def register_image(static,
static_grid2world,
moving,
moving_grid2world,
transformation_type='affine',
dwi=None):
if transformation_type not in ['rigid', 'affine']:
raise ValueError('Transformation type not available in Dipy')
# Set all parameters for registration
nbins = 32
params0 = None
sampling_prop = None
level_iters = [50, 25, 5]
sigmas = [8.0, 4.0, 2.0]
factors = [8, 4, 2]
metric = MutualInformationMetric(nbins, sampling_prop)
reg_obj = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors,
verbosity=0)
# First, align the center of mass of both volume
c_of_mass = transform_centers_of_mass(static, static_grid2world, moving,
moving_grid2world)
# Then, rigid transformation (translation + rotation)
transform = RigidTransform3D()
rigid = reg_obj.optimize(static,
moving,
transform,
params0,
static_grid2world,
moving_grid2world,
starting_affine=c_of_mass.affine)
if transformation_type == 'affine':
# Finally, affine transformation (translation + rotation + scaling)
transform = AffineTransform3D()
affine = reg_obj.optimize(static,
moving,
transform,
params0,
static_grid2world,
moving_grid2world,
starting_affine=rigid.affine)
mapper = affine
transformation = affine.affine
else:
mapper = rigid
transformation = rigid.affine
if dwi is not None:
trans_dwi = transform_dwi(mapper, static, dwi)
return trans_dwi, transformation
else:
return mapper.transform(moving), transformation
def register_affine(t_masked,
m_masked,
affreg=None,
final_iters=(10000, 1000, 100)):
""" Run affine registration between images `t_masked`, `m_masked`
Parameters
----------
t_masked : image
Template image object, with image data masked to set out-of-brain
voxels to zero.
m_masked : image
Moving (individual) image object, with image data masked to set
out-of-brain voxels to zero.
affreg : None or AffineRegistration instance, optional
AffineRegistration with which to register `m_masked` to `t_masked`. If
None, we make an instance with default parameters.
final_iters : tuple, optional
Length 3 tuple of level iterations to use on final affine pass of the
registration.
Returns
-------
affine : shape (4, 4) ndarray
Final affine mapping from voxels in `t_masked` to voxels in `m_masked`.
"""
if affreg is None:
metric = MutualInformationMetric(nbins=32, sampling_proportion=None)
affreg = AffineRegistration(metric=metric)
t_data = t_masked.get_data().astype(float)
m_data = m_masked.get_data().astype(float)
t_aff = t_masked.affine
m_aff = m_masked.affine
translation = affreg.optimize(t_data, m_data, TranslationTransform3D(),
None, t_aff, m_aff)
rigid = affreg.optimize(t_data,
m_data,
RigidTransform3D(),
None,
t_aff,
m_aff,
starting_affine=translation.affine)
# Maybe bump up iterations for last step
if final_iters is not None:
affreg.level_iters = list(final_iters)
affine = affreg.optimize(t_data,
m_data,
AffineTransform3D(),
None,
t_aff,
m_aff,
starting_affine=rigid.affine)
return affine.affine
def translation_transform(static, moving, static_grid2world, moving_grid2world,
nbins, sampling_prop, metric, level_iters, sigmas,
factors, starting_affine):
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [10000, 1000, 100]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = TranslationTransform3D()
params0 = None
translation = affreg.optimize(static,
moving,
transform,
params0,
static_grid2world,
moving_grid2world,
starting_affine=starting_affine)
return translation
def fine_alignment(static, moving, starting_affine=None):
"""Use mutual information to align two images.
Parameters
----------
static : array
The reference image.
moving : array
The moving image.
starting_affine : array
A proposed initial transformation
Returns
-------
img_warp : array
The moving image warped towards the static image
affine : array
The affine transformation for this warping
"""
metric = MutualInformationMetric()
reggy = AffineRegistration(metric=metric)
transform = AffineTransform2D()
affine = reggy.optimize(static,
moving,
transform,
None,
starting_affine=starting_affine)
img_warp = affine.transform(moving)
return img_warp, affine.affine
def rigid_registration(fixed, moving, metric=MutualInformationMetric(), **kwargs):
affreg = AffineRegistration(metric=metric, **kwargs)
rigid = affreg.optimize(fixed.get_data(), moving.get_data(),
RigidTransform3D(), None,
fixed.affine, moving.affine)
transformed = rigid.transform(moving.get_data())
# convert transformed to full nibabel image, just data now
# TODO: get affine for transformed from rigid.affine?
transformed = new_img_like(moving, transformed)
return transformed
def registration(ref, moving, ref_mask=None, moving_mask=None):
ref_mask_data, mov_mask_data = None, None
ref_data = ref.get_fdata()
if ref_mask:
ref_mask_data = ref_mask.get_fdata() > 0.5
mov_data = moving.get_fdata()
if moving_mask:
mov_mask_data = moving_mask.get_fdata() > 0.5
metric = MutualInformationMetric(nbins=32, sampling_proportion=None)
transform = RigidTransform3D()
affreg = AffineRegistration(
metric=metric, level_iters=[10000, 1000, 0], factors=[6, 4, 2], sigmas=[4, 2, 0]
)
rigid = affreg.optimize(
ref_data,
mov_data,
transform,
None,
ref.affine,
moving.affine,
starting_affine="mass",
static_mask=ref_mask_data,
moving_mask=mov_mask_data,
)
affreg = AffineRegistration(
metric=metric, level_iters=[10000, 1000, 0], factors=[4, 2, 2], sigmas=[4, 2, 0]
)
transform = RigidScalingTransform3D()
# transform = AffineTransform3D()
return affreg.optimize(
ref_data,
mov_data,
transform,
None,
ref.affine,
moving.affine,
starting_affine=rigid.affine,
static_mask=ref_mask_data,
moving_mask=mov_mask_data,
)
def dipy_align(static, static_grid2world, moving, moving_grid2world, prealign=None):
r""" Full rigid registration with Dipy's imaffine module
Here we implement an extra optimization heuristic: move the geometric
centers of the images to the origin. Imaffine does not do this by default
because we want to give the user as much control of the optimization
process as possible.
"""
# Bring the center of the moving image to the origin
c_moving = tuple(0.5 * np.array(moving.shape, dtype=np.float64))
c_moving = moving_grid2world.dot(c_moving + (1,))
correction_moving = np.eye(4, dtype=np.float64)
correction_moving[:3, 3] = -1 * c_moving[:3]
centered_moving_aff = correction_moving.dot(moving_grid2world)
# Bring the center of the static image to the origin
c_static = tuple(0.5 * np.array(static.shape, dtype=np.float64))
c_static = static_grid2world.dot(c_static + (1,))
correction_static = np.eye(4, dtype=np.float64)
correction_static[:3, 3] = -1 * c_static[:3]
centered_static_aff = correction_static.dot(static_grid2world)
dim = len(static.shape)
metric = MutualInformationMetric(nbins=32, sampling_proportion=0.3)
level_iters = [10000, 1000, 100]
affr = AffineRegistration(metric=metric, level_iters=level_iters)
affr.verbosity = VerbosityLevels.DEBUG
# metric.verbosity = VerbosityLevels.DEBUG
# Registration schedule: center-of-mass then translation, then rigid and then affine
if prealign is None:
prealign = "mass"
transforms = ["TRANSLATION", "RIGID", "AFFINE"]
sol = np.eye(dim + 1)
for transform_name in transforms:
transform = regtransforms[(transform_name, dim)]
print("Optimizing: %s" % (transform_name,))
x0 = None
sol = affr.optimize(
static, moving, transform, x0, centered_static_aff, centered_moving_aff, starting_affine=prealign
)
prealign = sol.affine.copy()
# Now bring the geometric centers back to their original location
fixed = np.linalg.inv(correction_moving).dot(sol.affine.dot(correction_static))
sol.set_affine(fixed)
sol.domain_grid2world = static_grid2world
sol.codomain_grid2world = moving_grid2world
return sol
def estimate_translation(
fixed,
moving,
metric_sampling=1.0,
factors=(4, 2, 1),
level_iters=(1000, 1000, 1000),
sigmas=(8, 4, 1),
):
"""
Estimate translation between 2D or 3D images using dipy.align.
