ENH: Add transform analysis utils

Add transform analysis utils.

Transfer contents from the `NiFreeze` projects so that hey can be reused
across projects requiring transform analysis:
https://github.com/nipreps/nifreeze/blob/d27ba7552bbd9095c3c13b46443d87a4b5504c4c/src/nifreeze/analysis/motion.py
https://github.com/nipreps/nifreeze/blob/d27ba7552bbd9095c3c13b46443d87a4b5504c4c/src/nifreeze/data/utils.py

Add a fixture to be able to reuse a random number generator across
tests.

Author: jhlegarreta

nitransforms/analysis/__init__.py

@@ -0,0 +1,188 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+"""
+Utilities to aid in performing and evaluating image registration.
+
+This module provides functions to compute displacements of image coordinates
+under a transformation, useful for assessing the accuracy of image registration
+processes.
+
+"""
+
+from __future__ import annotations
+
+from itertools import product
+from typing import Tuple
+
+import nibabel as nb
+import numpy as np
+from scipy.stats import zscore
+
+from nitransforms.base import TransformBase
+
+
+RADIUS = 50.0
+"""Typical radius (in mm) of a sphere mimicking the size of a typical human brain."""
+
+
+def compute_fd_from_motion(motion_parameters: np.ndarray, radius: float = RADIUS) -> np.ndarray:
+    """Compute framewise displacement (FD) from motion parameters.
+
+    Each row in the motion parameters represents one frame, and columns
+    represent each coordinate axis ``x``, `y``, and ``z``. Translation
+    parameters are followed by rotation parameters column-wise.
+
+    Parameters
+    ----------
+    motion_parameters : :obj:`numpy.ndarray`
+        Motion parameters.
+    radius : :obj:`float`, optional
+        Radius (in mm) of a sphere mimicking the size of a typical human brain.
+
+    Returns
+    -------
+    :obj:`numpy.ndarray`
+        The framewise displacement (FD) as the sum of absolute differences
+        between consecutive frames.
+    """
+
+    translations = motion_parameters[:, :3]
+    rotations_deg = motion_parameters[:, 3:]
+    rotations_rad = np.deg2rad(rotations_deg)
+
+    # Compute differences between consecutive frames
+    d_translations = np.vstack([np.zeros((1, 3)), np.diff(translations, axis=0)])
+    d_rotations = np.vstack([np.zeros((1, 3)), np.diff(rotations_rad, axis=0)])
+
+    # Convert rotations from radians to displacement on a sphere
+    rotation_displacement = d_rotations * radius
+
+    # Compute FD as sum of absolute differences
+    return np.sum(np.abs(d_translations) + np.abs(rotation_displacement), axis=1)
+
+
+def compute_fd_from_transform(
+    img: nb.spatialimages.SpatialImage,
+    test_xfm: TransformBase,
+    radius: float = RADIUS,
+) -> float:
+    """
+    Compute the framewise displacement (FD) for a given transformation.
+
+    Parameters
+    ----------
+    img : :obj:`~nibabel.spatialimages.SpatialImage`
+        The reference image. Used to extract the center coordinates.
+    test_xfm : :obj:`~nitransforms.base.TransformBase`
+        The transformation to test. Applied to coordinates around the image center.
+    radius : :obj:`float`, optional
+        The radius (in mm) of the spherical neighborhood around the center of the image.
+
+    Returns
+    -------
+    :obj:`float`
+        The average framewise displacement (FD) for the test transformation.
+
+    """
+    affine = img.affine
+    # Compute the center of the image in voxel space
+    center_ijk = 0.5 * (np.array(img.shape[:3]) - 1)
+    # Convert to world coordinates
+    center_xyz = nb.affines.apply_affine(affine, center_ijk)
+    # Generate coordinates of points at radius distance from center
+    fd_coords = np.array(list(product(*((radius, -radius),) * 3))) + center_xyz
+    # Compute the average displacement from the test transformation
+    return np.mean(np.linalg.norm(test_xfm.map(fd_coords) - fd_coords, axis=-1))
+
+
+def displacements_within_mask(
+    mask_img: nb.spatialimages.SpatialImage,
+    test_xfm: TransformBase,
+    reference_xfm: TransformBase | None = None,
+) -> np.ndarray:
+    """
+    Compute the distance between voxel coordinates mapped through two transforms.
+
+    Parameters
+    ----------
+    mask_img : :obj:`~nibabel.spatialimages.SpatialImage`
+        A mask image that defines the region of interest. Voxel coordinates
+        within the mask are transformed.
+    test_xfm : :obj:`~nitransforms.base.TransformBase`
+        The transformation to test. This transformation is applied to the
+        voxel coordinates.
+    reference_xfm : :obj:`~nitransforms.base.TransformBase`, optional
+        A reference transformation to compare with. If ``None``, the identity
+        transformation is assumed (no transformation).
+
+    Returns
+    -------
+    :obj:`~numpy.ndarray`
+        An array of displacements (in mm) for each voxel within the mask.
+
+    """
+    # Mask data as boolean (True for voxels inside the mask)
+    maskdata = np.asanyarray(mask_img.dataobj) > 0
+    # Convert voxel coordinates to world coordinates using affine transform
+    xyz = nb.affines.apply_affine(
+        mask_img.affine,
+        np.argwhere(maskdata),
+    )
+    # Apply the test transformation
+    targets = test_xfm.map(xyz)
+
+    # Compute the difference (displacement) between the test and reference transformations
+    diffs = targets - xyz if reference_xfm is None else targets - reference_xfm.map(xyz)
+    return np.linalg.norm(diffs, axis=-1)
+
+
+def extract_motion_parameters(affine: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+    """Extract translation (mm) and rotation (degrees) parameters from an affine matrix.
+
+    Parameters
+    ----------
+    affine : :obj:`~numpy.ndarray`
+        The affine transformation matrix.
+
+    Returns
+    -------
+    :obj:`tuple`
+        Extracted translation and rotation parameters.
+    """
+
+    translation = affine[:3, 3]
+    rotation_rad = np.arctan2(
+        [affine[2, 1], affine[0, 2], affine[1, 0]], [affine[2, 2], affine[0, 0], affine[1, 1]]
+    )
+    rotation_deg = np.rad2deg(rotation_rad)
+    return *translation, *rotation_deg
+
+
+def identify_spikes(fd: np.ndarray, threshold: float = 2.0):
+    """Identify motion spikes in framewise displacement data.
+
+    Identifies high-motion frames as timepoint exceeding a given threshold value
+    based on z-score normalized framewise displacement (FD) values.
+
+    Parameters
+    ----------
+    fd : :obj:`~numpy.ndarray`
+        Framewise displacement data.
+    threshold : :obj:`float`, optional
+        Threshold value to determine motion spikes.
+
+    Returns
+    -------
+    indices : :obj:`~numpy.ndarray`
+        Indices of identified motion spikes.
+    mask : :obj:`~numpy.ndarray`
+        Mask of identified motion spikes.
+    """
+
+    # Normalize (z-score)
+    fd_norm = zscore(fd)
+
+    mask = fd_norm > threshold
+    indices = np.where(mask)[0]
+
+    return indices, mask

