def test_transform_origins_3d():
# Create arbitrary image-to-space transforms
axis = np.array([.5, 2.0, 1.5])
t = 0.15 # translation factor
for theta in [-1 * np.pi / 6.0, 0.0, np.pi / 5.0]: # rotation angle
for s in [0.83, 1.3, 2.07]: # scale
m_shapes = [(256, 256, 128), (255, 255, 127), (64, 127, 142)]
for shape_moving in m_shapes:
s_shapes = [(256, 256, 128), (255, 255, 127), (64, 127, 142)]
for shape_static in s_shapes:
moving = np.ndarray(shape=shape_moving)
static = np.ndarray(shape=shape_static)
trans = np.array([[1, 0, 0, -t * shape_static[0]],
[0, 1, 0, -t * shape_static[1]],
[0, 0, 1, -t * shape_static[2]],
[0, 0, 0, 1]])
trans_inv = npl.inv(trans)
rot = np.zeros(shape=(4, 4))
rot[:3, :3] = geometry.rodrigues_axis_rotation(axis, theta)
rot[3, 3] = 1.0
scale = np.array([[1 * s, 0, 0, 0],
[0, 1 * s, 0, 0],
[0, 0, 1 * s, 0],
[0, 0, 0, 1]])
static_grid2world = trans_inv.dot(
scale.dot(rot.dot(trans)))
moving_grid2world = npl.inv(static_grid2world)
# Expected translation
c_static = static_grid2world[:3, 3]
c_moving = moving_grid2world[:3, 3]
expected = np.eye(4)
expected[:3, 3] = c_moving - c_static
# Implementation under test
actual = imaffine.transform_origins(static,
static_grid2world,
moving,
moving_grid2world)
assert_array_almost_equal(actual.affine, expected)
def test_transform_origins_3d():
# Create arbitrary image-to-space transforms
axis = np.array([.5, 2.0, 1.5])
t = 0.15 # translation factor
for theta in [-1 * np.pi / 6.0, 0.0, np.pi / 5.0]: # rotation angle
for s in [0.83, 1.3, 2.07]: # scale
m_shapes = [(256, 256, 128), (255, 255, 127), (64, 127, 142)]
for shape_moving in m_shapes:
s_shapes = [(256, 256, 128), (255, 255, 127), (64, 127, 142)]
for shape_static in s_shapes:
moving = np.ndarray(shape=shape_moving)
static = np.ndarray(shape=shape_static)
trans = np.array([[1, 0, 0, -t * shape_static[0]],
[0, 1, 0, -t * shape_static[1]],
[0, 0, 1, -t * shape_static[2]],
[0, 0, 0, 1]])
trans_inv = npl.inv(trans)
rot = np.zeros(shape=(4, 4))
rot[:3, :3] = geometry.rodrigues_axis_rotation(axis, theta)
rot[3, 3] = 1.0
scale = np.array([[1 * s, 0, 0, 0],
[0, 1 * s, 0, 0],
[0, 0, 1 * s, 0],
[0, 0, 0, 1]])
static_grid2world = trans_inv.dot(
scale.dot(rot.dot(trans)))
moving_grid2world = npl.inv(static_grid2world)
# Expected translation
c_static = static_grid2world[:3, 3]
c_moving = moving_grid2world[:3, 3]
expected = np.eye(4)
expected[:3, 3] = c_moving - c_static
# Implementation under test
actual = imaffine.transform_origins(static,
static_grid2world,
moving,
moving_grid2world)
assert_array_almost_equal(actual.affine, expected)
transform_origins)
from dipy.align.transforms import (TranslationTransform3D, RigidTransform3D,
AffineTransform3D)
static = np.asarray(img_t2_mni.dataobj)
static_affine = img_t2_mni.affine
moving = mean_b0_masked_stanford
moving_affine = hardi_img.affine
"""
We estimate an affine that maps the origin of the moving image to that of the
static image. We can then use this later to account for the offsets of each
image.
"""
affine_map = transform_origins(static, static_affine, moving, moving_affine)
"""
We specify the mismatch metric:
"""
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
"""
As well as the optimization strategy:
"""
level_iters = [10, 10, 5]
sigmas = [3.0, 1.0, 0.0]
def wm_syn(t1w_brain,
ap_path,
working_dir,
fa_path=None,
template_fa_path=None):
"""
A function to perform SyN registration
Parameters
----------
t1w_brain : str
File path to the skull-stripped T1w brain Nifti1Image.
ap_path : str
File path to the AP moving image.
working_dir : str
Path to the working directory to perform SyN and save outputs.
fa_path : str
File path to the FA moving image.
template_fa_path : str
File path to the T1w-connformed template FA reference image.