Parameters
----------
fixed : numpy array, 2D or 3D
The reference image.
moving : numpy array, 2D or 3D
The image to be transformed.
metric_sampling : float, within the interval (0, 1]
Fraction of the metric sampling to use for optimization
factors : iterable
The image pyramid factors to use
level_iters : iterable
Number of iterations per pyramid level
sigmas : iterable
Standard deviation of gaussian blurring for each pyramid level
"""
from dipy.align.transforms import TranslationTransform2D, TranslationTransform3D
from dipy.align.imaffine import AffineRegistration, MutualInformationMetric
metric = MutualInformationMetric(32, metric_sampling)
affreg = AffineRegistration(
metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors,
verbosity=0,
)
if fixed.ndim == 2:
transform = TranslationTransform2D()
elif fixed.ndim == 3:
transform = TranslationTransform3D()
tx = affreg.optimize(fixed, moving, transform, params0=None)
return tx
def register(metric_name, static, moving, static_grid2space, moving_grid2space):
if metric_name == "LCC":
from dipy.align.imaffine import LocalCCMetric
radius = 4
metric = LocalCCMetric(radius)
elif metric_name == "MI":
nbins = 32
sampling_prop = None
metric = MattesMIMetric(nbins, sampling_prop)
else:
raise ValueError("Unknown metric " + metric_name)
align_centers = True
# schedule = ['TRANSLATION', 'RIGID', 'AFFINE']
schedule = ["TRANSLATION", "RIGID"]
if True:
level_iters = [100, 100, 100]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
else:
level_iters = [100]
sigmas = [0.0]
factors = [1]
affreg = AffineRegistration(metric=metric, level_iters=level_iters, sigmas=sigmas, factors=factors)
out = np.eye(4)
if align_centers:
print("Aligning centers of mass")
c_static = ndimage.measurements.center_of_mass(np.array(static))
c_static = static_grid2space.dot(c_static + (1,))
original_static = static_grid2space.copy()
static_grid2space = static_grid2space.copy()
static_grid2space[:3, 3] -= c_static[:3]
out = align_centers_of_mass(static, static_grid2space, moving, moving_grid2space)
for step in schedule:
print("Optimizing: %s" % (step,))
transform = regtransforms[(step, 3)]
params0 = None
out = affreg.optimize(
static, moving, transform, params0, static_grid2space, moving_grid2space, starting_affine=out
)
if align_centers:
print("Updating center-of-mass reference")
T = np.eye(4)
T[:3, 3] = -1 * c_static[:3]
out = out.dot(T)
return out
def align_xmap_np(static,
moving,
static_grid2world=np.eye(4),
moving_grid2world=np.eye(4)):
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [10000, 1000, 100]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors,
verbosity=0)
transform = RigidTransform3D()
params0 = None
# starting_affine = transform_centers_of_mass(static,
# static_grid2world,
# moving,
# moving_grid2world,
# ).affine
# print("\tCOM affine transform is: {}".format(starting_affine))
starting_affine = np.eye(4)
rigid = affreg.optimize(
static,
moving,
transform,
params0,
static_grid2world,
moving_grid2world,
starting_affine=starting_affine,
# verbosity=0,
)
# Transform
transformed = rigid.transform(moving)
# print("\tThe transform is: {}".format(rigid.affine))
return transformed
def mutualInfo_dipy(img1, img2):
img1_grid2world = np.identity(3)
img2_grid2world = np.identity(3)
# compute center of mass
c_of_mass = transform_centers_of_mass(img1, img1_grid2world, img2,
img2_grid2world)
x_shift = c_of_mass.affine[1, -1]
y_shift = c_of_mass.affine[0, -1]
# prepare affine registration
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [10000, 1000, 100]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
# translation
translation = affreg.optimize(img1,
img2,
TranslationTransform2D(),
None,
img1_grid2world,
img2_grid2world,
starting_affine=c_of_mass.affine)
# rotation
# ~ rigid = affreg.optimize(im1, im2, RigidTransform2D(), None,
# ~ im1_grid2world, im2_grid2world,
# ~ starting_affine=translation.affine)
# ~ transformed = rigid.transform(im2)
# ~ # resize, shear
# ~ affine = affreg.optimize(im1, im2, AffineTransform2D(), None,
# ~ im1_grid2world, im2_grid2world,
# ~ starting_affine=rigid.affine)
x_shift = translation.affine[1, -1]
y_shift = translation.affine[0, -1]
return np.asarray([-x_shift, -y_shift])
def affine_transform(static, moving, static_grid2world, moving_grid2world,
nbins, sampling_prop, metric, level_iters, sigmas,
factors, starting_affine):
transform = AffineTransform3D()
params0 = None
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
affine = affreg.optimize(static,
moving,
transform,
params0,
static_grid2world,
moving_grid2world,
starting_affine=starting_affine)
return affine
def transform_affine(static, moving):
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [10000, 1000, 100]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = AffineTransform3D()
params0 = None
affine = affreg.optimize(static, moving, transform, params0, None, None,
None)
transformed = affine.transform(moving)
return transformed
def affine_registration(reference, reference_grid2world, scan,
scan_grid2world):
#get first b0 volumes for both scans
reference_b0 = reference[:, :, :, 0]
scan_b0 = scan[:, :, :, 0]
#In this function we use multiple stages to register the 2 scans
#providng previous results as initialisation to the next stage,
#the reason we do this is because registration is a non-convex
#problem thus it is important to initialise as close to the
#optiaml value as possible
#Stage1: we obtain a very rough (and fast) registration by just aligning
#the centers of mass of the two images
center_of_mass = transform_centers_of_mass(reference_b0,
reference_grid2world, scan_b0,
scan_grid2world)
#create the similarity metric (Mutual Information) to be used:
nbins = 32
sampling_prop = None #use all voxels to perform registration
metric = MutualInformationMetric(nbins, sampling_prop)
#We use a multi-resolution stratergy to accelerate convergence and avoid
#getting stuck at local optimas (below are the parameters)
level_iters = [10000, 1000, 100]
sigmas = [3.0, 1.0, 0.0
] #parameters for gaussian kernel smoothing at each resolution
factors = [4, 2, 1] #subsampling factor
#optimisation algorithm used is L-BFGS-B
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
#Stage2: Perform a basic translation transform
transform = TranslationTransform3D()
translation = affreg.optimize(reference_b0,
scan_b0,
transform,
None,
reference_grid2world,
scan_grid2world,
starting_affine=center_of_mass.affine)
#Stage3 : optimize previous result with a rigid transform
#(Includes translation, rotation)
transform = RigidTransform3D()
rigid = affreg.optimize(reference_b0,
scan_b0,
transform,
None,
reference_grid2world,
scan_grid2world,
starting_affine=translation.affine)
#Stage4 : optimize previous result with a affine transform
#(Includes translation, rotation, scale, shear)
transform = AffineTransform3D()
affine = affreg.optimize(reference_b0,