nitransforms/analysis/utils.py

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+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+
+import numpy as np
+import pytest
+
+
+@pytest.fixture(autouse=True)
+def random_number_generator(request):
+    """Automatically set a fixed-seed random number generator for all tests."""
+    request.node.rng = np.random.default_rng(1234)

nitransforms/tests/conftest.py

@@ -0,0 +1,185 @@
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+
+import numpy as np
+import nibabel as nb
+import pytest
+
+import nitransforms as nt
+
+from nitransforms.analysis.utils import (
+    compute_fd_from_motion,
+    compute_fd_from_transform,
+    compute_percentage_change,
+    displacements_within_mask,
+    extract_motion_parameters,
+    identify_spikes,
+)
+
+
+@pytest.fixture
+def identity_affine():
+    return np.eye(4)
+
+
+@pytest.fixture
+def simple_mask_img(identity_affine):
+    # 3x3x3 mask with center voxel as 1, rest 0
+    data = np.zeros((3, 3, 3), dtype=np.uint8)
+    data[1, 1, 1] = 1
+    return nb.Nifti1Image(data, identity_affine)
+
+
+@pytest.fixture
+def translation_transform():
+    # Simple translation of (1, 2, 3) mm
+    return nt.linear.Affine(map=np.array([
+        [1, 0, 0, 1],
+        [0, 1, 0, 2],
+        [0, 0, 1, 3],
+        [0, 0, 0, 1],
+    ]))
+
+
+@pytest.fixture
+def rotation_transform():
+    # 90 degree rotation around z axis
+    angle = np.pi / 2
+    rot = np.array([
+        [np.cos(angle), -np.sin(angle), 0, 0],
+        [np.sin(angle), np.cos(angle), 0, 0],
+        [0, 0, 1, 0],
+        [0, 0, 0, 1],
+    ])
+    return nt.linear.Affine(map=rot)
+
+
+@pytest.mark.parametrize(
+    "test_xfm, reference_xfm, expected",
+    [
+        (nt.linear.Affine(np.eye(4)), None, np.zeros(1)),
+        (nt.linear.Affine(np.array([
+            [1, 0, 0, 1],
+            [0, 1, 0, 2],
+            [0, 0, 1, 3],
+            [0, 0, 0, 1],
+        ])), None, [np.linalg.norm([1, 2, 3])]),
+        (nt.linear.Affine(np.array([
+            [1, 0, 0, 1],
+            [0, 1, 0, 2],
+            [0, 0, 1, 3],
+            [0, 0, 0, 1],
+        ])), nt.linear.Affine(np.eye(4)), [np.linalg.norm([1, 2, 3])]),
+    ],
+)
+def test_displacements_within_mask(simple_mask_img, test_xfm, reference_xfm, expected):
+    disp = displacements_within_mask(simple_mask_img, test_xfm, reference_xfm)
+    np.testing.assert_allclose(disp, expected)
+
+
+@pytest.mark.parametrize(
+    "test_xfm, expected",
+    [
+        (nt.linear.Affine(np.eye(4)), 0),
+        (nt.linear.Affine(np.array([
+            [1, 0, 0, 1],
+            [0, 1, 0, 2],
+            [0, 0, 1, 3],
+            [0, 0, 0, 1],
+        ])), np.linalg.norm([1, 2, 3])),
+    ],
+)
+def test_compute_fd_from_transform(simple_mask_img, test_xfm, expected):
+    fd = compute_fd_from_transform(simple_mask_img, test_xfm)
+    assert np.isclose(fd, expected)
+
+
+@pytest.mark.parametrize(
+    "motion_params, radius, expected",
+    [
+        (np.zeros((5, 6)), 50, np.zeros(5)),  # 5 frames, 3 trans, 3 rot