"""
import uuid
from time import strftime
from dipy.align.imaffine import (
MutualInformationMetric,
AffineRegistration,
transform_origins,
)
from dipy.align.transforms import (
TranslationTransform3D,
RigidTransform3D,
AffineTransform3D,
)
from dipy.align.imwarp import SymmetricDiffeomorphicRegistration
from dipy.align.metrics import CCMetric
# from dipy.viz import regtools
# from nilearn.image import resample_to_img
ap_img = nib.load(ap_path)
t1w_brain_img = nib.load(t1w_brain)
static = np.asarray(t1w_brain_img.dataobj, dtype=np.float32)
static_affine = t1w_brain_img.affine
moving = np.asarray(ap_img.dataobj, dtype=np.float32)
moving_affine = ap_img.affine
affine_map = transform_origins(static, static_affine, moving,
moving_affine)
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [10, 10, 5]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affine_reg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = TranslationTransform3D()
params0 = None
translation = affine_reg.optimize(static, moving, transform, params0,
static_affine, moving_affine)
transform = RigidTransform3D()
rigid_map = affine_reg.optimize(
static,
moving,
transform,
params0,
static_affine,
moving_affine,
starting_affine=translation.affine,
)
transform = AffineTransform3D()
# We bump up the iterations to get a more exact fit:
affine_reg.level_iters = [1000, 1000, 100]
affine_opt = affine_reg.optimize(
static,
moving,
transform,
params0,
static_affine,
moving_affine,
starting_affine=rigid_map.affine,
)
# We now perform the non-rigid deformation using the Symmetric
# Diffeomorphic Registration(SyN) Algorithm:
metric = CCMetric(3)
level_iters = [10, 10, 5]
# Refine fit
if template_fa_path is not None:
from nilearn.image import resample_to_img
fa_img = nib.load(fa_path)
template_img = nib.load(template_fa_path)
template_img_res = resample_to_img(template_img, t1w_brain_img)
static = np.asarray(template_img_res.dataobj, dtype=np.float32)
static_affine = template_img_res.affine
moving = np.asarray(fa_img.dataobj, dtype=np.float32)
moving_affine = fa_img.affine
else:
static = np.asarray(t1w_brain_img.dataobj, dtype=np.float32)
static_affine = t1w_brain_img.affine
moving = np.asarray(ap_img.dataobj, dtype=np.float32)
moving_affine = ap_img.affine
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(static, moving, static_affine, moving_affine,
affine_opt.affine)
warped_moving = mapping.transform(moving)
# Save warped FA image
run_uuid = f"{strftime('%Y%m%d_%H%M%S')}_{uuid.uuid4()}"
warped_fa = f"{working_dir}/warped_fa_{run_uuid}.nii.gz"
nib.save(nib.Nifti1Image(warped_moving, affine=static_affine), warped_fa)
# # We show the registration result with:
# regtools.overlay_slices(static, warped_moving, None, 0,
# "Static", "Moving",
# "%s%s%s%s" % (working_dir,
# "/transformed_sagittal_", run_uuid, ".png"))
# regtools.overlay_slices(static, warped_moving, None,
# 1, "Static", "Moving",
# "%s%s%s%s" % (working_dir,
# "/transformed_coronal_", run_uuid, ".png"))
# regtools.overlay_slices(static, warped_moving,
# None, 2, "Static", "Moving",
# "%s%s%s%s" % (working_dir,
# "/transformed_axial_", run_uuid, ".png"))
return mapping, affine_map, warped_fa
def wm_syn(template_path, fa_path, working_dir):
"""
A function to perform ANTS SyN registration
Parameters
----------
template_path : str
File path to the template reference image.
fa_path : str
File path to the FA moving image.
working_dir : str
Path to the working directory to perform SyN and save outputs.