scan_b0,
transform,
None,
reference_grid2world,
scan_grid2world,
starting_affine=rigid.affine)
if params.reg_type == "SDR":
#Stage 5 : Symmetric Diffeomorphic Registration
metric = CCMetric(3)
level_iters = [400, 200, 100]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(reference_b0, scan_b0, reference_grid2world,
scan_grid2world, affine.affine)
else:
mapping = affine
#Once this is completed we can perform the affine transformation on each
#volume of scan2
for volume in range(0, scan.shape[3]):
#note affine is an AffineMap object,
#The transform method transforms the input image from co-domain to domain space
#By default, the transformed image is sampled at a grid defined by the shape of the domain
#The sampling is performed using linear interpolation (refer to comp vision lab on homographies)
scan[:, :, :, volume] = mapping.transform(scan[:, :, :, volume])
return scan
def register_3d(params):
r'''
Runs affine registration with the parsed parameters
'''
print('Registering %s to %s'%(params.moving, params.static))
sys.stdout.flush()
metric_name=params.metric[0:params.metric.find('[')]
metric_params_list=params.metric[params.metric.find('[')+1:params.metric.find(']')].split(',')
moving_mask = None
static_mask = None
#Initialize the appropriate metric
if metric_name == 'MI':
nbins=int(metric_params_list[0])
sampling_proportion = None
try:
sampling_proportion = float(metric_params_list[1])
except:
pass
metric = MattesMIMetric(nbins, sampling_proportion)
elif metric_name == 'LCC':
from dipy.align.imaffine import LocalCCMetric
radius=int(metric_params_list[0])
metric = LocalCCMetric(radius)
else:
raise ValueError('Unknown metric: %s'%(metric_name,))
#Initialize the optimizer
opt_iter = [int(i) for i in params.iter.split(',')]
transforms = [t for t in params.transforms.split(',')]
if params.ss_sigma_factor is not None:
ss_sigma_factor = float(params.ss_sigma_factor)
else:
ss_sigma_factor = None
factors = [int(i) for i in params.factors.split(',')]
sigmas = [float(i) for i in params.sigmas.split(',')]
#method = 'CGGS'
method = params.method
affreg = AffineRegistration(metric=metric,
level_iters=opt_iter,
sigmas=sigmas,
factors=factors,
method=method,
ss_sigma_factor=ss_sigma_factor,
options=None)
#Load the data
moving_nib = nib.load(params.moving)
moving_affine = moving_nib.get_affine()
moving = moving_nib.get_data().squeeze().astype(np.float64)
# Bring the center of the image to the origin
#c_moving = ndimage.measurements.center_of_mass(np.array(moving))
c_moving = tuple(0.5 * np.array(moving.shape, dtype=np.float64))
c_moving = moving_affine.dot(c_moving+(1,))
correction_moving = np.eye(4, dtype=np.float64)
correction_moving[:3,3] = -1 * c_moving[:3]
centered_moving_aff = correction_moving.dot(moving_affine)
static_nib = nib.load(params.static)
static_affine = static_nib.get_affine()
static = static_nib.get_data().squeeze().astype(np.float64)
# Bring the center of the image to the origin
#c_static = ndimage.measurements.center_of_mass(np.array(static))
c_static = tuple(0.5 * np.array(static.shape, dtype=np.float64))
c_static = static_affine.dot(c_static+(1,))
correction_static = np.eye(4, dtype=np.float64)
correction_static[:3,3] = -1 * c_static[:3]
centered_static_aff = correction_static.dot(static_affine)
dim = len(static.shape)
#Run the registration
sol = np.eye(dim + 1)
prealign = 'mass'
for transform_name in transforms:
transform = regtransforms[(transform_name, dim)]
print('Optimizing: %s'%(transform_name,))
x0 = None
sol = affreg.optimize(static, moving, transform, x0,
centered_static_aff, centered_moving_aff, starting_affine = prealign)
prealign = sol.affine.copy()
# Correct solution
fixed = np.linalg.inv(correction_moving).dot(sol.affine.dot(correction_static))
sol.set_affine(fixed)
sol.domain_grid2world = static_affine
sol.codomain_grid2world = moving_affine
save_registration_results(sol, params)
print('Solution: ', sol.affine)
def register_save(inputpathdir, target_path, subject, outputpath, figspath,
params, registration_types, applydirs, verbose):
anat_path = get_anat(inputpathdir, subject)
#myanat = load_nifti(anat_path)
myanat = nib.load(anat_path)
anat_data = np.squeeze(myanat.get_data()[..., 0])
anat_affine = myanat.affine
anat_hdr = myanat.header
vox_size = myanat.header.get_zooms()[0]
#mynifti = load_nifti("/Volumes/Data/Badea/Lab/19abb14/N57437_nii4D.nii")
#anat_data = np.squeeze(myanat[0])[..., 0]
#anat_affine = myanat[1]
#hdr = myanat.header
mytarget = nib.load(target_path)
target_data = np.squeeze(mytarget.get_data()[..., 0])
target_affine = mytarget.affine
identity = np.eye(4)
affine_map = AffineMap(identity, target_data.shape, target_affine,
anat_data.shape, anat_affine)
resampled = affine_map.transform(anat_data)
"""
regtools.overlay_slices(target_data, resampled, None, 0,
"target_data", "anat_data", figspath + "resampled_0.png")
regtools.overlay_slices(target_data, resampled, None, 1,
"target_data", "anat_data", figspath + "resampled_1.png")
regtools.overlay_slices(target_data, resampled, None, 2,
"target_data", "anat_data", figspath + "resampled_2.png")
"""
c_of_mass = transform_centers_of_mass(target_data, target_affine,
anat_data, anat_affine)
apply_niftis = []
apply_trks = []
if inputpathdir in applydirs:
applyfiles = [anat_path]
else:
applyfiles = []
for applydir in applydirs:
apply_niftis.extend(get_niftis(applydir, subject))
apply_trks.extend(get_trks(applydir, subject))
if "center_mass" in registration_types:
if apply_trks:
metric = CCMetric(3)
level_iters = [10, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(target_data, anat_data, target_affine,
anat_affine, c_of_mass.affine)
for apply_nifti in apply_niftis:
fname = os.path.basename(apply_nifti).split(".")[0]
fpath = outputpath + fname + "_centermass.nii"
applynii = nib.load(apply_nifti)
apply_data = applynii.get_data()
apply_affine = applynii.affine
apply_hdr = myanat.header
if len(np.shape(apply_data)) == 4:
transformed_all = c_of_mass.transform(apply_data, apply4D=True)
transformed = transformed_all[:, :, :, 0]
else:
transformed_all = c_of_mass.transform(apply_data)
transformed = transformed_all
save_nifti(fpath, transformed_all, apply_affine, hdr=apply_hdr)
if figspath is not None:
regtools.overlay_slices(target_data, transformed, None, 0,
"target_data", "Transformed",
figspath + fname + "_centermass_1.png")
regtools.overlay_slices(target_data, transformed, None, 1,
"target_data", "Transformed",
figspath + fname + "_centermass_2.png")
regtools.overlay_slices(target_data, transformed, None, 2,
"target_data", "Transformed",
figspath + fname + "_centermass_3.png")
if verbose:
print("Saved the file at " + fpath)
#mapping = sdr.optimize(target_data, anat_data, target_affine, anat_affine,
# c_of_mass.affine)
#warped_moving = mapping.transform(anat_data)
for apply_trk in apply_trks:
fname = os.path.basename(apply_trk).split(".")[0]