+        (
+            np.array([
+                [0,0,0,0,0,0],
+                [2,0,0,0,0,0],  # 2mm translation in x at frame 1
+                [2,0,0,90,0,0],
+            ]),  # 90deg rotation in x at frame 2
+            50,
+            [0, 2, abs(np.deg2rad(90)) * 50]
+        ),  # First frame: 0, Second: translation 2mm, Third: rotation (pi/2)*50
+    ],
+)
+def test_compute_fd_from_motion(motion_params, radius, expected):
+    fd = compute_fd_from_motion(motion_params, radius=radius)
+    np.testing.assert_allclose(fd, expected, atol=1e-4)
+
+
+@pytest.mark.parametrize(
+    "affine, expected_trans, expected_rot",
+    [
+        (np.eye(4) + np.array([[0,0,0,10],[0,0,0,15],[0,0,0,20],[0,0,0,0]]),  # translation only
+         [10, 15, 20], [0, 0, 0]),
+        (np.array([
+            [1, 0, 0, 0],
+            [0, np.cos(np.deg2rad(30)), -np.sin(np.deg2rad(30)), 0],
+            [0, np.sin(np.deg2rad(30)), np.cos(np.deg2rad(30)), 0],
+            [0, 0, 0, 1],  # rotation only
+        ]), [0, 0, 0], [30, 0, 0]),  # Only one rot will be close to 30
+    ],
+)
+def test_extract_motion_parameters(affine, expected_trans, expected_rot):
+    params = extract_motion_parameters(affine)
+    assert np.allclose(params[:3], expected_trans)
+    # For rotation case, at least one value close to 30
+    if np.any(np.abs(expected_rot)):
+        assert np.any(np.isclose(np.abs(params[3:]), 30, atol=1e-4))
+    else:
+        assert np.allclose(params[3:], expected_rot)
+
+
+@pytest.mark.parametrize(
+    "reference, test, mask, expected",
+    [
+        (
+            np.array([[1.0, 2.0], [0.0, 4.0]]),
+            np.array([[2.0, 1.0], [3.0, 8.0]]),
+            np.array([[True, True], [True, True]]),
+            np.array([[(2.0 - 1.0) / 1.0, (1.0 - 2.0) / 2.0], [0, (8.0 - 4.0) / 4.0]]) * 100,
+        ),
+        (
+            np.zeros((2,2)),
+            np.ones((2,2)),
+            np.ones((2,2), dtype=bool),
+            np.zeros((2,2)),
+        ),
+        (
+            np.array([[5, 10], [15, 20]]),
+            np.array([[10, 5], [30, 10]]),
+            np.array([[False, True], [True, False]]),
+            np.array([[0, (5 - 10) / 10], [(30 - 15) / 15, 0]]) * 100,
+        ),
+    ],
+)
+def test_compute_percentage_change_param(reference, test, mask, expected):
+    result = compute_percentage_change(reference, test, mask)
+    np.testing.assert_array_almost_equal(result, expected)
+
+
+def test_identify_spikes(request):
+    rng = request.node.rng
+
+    n_samples = 450
+
+    fd = rng.normal(0, 5, n_samples)
+    threshold = 2.0
+
+    expected_indices = np.asarray(
+        [5, 57, 85, 100, 127, 180, 191, 202, 335, 393, 409]
+    )
+    expected_mask = np.zeros(n_samples, dtype=bool)
+    expected_mask[expected_indices] = True
+
+    obtained_indices, obtained_mask = identify_spikes(fd, threshold=threshold)
+
+    assert np.array_equal(obtained_indices, expected_indices)
+    assert np.array_equal(obtained_mask, expected_mask)