.
+        compression_opts : :obj:`typing.Any`, optional
+            Parameters for compression
+            `filters <https://docs.h5py.org/en/stable/high/dataset.html#dataset-compression>`__.
+
+        """
+        super().to_filename(filename, compression=compression, compression_opts=compression_opts)
+        # Overriding if you'd like to set a custom attribute, for example:
+        with h5py.File(filename, "r+") as out_file:
+            out_file.attrs["Type"] = "pet"
+
 
 def load(
-    filename,
-    brainmask_file=None,
-    frame_time=None,
-    frame_duration=None,
-):
-    """Load PET data."""
-    filename = Path(filename)
-    if filename.name.endswith(".h5"):
-        return PET.from_filename(filename)
+    filename: Path | str,
+    brainmask_file: Path | str | None = None,
+    motion_file: Path | str | None = None,
+    frame_time: np.ndarray | list[float] | None = None,
+    frame_duration: np.ndarray | list[float] | None = None,
+) -> PET:
+    """
+    Load PET data from HDF5 or NIfTI, creating a PET object with appropriate metadata.
+
+    Parameters
+    ----------
+    filename : :obj:`os.pathlike`
+        The NIfTI or HDF5 file.
+    brainmask_file : :obj:`os.pathlike`, optional
+        A brainmask NIfTI file. If provided, will be loaded and
+        stored in the returned dataset.
+    motion_file : :obj:`os.pathlike`
+        A file containing head-motion affine matrices (linear).
+    frame_time : :obj:`numpy.ndarray` or :obj:`list` of :obj:`float`, optional
+        The start times of each frame relative to the beginning of the acquisition.
+        If ``None``, an error is raised (since BIDS requires ``FrameTimesStart``).
+    frame_duration : :obj:`numpy.ndarray` or :obj:`list` of :obj:`float`, optional
+        The duration of each frame.
+        If ``None``, it is derived by the difference of consecutive frame times,
+        defaulting the last frame to match the second-last.
+
+    Returns
+    -------
+    :obj:`~nifreeze.data.pet.PET`
+        A PET object storing the data, metadata, and any optional mask.
+
+    Raises
+    ------
+    RuntimeError
+        If ``frame_time`` is not provided (BIDS requires it).
 
-    img = nb.load(filename)
-    retval = PET(
-        dataobj=img.get_fdata(dtype="float32"),
-        affine=img.affine,
-    )
+    """
+    if motion_file:
+        raise NotImplementedError
 
-    if frame_time is None:
-        raise RuntimeError(
-            "Start time of frames is mandatory (see https://bids-specification.readthedocs.io/"
-            "en/stable/glossary.html#objects.metadata.FrameTimesStart)"
+    filename = Path(filename)
+    if filename.suffix == ".h5":
+        # Load from HDF5
+        pet_obj = PET.from_filename(filename)
+    else:
+        # Load from NIfTI
+        img = nb.load(str(filename))
+        data = img.get_fdata(dtype=np.float32)
+        pet_obj = PET(
+            dataobj=data,
+            affine=img.affine,
         )
 
-    frame_time = np.array(frame_time, dtype="float32") - frame_time[0]
-    if frame_duration is None:
-        frame_duration = np.diff(frame_time)
-        if len(frame_duration) == (retval.dataobj.shape[-1] - 1):
-            frame_duration = np.append(frame_duration, frame_duration[-1])
-
-    retval.total_duration = frame_time[-1] + frame_duration[-1]
-    retval.frame_time = frame_time + 0.5 * np.array(frame_duration, dtype="float32")
-
-    assert len(retval.frame_time) == retval.dataobj.shape[-1]
+    # Verify the user provided frame_time if not already in the PET object
+    if pet_obj.frame_time is None and frame_time is None:
+        raise RuntimeError(
+            "The `frame_time` is mandatory for PET data to comply with BIDS. "
+            "See https://bids-specification.readthedocs.io for details."
+        )
 
-    if brainmask_file:
-        mask = nb.load(brainmask_file)
-        retval.brainmask = np.asanyarray(mask.dataobj)
+    # If the user supplied new values, set them
+    if frame_time is not None:
+        # Convert to a float32 numpy array and zero out the earliest time
+        frame_time_arr = np.array(frame_time, dtype=np.float32)
+        frame_time_arr -= frame_time_arr[0]
+        pet_obj.frame_time = frame_time_arr
 