fpath = outputpath + fname + "_centermass.trk"
sft = load_tractogram(apply_trk, 'same')
target_isocenter = np.diag(
np.array([-vox_size, vox_size, vox_size, 1]))
origin_affine = affine_map.affine.copy()
origin_affine[0][3] = -origin_affine[0][3]
origin_affine[1][3] = -origin_affine[1][3]
origin_affine[2][3] = origin_affine[2][3] / vox_size
origin_affine[1][3] = origin_affine[1][3] / vox_size**2
# Apply the deformation and correct for the extents
mni_streamlines = deform_streamlines(
sft.streamlines,
deform_field=mapping.get_forward_field(),
stream_to_current_grid=target_isocenter,
current_grid_to_world=origin_affine,
stream_to_ref_grid=target_isocenter,
ref_grid_to_world=np.eye(4))
if has_fury:
show_template_bundles(mni_streamlines,
anat_data,
show=False,
fname=figspath + fname +
'_streamlines_centermass.png')
sft = StatefulTractogram(mni_streamlines, myanat, Space.RASMM)
save_tractogram(sft, fpath, bbox_valid_check=False)
if verbose:
print("Saved the file at " + fpath)
metric = MutualInformationMetric(params.nbins, params.sampling_prop)
if "AffineRegistration" in registration_types:
affreg = AffineRegistration(metric=metric,
level_iters=params.level_iters,
sigmas=params.sigmas,
factors=params.factors)
transform = TranslationTransform3D()
params0 = None
starting_affine = c_of_mass.affine
translation = affreg.optimize(target_data,
anat_data,
transform,
params0,
target_affine,
anat_affine,
starting_affine=starting_affine)
if apply_trks:
metric = CCMetric(3)
level_iters = [10, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(target_data, anat_data, target_affine,
anat_affine, translation.affine)
for apply_nifti in apply_niftis:
fname = os.path.basename(apply_nifti).split(".")[0]
fpath = outputpath + fname + "_affinereg.nii"
applynii = nib.load(apply_nifti)
apply_data = applynii.get_data()
apply_affine = applynii.affine
apply_hdr = myanat.header
if len(np.shape(apply_data)) == 4:
transformed_all = translation.transform(apply_data,
apply4D=True)
transformed = transformed_all[:, :, :, 0]
else:
transformed_all = translation.transform(apply_data)
transformed = transformed_all
save_nifti(fpath, transformed_all, anat_affine, hdr=anat_hdr)
if figspath is not None:
regtools.overlay_slices(target_data, transformed, None, 0,
"target_data", "Transformed",
figspath + fname + "_affinereg_1.png")
regtools.overlay_slices(target_data, transformed, None, 1,
"target_data", "Transformed",
figspath + fname + "_affinereg_2.png")
regtools.overlay_slices(target_data, transformed, None, 2,
"target_data", "Transformed",
figspath + fname + "_affinereg_3.png")
if verbose:
print("Saved the file at " + fpath)
for apply_trk in apply_trks:
fname = os.path.basename(apply_trk).split(".")[0]
fpath = outputpath + fname + "_affinereg.trk"
sft = load_tractogram(apply_trk, 'same')
target_isocenter = np.diag(
np.array([-vox_size, vox_size, vox_size, 1]))
origin_affine = affine_map.affine.copy()
origin_affine[0][3] = -origin_affine[0][3]
origin_affine[1][3] = -origin_affine[1][3]
origin_affine[2][3] = origin_affine[2][3] / vox_size
origin_affine[1][3] = origin_affine[1][3] / vox_size**2
# Apply the deformation and correct for the extents
mni_streamlines = deform_streamlines(
sft.streamlines,
deform_field=mapping.get_forward_field(),
stream_to_current_grid=target_isocenter,
current_grid_to_world=origin_affine,
stream_to_ref_grid=target_isocenter,
ref_grid_to_world=np.eye(4))
if has_fury:
show_template_bundles(mni_streamlines,
anat_data,
show=False,
fname=figspath + fname +
'_streamlines_affinereg.png')
sft = StatefulTractogram(mni_streamlines, myanat, Space.RASMM)
save_tractogram(sft, fpath, bbox_valid_check=False)
if verbose:
print("Saved the file at " + fpath)
if "RigidTransform3D" in registration_types:
transform = RigidTransform3D()
params0 = None
if 'translation' not in locals():
affreg = AffineRegistration(metric=metric,
level_iters=params.level_iters,
sigmas=params.sigmas,
factors=params.factors)
translation = affreg.optimize(target_data,
anat_data,
transform,
params0,
target_affine,
anat_affine,
starting_affine=c_of_mass.affine)
starting_affine = translation.affine
rigid = affreg.optimize(target_data,
anat_data,
transform,
params0,
target_affine,
anat_affine,
starting_affine=starting_affine)
transformed = rigid.transform(anat_data)
if apply_trks:
metric = CCMetric(3)
level_iters = [10, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(target_data, anat_data, target_affine,
anat_affine, rigid.affine)
for apply_nifti in apply_niftis:
fname = os.path.basename(apply_nifti).split(".")[0]
fpath = outputpath + fname + "_rigidtransf3d.nii"
applynii = nib.load(apply_nifti)
apply_data = applynii.get_data()
apply_affine = applynii.affine
apply_hdr = myanat.header
if len(np.shape(apply_data)) == 4:
transformed_all = rigid.transform(apply_data, apply4D=True)
transformed = transformed_all[:, :, :, 0]
else:
transformed_all = rigid.transform(apply_data)
transformed = transformed_all
save_nifti(fpath, transformed_all, anat_affine, hdr=anat_hdr)
if figspath is not None:
regtools.overlay_slices(
target_data, transformed, None, 0, "target_data",
"Transformed", figspath + fname + "_rigidtransf3d_1.png")
regtools.overlay_slices(
target_data, transformed, None, 1, "target_data",
"Transformed", figspath + fname + "_rigidtransf3d_2.png")
regtools.overlay_slices(
target_data, transformed, None, 2, "target_data",
"Transformed", figspath + fname + "_rigidtransf3d_3.png")
if verbose:
print("Saved the file at " + fpath)
for apply_trk in apply_trks:
fname = os.path.basename(apply_trk).split(".")[0]
fpath = outputpath + fname + "_rigidtransf3d.trk"
sft = load_tractogram(apply_trk, 'same')
target_isocenter = np.diag(
np.array([-vox_size, vox_size, vox_size, 1]))
origin_affine = affine_map.affine.copy()
origin_affine[0][3] = -origin_affine[0][3]
origin_affine[1][3] = -origin_affine[1][3]
origin_affine[2][3] = origin_affine[2][3] / vox_size
origin_affine[1][3] = origin_affine[1][3] / vox_size**2
# Apply the deformation and correct for the extents
mni_streamlines = deform_streamlines(
sft.streamlines,
deform_field=mapping.get_forward_field(),
stream_to_current_grid=target_isocenter,
current_grid_to_world=origin_affine,
stream_to_ref_grid=target_isocenter,
ref_grid_to_world=np.eye(4))
if has_fury:
show_template_bundles(mni_streamlines,
anat_data,
show=False,
fname=figspath + fname +
'_rigidtransf3d.png')
sft = StatefulTractogram(mni_streamlines, myanat, Space.RASMM)
save_tractogram(sft, fpath, bbox_valid_check=False)
if verbose:
print("Saved the file at " + fpath)
if "AffineTransform3D" in registration_types:
transform = AffineTransform3D()
params0 = None
starting_affine = rigid.affine
affine = affreg.optimize(target_data,
anat_data,
transform,
params0,
target_affine,
anat_affine,
starting_affine=starting_affine)
transformed = affine.transform(anat_data)
if apply_trks:
metric = CCMetric(3)
level_iters = [10, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(target_data, anat_data, target_affine,
anat_affine, affine.affine)
for apply_nifti in apply_niftis:
fname = os.path.basename(apply_nifti).split(".")[0]
fpath = outputpath + fname + "_affinetransf3d.nii"
applynii = nib.load(apply_nifti)
apply_data = applynii.get_data()
apply_affine = applynii.affine
apply_hdr = myanat.header
if len(np.shape(apply_data)) == 4:
transformed_all = affine.transform(apply_data, apply4D=True)
transformed = transformed_all[:, :, :, 0]
else:
transformed_all = affine.transform(apply_data)
transformed = transformed_all
save_nifti(fpath, transformed_all, anat_affine, hdr=anat_hdr)
if figspath is not None:
regtools.overlay_slices(
target_data, transformed, None, 0, "target_data",
"Transformed", figspath + fname + "_affinetransf3d_1.png")
regtools.overlay_slices(
target_data, transformed, None, 1, "target_data",
"Transformed", figspath + fname + "_affinetransf3d_2.png")
regtools.overlay_slices(
target_data, transformed, None, 2, "target_data",
"Transformed", figspath + fname + "_affinetransf3d_3.png")
if verbose:
print("Saved the file at " + fpath)
for apply_trk in apply_trks:
fname = os.path.basename(apply_trk).split(".")[0]
fpath = outputpath + fname + "_affinetransf3d.trk"
sft = load_tractogram(apply_trk, 'same')
target_isocenter = np.diag(
np.array([-vox_size, vox_size, vox_size, 1]))
origin_affine = affine_map.affine.copy()
origin_affine[0][3] = -origin_affine[0][3]
origin_affine[1][3] = -origin_affine[1][3]
origin_affine[2][3] = origin_affine[2][3] / vox_size
origin_affine[1][3] = origin_affine[1][3] / vox_size**2
# Apply the deformation and correct for the extents
mni_streamlines = deform_streamlines(
sft.streamlines,
deform_field=mapping.get_forward_field(),
stream_to_current_grid=target_isocenter,
current_grid_to_world=origin_affine,
stream_to_ref_grid=target_isocenter,
ref_grid_to_world=np.eye(4))
if has_fury:
show_template_bundles(mni_streamlines,
anat_data,
show=False,
fname=figspath + fname +
'_streamlines_affinetransf3d.png')
sft = StatefulTractogram(mni_streamlines, myanat, Space.RASMM)
save_tractogram(sft, fpath, bbox_valid_check=False)
if verbose:
print("Saved the file at " + fpath)
factors=factors)
"""
Now let's register these volumes together without any masking. For the purposes
of this example, we will not provide an inital transformation based on centre
of mass, but this would work fine with masks.
Note that use of masks is not currently implemented for sparse sampling.
"""
transform = TranslationTransform3D()
transl = affreg.optimize(static,
moving,
transform,
None,
static_grid2world,
moving_grid2world,
starting_affine=None,
static_mask=None,
moving_mask=None)
transform = RigidTransform3D()
transl = affreg.optimize(static,
moving,
transform,
None,
static_grid2world,
moving_grid2world,
starting_affine=transl.affine,
static_mask=None,
moving_mask=None)
transformed = transl.transform(moving)
def gm_network(mr_filename, gm_filename, at_filename, template_mr):
new_gmfilename = os.path.abspath(gm_filename).replace(".nii", "_mni.nii")
new_atfilename = os.path.abspath(at_filename).replace(".nii", "_mni.nii")
networks = 0
if not ((os.path.isfile(new_gmfilename)) or
(os.path.islink(new_gmfilename))):
print('file {} does not exist'.format(new_gmfilename))
print('performing registration to MNI ... ', end='')
start = time.process_time()
# see if we can maybe find a transform
tf_filename = os.path.abspath(gm_filename).split(
'.nii')[0] + "_reg.npz"
if not ((os.path.isfile(tf_filename)) or
(os.path.islink(tf_filename))):
# see https://dipy.org/documentation/1.2.0./examples_built/ ..
# .. affine_registration_3d/#example-affine-registration-3d
static, static_affine = load_nifti(template_mr)
moving, moving_affine = load_nifti(mr_filename)
grey, grey_affine = load_nifti(gm_filename)
atl, atl_affine = load_nifti(at_filename)
# first initialise by putting centres of mass on top of each other
c_of_mass = transform_centers_of_mass(static, static_affine,
moving, moving_affine)
# initialise transform parameters (e.g. the mutual information criterion)
# these parameters won' need to be changed between the different stages
nbins = 64
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [25, 15, 5]
sigmas = [2, 1, 0]
factors = [4, 2, 1]
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
# give slightly more degrees of freedom, by allowing translation of centre of gravity
print('\nTranslation only:')
transform = TranslationTransform3D()
params0 = None
translation = affreg.optimize(static,
moving,
transform,
params0,
static_affine,
moving_affine,
starting_affine=c_of_mass.affine)
# refine further by allowing all rigid transforms (rotations/translations around the centre of gravity)
print('Rigid transform:')
transform = RigidTransform3D()
params0 = None
rigid = affreg.optimize(static,
moving,
transform,
params0,
static_affine,
moving_affine,
starting_affine=translation.affine)
full_affine = False
# the GM networks method is based on keeping the cortical shape intact
if (full_affine):
# refine to a full affine transform by adding scaling and shearing
print('Affine transform:')
transform = AffineTransform3D()
params0 = None
affine = affreg.optimize(static,
moving,
transform,
params0,
static_affine,
moving_affine,
starting_affine=rigid.affine)
final = affine
else:
final = rigid
np.savez(tf_filename, final)
else:
with np.load(tf_filename, allow_pickle=True) as npzfile:
final = npzfile['arr_0']
# transform the grey matter data instead of the MRI itself
resampled = final.transform(grey)
save_nifti(new_gmfilename, resampled, static_affine)
resampled = final.transform(atl)
save_nifti(new_atfilename, resampled, static_affine)
print('finished in {:.2f}s'.format(time.process_time() - start))
if ((os.path.isfile(new_gmfilename)) or (os.path.islink(new_gmfilename))):
# only cube size implemented so far
cubesize = 3
# load the grey matter map and the template to which it was registered
gm_img = nib.load(new_gmfilename)
template_data = np.asarray(nib.load(template_mr).dataobj)
gm_data = np.asarray(gm_img.dataobj)
# find the best cube grid position (with the most nonzero cubes)
cube_nonzeros, cube_offsets, gm_incubes = cube_grid_position(
gm_data, template_data, cubesize)
gm_shape = gm_incubes.shape
# write out the cube map, where each voxel in a cube is labelled with its cube index
# be aware of the @ operator, this is a true matrix product A[n*m] x B[m*p] = C [n*p]
cubes_data = np.zeros(template_data.shape).flatten()
cubes_data[cube_offsets] = np.ones(
cubesize**3)[:, np.newaxis] @ np.arange(cube_nonzeros).reshape(
1, cube_nonzeros)
cubes_data = cubes_data.reshape(template_data.shape)
cubes_file = os.path.abspath(gm_filename).replace(
".nii", "_cubes.nii")
cubes_map = nib.Nifti1Image(cubes_data, gm_img.affine)
cubes_map.to_filename(cubes_file)