"""
import uuid
from time import strftime
from dipy.align.imaffine import MutualInformationMetric, AffineRegistration, transform_origins
from dipy.align.transforms import TranslationTransform3D, RigidTransform3D, AffineTransform3D
from dipy.align.imwarp import SymmetricDiffeomorphicRegistration
from dipy.align.metrics import CCMetric
from dipy.viz import regtools
fa_img = nib.load(fa_path)
template_img = nib.load(template_path)
static = np.asarray(template_img.dataobj)
static_affine = template_img.affine
moving = np.asarray(fa_img.dataobj).astype(np.float32)
moving_affine = fa_img.affine
affine_map = transform_origins(static, static_affine, moving,
moving_affine)
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [10, 10, 5]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affine_reg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = TranslationTransform3D()
params0 = None
translation = affine_reg.optimize(static, moving, transform, params0,
static_affine, moving_affine)
transform = RigidTransform3D()
rigid_map = affine_reg.optimize(static,
moving,
transform,
params0,
static_affine,
moving_affine,
starting_affine=translation.affine)
transform = AffineTransform3D()
# We bump up the iterations to get a more exact fit:
affine_reg.level_iters = [1000, 1000, 100]
affine_opt = affine_reg.optimize(static,
moving,
transform,
params0,
static_affine,
moving_affine,
starting_affine=rigid_map.affine)
# We now perform the non-rigid deformation using the Symmetric Diffeomorphic Registration(SyN) Algorithm:
metric = CCMetric(3)
level_iters = [10, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(static, moving, static_affine, moving_affine,
affine_opt.affine)
warped_moving = mapping.transform(moving)
# Save warped FA image
run_uuid = '%s_%s' % (strftime('%Y%m%d_%H%M%S'), uuid.uuid4())
warped_fa = '{}/warped_fa_{}.nii.gz'.format(working_dir, run_uuid)
nib.save(nib.Nifti1Image(warped_moving, affine=static_affine), warped_fa)
# We show the registration result with:
regtools.overlay_slices(
static, warped_moving, None, 0, "Static", "Moving",
"%s%s%s%s" % (working_dir, "/transformed_sagittal_", run_uuid, ".png"))
regtools.overlay_slices(
static, warped_moving, None, 1, "Static", "Moving",
"%s%s%s%s" % (working_dir, "/transformed_coronal_", run_uuid, ".png"))
regtools.overlay_slices(
static, warped_moving, None, 2, "Static", "Moving",
"%s%s%s%s" % (working_dir, "/transformed_axial_", run_uuid, ".png"))
return mapping, affine_map, warped_fa
def wm_syn(template_path, fa_path, working_dir):
"""A function to perform ANTS SyN registration using dipy functions
Parameters
----------
template_path : str
File path to the template reference FA image.
fa_path : str
File path to the FA moving image (image to be fitted to reference)
working_dir : str
Path to the working directory to perform SyN and save outputs.
Returns
-------
DiffeomorphicMap
An object that can be used to register images back and forth between static (template) and moving (FA) domains
AffineMap
An object used to transform the moving (FA) image towards the static image (template)
"""
fa_img = nib.load(fa_path)
template_img = nib.load(template_path)
static = template_img.get_data()
static_affine = template_img.affine
moving = fa_img.get_data().astype(np.float32)
moving_affine = fa_img.affine
affine_map = transform_origins(static, static_affine, moving,
moving_affine)
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [10, 10, 5]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affine_reg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = TranslationTransform3D()
params0 = None
translation = affine_reg.optimize(static, moving, transform, params0,
static_affine, moving_affine)
transform = RigidTransform3D()
rigid_map = affine_reg.optimize(
static,
moving,
transform,
params0,
static_affine,
moving_affine,
starting_affine=translation.affine,
)
transform = AffineTransform3D()
# We bump up the iterations to get a more exact fit:
affine_reg.level_iters = [1000, 1000, 100]
affine_opt = affine_reg.optimize(
static,
moving,
transform,
params0,
static_affine,
moving_affine,
starting_affine=rigid_map.affine,
)
# We now perform the non-rigid deformation using the Symmetric Diffeomorphic Registration(SyN) Algorithm:
metric = CCMetric(3)
level_iters = [10, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(static, moving, static_affine, moving_affine,
affine_opt.affine)
warped_moving = mapping.transform(moving)
# We show the registration result with:
regtools.overlay_slices(
static,
warped_moving,
None,
0,
"Static",
"Moving",
f"{working_dir}/transformed_sagittal.png",
)
regtools.overlay_slices(
static,
warped_moving,
None,
1,
"Static",
"Moving",
f"{working_dir}/transformed_coronal.png",
)
regtools.overlay_slices(
static,
warped_moving,
None,
2,
"Static",
"Moving",
f"{working_dir}/transformed_axial.png",
)
return mapping, affine_map