-    return retval
+    # If the user doesn't provide frame_duration, we derive it:
+    if frame_duration is None:
+        frame_time_arr = pet_obj.frame_time
+        # If shape is e.g. (N,), then we can do
+        durations = np.diff(frame_time_arr)
+        if len(durations) == (len(frame_time_arr) - 1):
+            durations = np.append(durations, durations[-1])  # last frame same as second-last
+    else:
+        durations = np.array(frame_duration, dtype=np.float32)
+
+    # Set total_duration and shift frame_time to the midpoint
+    pet_obj.total_duration = float(frame_time_arr[-1] + durations[-1])
+    pet_obj.frame_time = frame_time_arr + 0.5 * durations
+
+    # If a brain mask is provided, load and attach
+    if brainmask_file is not None:
+        mask_img = nb.load(str(brainmask_file))
+        pet_obj.brainmask = np.asanyarray(mask_img.dataobj, dtype=bool)
+
+    return pet_obj
diff --git a/src/nifreeze/estimator.py b/src/nifreeze/estimator.py
index 33ae799c1..b9acbf12f 100644
--- a/src/nifreeze/estimator.py
+++ b/src/nifreeze/estimator.py
@@ -56,7 +56,7 @@ def estimate(
         data : :obj:`~nifreeze.dmri.DWI`
             The target DWI dataset, represented by this tool's internal
             type. The object is used in-place, and will contain the estimated
-            parameters in its ``em_affines`` property, as well as the rotated
+            parameters in its ``motion_affines`` property, as well as the rotated
             *b*-vectors within its ``gradients`` property.
         n_iter : :obj:`int`
             Number of iterations this particular model is going to be repeated.
@@ -177,11 +177,10 @@ def estimate(
                             fixed,
                             moving,
                             bmask_img,
-                            data.em_affines,
+                            data.motion_affines,
                             data.affine,
                             data.dataobj.shape[:3],
                             data_test[1][3],
-                            data.fieldmap,
                             i_iter,
                             i,
                             ptmp_dir,
@@ -193,7 +192,7 @@ def estimate(
                         data.set_transform(i, xform.matrix)
                         pbar.update()
 
-        return data.em_affines
+        return data.motion_affines
 
 
 def _prepare_brainmask_data(brainmask, affine):
diff --git a/src/nifreeze/model/base.py b/src/nifreeze/model/base.py
index 65f90bd10..748285fef 100644
--- a/src/nifreeze/model/base.py
+++ b/src/nifreeze/model/base.py
@@ -30,7 +30,7 @@
 
 
 class ModelFactory:
-    """A factory for instantiating diffusion models."""
+    """A factory for instantiating data models."""
 
     @staticmethod
     def init(model="DTI", **kwargs):
diff --git a/src/nifreeze/registration/ants.py b/src/nifreeze/registration/ants.py
index 2b58f7a44..f6bba237d 100644
--- a/src/nifreeze/registration/ants.py
+++ b/src/nifreeze/registration/ants.py
@@ -409,7 +409,6 @@ def _run_registration(
     affine: np.ndarray,
     shape: tuple[int, int, int],
     bval: int,
-    fieldmap: nb.spatialimages.SpatialImage,
     i_iter: int,
     vol_idx: int,
     dirname: Path,
@@ -435,8 +434,6 @@ def _run_registration(
         Shape of the DWI frame.
     bval : :obj:`int`
         b-value of the corresponding DWI volume.
-    fieldmap : :class:`~nibabel.spatialimages.SpatialImage`
-        Fieldmap.
     i_iter : :obj:`int`
         Iteration number.
     vol_idx : :obj:`int`
@@ -472,14 +469,9 @@ def _run_registration(
         registration.inputs.fixed_image_masks = ["NULL", bmask_img]
 