# make a randomised version of the grey matter densities in the cubes
# 1: exchange between and inside cubes (could be too many degrees of freedom!)
gm_random = gm_incubes.flatten()
gm_random = gm_random[np.random.permutation(
len(gm_random)).reshape(gm_shape)]
# 2: exchange cubes only ( this won't change the values in the correlation matrix, only positions )
# gm_random = gm_incubes [ :, np.random.permutation ( gm_shape [1] ) ];
# 3: exchange cubes and shuffle inside cubes
# gm_random = gm_incubes [ np.random.permutation ( gm_shape [0] ), np.random.permutation ( gm_shape [1] )[ :, np.newaxis ] ];
add_diag = True
# name of the NIfTI file with networks
networks_file = os.path.abspath(gm_filename).replace(
".nii", "_gmnet.nii")
if not ((os.path.isfile(networks_file)) or
(os.path.islink(networks_file))):
# compute the cross correlation for observed and randomised cubes
networks = cube_cross_correlation(gm_incubes, gm_random, cubesize,
add_diag)
# save the networks to a file
networks_map = nib.Nifti1Image(networks, np.eye(4))
networks_map.to_filename(networks_file)
else:
print("loading already existing file")
networks = np.asarray(nib.load(networks_file).dataobj)
return networks, networks_file
def img_reg(moving_img, target_img, reg='non-lin'):
m_img = nib.load(moving_img)
t_img = nib.load(target_img)
m_img_data = m_img.get_data()
t_img_data = t_img.get_data()
m_img_affine = m_img.affine
t_img_affine = t_img.affine
identity = np.eye(4)
affine_map = AffineMap(identity, t_img_data.shape, t_img_affine,
m_img_data.shape, m_img_affine)
m_img_resampled = affine_map.transform(m_img_data)
c_of_mass = transform_centers_of_mass(t_img_data, t_img_affine, m_img_data,
m_img_affine)
tf_m_img_c_mass = c_of_mass.transform(m_img_data)
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]
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = TranslationTransform3D()
params0 = None
starting_affine = c_of_mass.affine
translation = affreg.optimize(t_img_data,
m_img_data,
transform,
params0,
t_img_affine,
m_img_affine,
starting_affine=starting_affine)
tf_m_img_translat = translation.transform(m_img_data)
transform = RigidTransform3D()
params0 = None
starting_affine = translation.affine
rigid = affreg.optimize(t_img_data,
m_img_data,
transform,
params0,
t_img_affine,
m_img_affine,
starting_affine=starting_affine)
tf_m_img_rigid = rigid.transform(m_img_data)
transform = AffineTransform3D()
affreg.level_iters = [10, 10, 10]
affine = affreg.optimize(t_img_data,
m_img_data,
transform,
params0,
t_img_affine,
m_img_affine,
starting_affine=rigid.affine)
if reg is None or reg == 'non-lin':
metric = CCMetric(3)
level_iters = [10, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(t_img_data, m_img_data, t_img_affine,
m_img_affine, affine.affine)
tf_m_img = mapping.transform(m_img_data)
elif reg == 'affine':
tf_m_img_aff = affine.transform(m_img_data)
return tf_m_img
metric = CCMetric(3)
level_iters = [10, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(t_img_data,
m_img_data,
t_img_affine,
m_img_affine,
starting_affine=affine.affine)
tf_m_img = mapping.transform(m_img_data)
def main():
parser = _build_arg_parser()
args = parser.parse_args()
if args.load_transfo and args.in_native_fa is None:
parser.error('When loading a transformation, the final reference is '
'needed, use --in_native_fa.')
assert_inputs_exist(parser, [args.in_dsi_tractogram, args.in_dsi_fa],
optional=args.in_native_fa)
assert_outputs_exist(parser, args, args.out_tractogram)
sft = load_tractogram(args.in_dsi_tractogram,
'same',
bbox_valid_check=False)
# LPS -> RAS convention in voxel space
sft.to_vox()
flip_axis = ['x', 'y']
sft_fix = StatefulTractogram(sft.streamlines, args.in_dsi_fa, Space.VOXMM)
sft_fix.to_vox()
sft_fix.streamlines._data -= get_axis_shift_vector(flip_axis)
sft_flip = flip_sft(sft_fix, flip_axis)
sft_flip.to_rasmm()
sft_flip.streamlines._data -= [0.5, 0.5, -0.5]
if not args.in_native_fa:
if args.cut_invalid:
sft_flip, _ = cut_invalid_streamlines(sft_flip)
elif args.remove_invalid:
sft_flip.remove_invalid_streamlines()
save_tractogram(sft_flip,
args.out_tractogram,
bbox_valid_check=not args.keep_invalid)
else:
static_img = nib.load(args.in_native_fa)
static_data = static_img.get_fdata()
moving_img = nib.load(args.in_dsi_fa)
moving_data = moving_img.get_fdata()
# DSI-Studio flips the volume without changing the affine (I think)
# So this has to be reversed (not the same problem as above)
vox_order = get_reference_info(moving_img)[3]
flip_axis = []
if vox_order[0] == 'L':
moving_data = moving_data[::-1, :, :]
flip_axis.append('x')
if vox_order[1] == 'P':
moving_data = moving_data[:, ::-1, :]
flip_axis.append('y')
if vox_order[2] == 'I':
moving_data = moving_data[:, :, ::-1]
flip_axis.append('z')
sft_flip_back = flip_sft(sft_flip, flip_axis)
if args.load_transfo:
transfo = np.loadtxt(args.load_transfo)
else:
# Sometimes DSI studio has quite a lot of skull left
# Dipy Median Otsu does not work with FA/GFA
if args.auto_crop:
moving_data = cube_crop_data(moving_data)
static_data = cube_crop_data(static_data)
# Since DSI Studio register to AC/PC and does not save the
# transformation We must estimate the transformation, since it's
# rigid it is 'easy'
c_of_mass = transform_centers_of_mass(static_data,
static_img.affine,
moving_data,
moving_img.affine)
nbins = 32
sampling_prop = None
level_iters = [1000, 100, 10]
sigmas = [3.0, 2.0, 1.0]
factors = [3, 2, 1]
metric = MutualInformationMetric(nbins, sampling_prop)
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = RigidTransform3D()
rigid = affreg.optimize(static_data,
moving_data,
transform,
None,
static_img.affine,
moving_img.affine,
starting_affine=c_of_mass.affine)
transfo = rigid.affine
if args.save_transfo:
np.savetxt(args.save_transfo, transfo)
new_sft = transform_warp_sft(sft_flip_back,
transfo,
static_img,
inverse=True,
remove_invalid=args.remove_invalid,
cut_invalid=args.cut_invalid)
if args.cut_invalid:
new_sft, _ = cut_invalid_streamlines(new_sft)
elif args.remove_invalid:
new_sft.remove_invalid_streamlines()
save_tractogram(new_sft,
args.out_tractogram,
bbox_valid_check=not args.keep_invalid)
def dots_segmentation(tensor_image,
mask,
atlas_dir,
wm_atlas=1,
max_iter=25,
convergence_threshold=0.005,
s_I=1 / 42,
c_O=0.5,
max_angle=67.5,
save_data=False,
overwrite=False,
output_dir=None,
file_name=None):
"""DOTS segmentation
Segment major white matter tracts in diffusion tensor images using Diffusion
Oriented Tract Segmentation (DOTS) algorithm.
Parameters
----------
tensor_image: niimg
Input image containing the diffusion tensor coefficients in the
following order: volumes 0-5: D11, D22, D33, D12, D13, D23
mask: niimg
Binary brain mask image which limits computation to the defined volume.
atlas_dir: str
Path to directory where the DOTS atlas information is stored. The atlas
information should be stored in a subdirectory called 'DOTS_atlas' as
generated by nighres.data.download_DOTS_atlas().
wm_atlas: int, optional
Define which white matter atlas to use. Option 1 for 23 tracts [2]_
and option 2 for 39 tracts [1]_. (default is 1)
max_iter: int, optional
Maximum number of iterations in the conditional modes algorithm.
(default is 20)
convergence_threshold: float, optional
Threshold for when the iterated conditonal modes algorithm is considered
to have converged. Defined as the fraction of labels that change during
one step of the algorithm. (default is 0.002)
s_I: float, optional
Parameter controlling how isotropic label energies propagate to their
neighborhood. (default is 1/42)
c_O: float, optional
Weight parameter for unclassified white matter atlas prior. (default
is 1/2)
max_angle: float, optional
Maximum angle (in degrees) between principal tensor directions before
connectivity coefficient c becomes negative. Possible values between 0
and 90. (default is 67.5)
save_data: bool, optional
Save output data to file. (default is False)
overwrite: bool, optional
Overwrite existing results. (default is False)
output_dir: str, optional
Path to desired output directory, will be created if it doesn't exist.
file_name: str, optional
Desired base name for output files without file extension, suffixes
will be added.
Returns
----------
dict
Dictionary collecting outputs under the following keys
(type of output files in brackets)
* segmentation (array_like): Hard segmentation of white matter.
* posterior (array_like): POsterior probabilities of tracts.
Notes
----------
Algorithm details can be found in the references below.
References
----------
.. [1] Bazin, Pierre-Louis, et al. "Direct segmentation of the major white
matter tracts in diffusion tensor images." Neuroimage (2011)
doi: https://doi.org/10.1016/j.neuroimage.2011.06.020
.. [2] Bazin, Pierre-Louis, et al. "Efficient MRF segmentation of DTI white
matter tracts using an overlapping fiber model." Proceedings of the
International Workshop on Diffusion Modelling and Fiber Cup (2009)
"""
print('\nDOTS white matter tract segmentation')
# make sure that saving related parameters are correct
if save_data:
output_dir = _output_dir_4saving(output_dir, tensor_image)
seg_file = os.path.join(
output_dir,
_fname_4saving(module=__name__,
file_name=file_name,
rootfile=tensor_image,
suffix='dots-seg'))
proba_file = os.path.join(
output_dir,
_fname_4saving(module=__name__,
file_name=file_name,
rootfile=tensor_image,
suffix='dots-proba'))
if overwrite is False \
and os.path.isfile(seg_file) and os.path.isfile(proba_file) :
print("skip computation (use existing results)")
output = {'segmentation': seg_file, 'posterior': proba_file}
return output
# For external tools: dipy
try:
from dipy.align.transforms import AffineTransform3D
from dipy.align.imaffine import MutualInformationMetric, AffineRegistration
except ImportError:
print('Error: Dipy could not be imported, it is required' +
' in order to run DOTS segmentation. \n (aborting)')
return None
# Ignore runtime warnings that arise from trying to divide by 0/nan
# and all nan slices
np.seterr(divide='ignore', invalid='ignore')
# Define the scalar constant c_I
c_I = 1 / 2
# Define constant c_C that is used in direction coefficient calculation
c_C = 90 / max_angle
# Create an array containing the directions between neighbors
v_xy = np.zeros((3, 3, 3, 3))
for i in range(3):
for j in range(3):
for k in range(3):
if (i, j, k) == (1, 1, 1):
v_xy[i, j, k, :] = np.nan
else:
x = np.array([1, 0, 0])
y = np.array([0, 1, 0])
z = np.array([0, 0, 1])
c = np.array([1, 1, 1])
v_xy[i, j, k, :] = i * x + y * j + z * k - c
v_xy[i,j,k,:] = v_xy[i,j,k,:] / \
np.linalg.norm(v_xy[i,j,k,:])
# Load tensor image
tensor_volume = load_volume(tensor_image).get_fdata()
# Load brain mask
brain_mask = load_volume(mask).get_fdata().astype(bool)
# Get dimensions of diffusion data
xs, ys, zs, _ = tensor_volume.shape
DWI_affine = load_volume(tensor_image).affine
# Calculate diffusion tensor eigenvalues and eigenvectors
tenfit = np.zeros((xs, ys, zs, 3, 3))
tenfit[:, :, :, 0, 0] = tensor_volume[:, :, :, 0]
tenfit[:, :, :, 1, 1] = tensor_volume[:, :, :, 1]
tenfit[:, :, :, 2, 2] = tensor_volume[:, :, :, 2]
tenfit[:, :, :, 0, 1] = tensor_volume[:, :, :, 3]
tenfit[:, :, :, 1, 0] = tensor_volume[:, :, :, 3]
tenfit[:, :, :, 0, 2] = tensor_volume[:, :, :, 4]
tenfit[:, :, :, 2, 0] = tensor_volume[:, :, :, 4]
tenfit[:, :, :, 1, 2] = tensor_volume[:, :, :, 5]
tenfit[:, :, :, 2, 1] = tensor_volume[:, :, :, 5]
tenfit[np.isnan(tenfit)] = 0
evals, evecs = np.linalg.eig(tenfit)
evals, evecs = np.real(evals), np.real(evecs)
for i in range(xs):
for j in range(ys):
for k in range(zs):
idx = np.argsort(evals[i, j, k, :])[::-1]
evecs[i, j, k, :, :] = evecs[i, j, k, :, idx].T
evals[i, j, k, :] = evals[i, j, k, idx]
evals[~brain_mask] = 0
evecs[~brain_mask] = 0
# Calculate FA
R = tenfit / np.trace(tenfit, axis1=3, axis2=4)[:, :, :, np.newaxis,
np.newaxis]
FA = np.sqrt(0.5 * (3 - 1 / (np.trace(np.matmul(R, R), axis1=3, axis2=4))))
FA[np.isnan(FA)] = 0
if wm_atlas == 1:
# Use smaller atlas
# Indices are
# 0 for isotropic regions
# 1 for unclassified white matter
# 2-22 for individual tracts
# 22-73 for overlapping tracts
N_t = 23