     if em_affines is not None and np.any(em_affines[vol_idx, ...]):
-        reference = namedtuple("ImageGrid", ("shape", "affine"))(shape=shape, affine=affine)
-
-        # create a nitransforms object
-        if fieldmap:
-            # compose fieldmap into transform
-            raise NotImplementedError
-        else:
-            initial_xform = Affine(matrix=em_affines[vol_idx], reference=reference)
+        ImageGrid = namedtuple("ImageGrid", ("shape", "affine"))
+        reference = ImageGrid(shape=shape, affine=affine)
+        initial_xform = Affine(matrix=em_affines[vol_idx], reference=reference)
         mat_file = dirname / f"init_{i_iter}_{vol_idx:05d}.mat"
         initial_xform.to_filename(mat_file, fmt="itk")
         registration.inputs.initial_moving_transform = str(mat_file)
diff --git a/test/test_data_base.py b/test/test_data_base.py
new file mode 100644
index 000000000..7f2c79562
--- /dev/null
+++ b/test/test_data_base.py
@@ -0,0 +1,149 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+#
+# Copyright The NiPreps Developers <nipreps@gmail.com>
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# We support and encourage derived works from this project, please read
+# about our expectations at
+#
+#     https://www.nipreps.org/community/licensing/
+#
+"""Test dataset base class."""
+
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+import nibabel as nb
+import numpy as np
+import pytest
+
+from nifreeze.data.base import NFDH5_EXT, BaseDataset, load
+
+
+@pytest.fixture
+def random_dataset() -> BaseDataset:
+    """Create a BaseDataset with random data for testing."""
+    data = np.random.rand(32, 32, 32, 5).astype(np.float32)
+    affine = np.eye(4, dtype=np.float32)
+    return BaseDataset(dataobj=data, affine=affine)
+
+
+def test_base_dataset_init(random_dataset: BaseDataset):
+    """Test that the BaseDataset can be initialized with random data."""
+    assert random_dataset.dataobj is not None
+    assert random_dataset.affine is not None
+    assert random_dataset.dataobj.shape == (32, 32, 32, 5)
+    assert random_dataset.affine.shape == (4, 4)
+
+
+def test_len(random_dataset: BaseDataset):
+    """Test that len(BaseDataset) returns the number of volumes."""
+    assert len(random_dataset) == 5  # last dimension is 5 volumes
+
+
+def test_getitem_volume_index(random_dataset: BaseDataset):
+    """
+    Test that __getitem__ returns the correct (volume, affine) tuple.
+
+    By default, motion_affines is None, so we expect to get None for the affine.
+    """
+    # Single volume
+    volume0, aff0 = random_dataset[0]
+    assert volume0.shape == (32, 32, 32)
+    # No transforms have been applied yet, so there's no motion_affines array
+    assert aff0 is None
+
+    # Slice of volumes
+    volume_slice, aff_slice = random_dataset[2:4]
+    assert volume_slice.shape == (32, 32, 32, 2)
+    assert aff_slice is None
+
+
+def test_set_transform(random_dataset: BaseDataset):
+    """
+    Test that calling set_transform changes the data and motion_affines.
+    For simplicity, we'll apply an identity transform and check that motion_affines is updated.
+    """
+    idx = 0
+    data_before = np.copy(random_dataset.dataobj[..., idx])
+    # Identity transform
+    affine = np.eye(4)
+    random_dataset.set_transform(idx, affine, order=1)
+
+    # Data shouldn't have changed (since transform is identity).
+    volume0, aff0 = random_dataset[idx]
+    assert np.allclose(data_before, volume0)
+
+    # motion_affines should be created and match the transform matrix.
+    assert random_dataset.motion_affines is not None
+    np.testing.assert_array_equal(random_dataset.motion_affines[idx], affine)
+    # The returned affine from __getitem__ should be the same.
+    np.testing.assert_array_equal(aff0, affine)
+
+
+def test_to_filename_and_from_filename(random_dataset: BaseDataset):
+    """Test writing a dataset to disk and reading it back from file."""
+    with TemporaryDirectory() as tmpdir:
+        h5_file = Path(tmpdir) / f"test_dataset{NFDH5_EXT}"
+        random_dataset.to_filename(h5_file)
+
+        # Check file exists
+        assert h5_file.is_file()
+
+        # Read from filename
+        ds2 = BaseDataset.from_filename(h5_file)
+        assert ds2.dataobj is not None
+        assert ds2.dataobj.shape == (32, 32, 32, 5)
+        assert ds2.affine.shape == (4, 4)
+        # Ensure the data is the same
+        assert np.allclose(random_dataset.dataobj, ds2.dataobj)
+
+
+def test_to_nifti(random_dataset: BaseDataset):
+    """Test writing a dataset to a NIfTI file."""
+    with TemporaryDirectory() as tmpdir:
+        nifti_file = Path(tmpdir) / "test_dataset.nii.gz"
+        random_dataset.to_nifti(nifti_file)
+
+        # Check file exists
+        assert nifti_file.is_file()
+
+        # Load the saved file with nibabel
+        img = nb.load(nifti_file)
+        data = img.get_fdata(dtype=np.float32)
+        assert data.shape == (32, 32, 32, 5)
+        assert np.allclose(data, random_dataset.dataobj)
+
+
+def test_load_hdf5(random_dataset: BaseDataset):
+    """Test the 'load' function with an HDF5 file."""
+    with TemporaryDirectory() as tmpdir:
+        h5_file = Path(tmpdir) / f"test_dataset{NFDH5_EXT}"
+        random_dataset.to_filename(h5_file)
+
+        ds2 = load(h5_file)
+        assert ds2.dataobj.shape == (32, 32, 32, 5)
+        assert np.allclose(random_dataset.dataobj, ds2.dataobj)
+
+
+def test_load_nifti(random_dataset: BaseDataset):
+    """Test the 'load' function with a NIfTI file."""
+    with TemporaryDirectory() as tmpdir:
+        nifti_file = Path(tmpdir) / "test_dataset.nii.gz"
+        random_dataset.to_nifti(nifti_file)
+
+        ds2 = load(nifti_file)
+        assert ds2.dataobj.shape == (32, 32, 32, 5)
+        assert np.allclose(random_dataset.dataobj, ds2.dataobj)
diff --git a/test/test_dmri.py b/test/test_data_dmri.py
similarity index 97%
rename from test/test_dmri.py
rename to test/test_data_dmri.py
index 74c5cadc4..565b6d9a5 100644
--- a/test/test_dmri.py
+++ b/test/test_data_dmri.py
@@ -49,7 +49,6 @@ def _create_dwi_random_dataobj():
     brainmask_dataobj = rng.random(vol_size, dtype="float32")
     b0_dataobj = rng.random(vol_size, dtype="float32")
     gradients = np.vstack([bvecs, bvals[np.newaxis, :]], dtype="float32")
-    fieldmap_dataobj = rng.random(vol_size, dtype="float32")
 
     return (
         dwi_dataobj,
@@ -57,7 +56,6 @@ def _create_dwi_random_dataobj():
         brainmask_dataobj,
         b0_dataobj,
         gradients,
-        fieldmap_dataobj,
         b0_thres,
     )
 
@@ -67,14 +65,12 @@ def _create_dwi_random_data(
     affine,
     brainmask_dataobj,
     b0_dataobj,
-    fieldmap_dataobj,
 ):
     dwi = nb.Nifti1Image(dwi_dataobj, affine)
     brainmask = nb.Nifti1Image(brainmask_dataobj, affine)
     b0 = nb.Nifti1Image(b0_dataobj, affine)
-    fieldmap = nb.Nifti1Image(fieldmap_dataobj, affine)
 
-    return dwi, brainmask, b0, fieldmap
+    return dwi, brainmask, b0
 
 
 def _serialize_dwi_data(
@@ -82,27 +78,23 @@ def _serialize_dwi_data(
     brainmask,
     b0,
     gradients,
-    fieldmap,
     _tmp_path,
 ):
     dwi_fname = _tmp_path / "dwi.nii.gz"
     brainmask_fname = _tmp_path / "brainmask.nii.gz"
     b0_fname = _tmp_path / "b0.nii.gz"
     gradients_fname = _tmp_path / "gradients.txt"
-    fieldmap_fname = _tmp_path / "fieldmap.nii.gz"
 
     nb.save(dwi, dwi_fname)
     nb.save(brainmask, brainmask_fname)
     nb.save(b0, b0_fname)
     np.savetxt(gradients_fname, gradients.T)
-    nb.save(fieldmap, fieldmap_fname)
 
     return (
         dwi_fname,
         brainmask_fname,
         b0_fname,
         gradients_fname,
-        fieldmap_fname,
     )
 
 
@@ -153,16 +145,14 @@ def test_equality_operator(tmp_path):
         brainmask_dataobj,
         b0_dataobj,
         gradients,
-        fieldmap_dataobj,
         b0_thres,
     ) = _create_dwi_random_dataobj()
 
-    dwi, brainmask, b0, fieldmap = _create_dwi_random_data(
+    dwi, brainmask, b0 = _create_dwi_random_data(
         dwi_dataobj,
         affine,
         brainmask_dataobj,
         b0_dataobj,
-        fieldmap_dataobj,
     )
 
     (
@@ -170,13 +160,11 @@ def test_equality_operator(tmp_path):
         brainmask_fname,
         b0_fname,
         gradients_fname,
-        fieldmap_fname,
     ) = _serialize_dwi_data(
         dwi,
         brainmask,
         b0,
         gradients,
-        fieldmap,
         tmp_path,
     )
 
@@ -185,7 +173,6 @@ def test_equality_operator(tmp_path):
         gradients_file=gradients_fname,
         b0_file=b0_fname,
         brainmask_file=brainmask_fname,
-        fmap_file=fieldmap_fname,
         b0_thres=b0_thres,
     )
     hdf5_filename = tmp_path / "test_dwi.h5"