N_o = 50
atlas_path = os.path.join(atlas_dir, 'DOTS_atlas')
fiber_p = nb.load(os.path.join(atlas_path,
'fiber_p.nii.gz')).get_fdata()
max_p = np.nanmax(fiber_p[:, :, :, 2::], axis=3)
fiber_dir = nb.load(os.path.join(atlas_path,
'fiber_dir.nii.gz')).get_fdata()
atlas_affine = nb.load(os.path.join(atlas_path,
'fiber_p.nii.gz')).affine
del_idx = [
9, 10, 13, 14, 15, 16, 21, 26, 27, 28, 29, 30, 31, 32, 33, 36, 37,
38
]
fiber_p = np.delete(fiber_p, del_idx, axis=3)
fiber_dir = np.delete(fiber_dir, del_idx, axis=4)
tract_pair_sets = tract_pair_sets_1
elif wm_atlas == 2:
# Use full atlas
# Indices are
# 0 for isotropic regions
# 1 for unclassified white matter
# 2-40 for individual tracts
# 41-224 for overlapping tracts
N_t = 41
N_o = 185
atlas_path = os.path.join(atlas_dir, 'DOTS_atlas')
fiber_p = nb.load(os.path.join(atlas_path,
'fiber_p.nii.gz')).get_fdata()
max_p = np.nanmax(fiber_p[:, :, :, 2::], axis=3)
fiber_dir = nb.load(os.path.join(atlas_path,
'fiber_dir.nii.gz')).get_fdata()
atlas_affine = nb.load(os.path.join(atlas_path,
'fiber_p.nii.gz')).affine
tract_pair_sets = tract_pair_sets_2
print('Diffusion and atlas data loaded ')
# Register atlas priors to DWI data with DiPy
print('Registering atlas priors to DWI data')
metric = MutualInformationMetric(nbins=32, sampling_proportion=None)
affreg = AffineRegistration(metric=metric,
level_iters=[10000, 1000, 100],
sigmas=[3.0, 1.0, 0.0],
factors=[4, 2, 1])
transformation = affreg.optimize(FA,
max_p,
AffineTransform3D(),
params0=None,
static_grid2world=DWI_affine,
moving_grid2world=atlas_affine,
starting_affine='mass')
reg_fiber_p = np.zeros((xs, ys, zs, fiber_p.shape[-1]))
for i in range(fiber_p.shape[-1]):
reg_fiber_p[:, :, :, i] = transformation.transform(fiber_p[:, :, :, i])
fiber_p = reg_fiber_p
reg_fiber_dir = np.zeros((xs, ys, zs, 3, fiber_dir.shape[-1]))
for i in range(fiber_dir.shape[-1]):
for j in range(3):
reg_fiber_dir[:, :, :, j,
i] = transformation.transform(fiber_dir[:, :, :, j,
i])
fiber_dir = reg_fiber_dir
fiber_p[~brain_mask, 0] = 1
fiber_p[~brain_mask, 1:] = 0
fiber_dir[~brain_mask] = 0
print('Finished registration of atlas priors to DWI data')
# Calculate diffusion type indices
print('Calculating d_T, d_O, d_I')
d_T = (evals[:, :, :, 0] - evals[:, :, :, 1]) / evals[:, :, :, 0]
d_O = (evals[:, :, :, 0] - evals[:, :, :, 2]) / evals[:, :, :, 0]
d_I = evals[:, :, :, 2] / evals[:, :, :, 0]
print('Finished calculating d_T, d_O, d_I')
# Calculate xplus and xminus
x_m_s_T = np.zeros((xs, ys, zs, 3))
x_p_s_T = np.zeros((xs, ys, zs, 3))
x_m_s_O = np.zeros((xs, ys, zs, 3))
x_p_s_O = np.zeros((xs, ys, zs, 3))
s_T_x_m = np.zeros((xs, ys, zs))
s_T_x_p = np.zeros((xs, ys, zs))
s_O_x_m = np.zeros((xs, ys, zs))
s_O_x_p = np.zeros((xs, ys, zs))
print('Calculating x^+, x^-, s_T, s_O')
for i in range(1, xs - 1):
print(str(np.round((i / xs) * 100, 0)) + ' %', end="\r")
for j in range(1, ys - 1):
for k in range(1, zs - 1):
if brain_mask[i, j, k]:
x_m_s_T[i, j, k, :], s_T_x_m[i, j, k] = _calc_x_minus_s_T(
i, j, k, evecs, v_xy)
x_p_s_T[i, j, k, :], s_T_x_p[i, j, k] = _calc_x_plus_s_T(
i, j, k, evecs, v_xy)
x_m_s_O[i, j, k, :], s_O_x_m[i, j, k] = _calc_x_minus_s_O(
i, j, k, evals, evecs, v_xy)
x_p_s_O[i, j, k, :], s_O_x_p[i, j, k] = _calc_x_plus_s_O(
i, j, k, evals, evecs, v_xy)
x_p_s_T = x_p_s_T.astype(int)
x_m_s_T = x_m_s_T.astype(int)
x_p_s_O = x_p_s_T.astype(int)
x_m_s_O = x_m_s_T.astype(int)
print('Finished calculating x^+, x^-, s_T, s_O')
# Calculate shape prior arrays
print('Calculating u_l, u_lm')
u_l = fiber_p**2 / np.nansum(fiber_p, axis=3)[:, :, :, np.newaxis]
u_lm = np.zeros((xs, ys, zs, len(tract_pair_sets)))
for idx in range(len(tract_pair_sets)):
l, m = tract_pair_sets[idx]
u_lm[:,:,:,idx] = fiber_p[:,:,:,l]*fiber_p[:,:,:,m]*(fiber_p[:,:,:,l]
+ fiber_p[:,:,:,m]) / \
np.nansum(fiber_p, axis=3)
u_l[:, :, :, 1] *= c_O # Scale by weight parameter
print('Finished calculating u_l, u_lm')
# Calculate direction coefficients
c_l = np.zeros((xs, ys, zs, N_t)) * np.nan
c_lm = np.zeros((xs, ys, zs, len(tract_pair_sets))) * np.nan
print('Calculating c_l, c_lm')
for i in range(xs):
print(str(np.round((i / xs) * 100, 0)) + ' %', end="\r")
for j in range(ys):
for k in range(zs):
for l in range(1, N_t):
if fiber_p[i, j, k, l] != 0:
c_l[i, j, k, l] = _calc_c_l(i, j, k, l, None, evecs,
fiber_dir, c_C)
for idx in range(len(tract_pair_sets)):
l, m = tract_pair_sets[idx]
if fiber_p[i, j, k, l] != 0 and fiber_p[i, j, k, m] != 0:
c_lm[i, j, k, idx] = _calc_c_l(i, j, k, l, m, evecs,
fiber_dir, c_C)
print('Finished calculating c_l, c_lm')
# Mask arrays
d_T[~brain_mask] = np.nan
d_O[~brain_mask] = np.nan
d_I[~brain_mask] = 1
fiber_p[~brain_mask, 0] = 1
fiber_p[~brain_mask, 1:] = np.nan
fiber_dir[~brain_mask] = np.nan
c_l[~brain_mask] = np.nan
c_lm[~brain_mask] = np.nan
u_l[~brain_mask] = np.nan
u_l[~brain_mask, 0] = 1
u_lm[~brain_mask] = np.nan
s_T_x_p[~brain_mask] = np.nan
s_T_x_m[~brain_mask] = np.nan
s_O_x_p[~brain_mask] = np.nan
s_O_x_m[~brain_mask] = np.nan
# Only ROIs where p != 0 are of interest
u_l[u_l == 0] = np.nan
u_lm[u_lm == 0] = np.nan
# Calculate energy based on unary term only
MRF_V1 = _calc_V1(d_T, d_O, d_I, u_l, u_lm, c_l, c_lm, c_I, fiber_p,
tract_pair_sets, N_t, N_o, brain_mask)
# Maximize U
print('Maximizing U')
curr_U = np.copy(MRF_V1)
iteration = 0
change_in_labels = np.inf
while iteration < max_iter and change_in_labels > convergence_threshold:
at = time.time()
prev_U = np.copy(curr_U)
prev_segmentation = _calc_segmentation(prev_U)
iteration += 1
print('Iteration ' + str(iteration))
curr_U = _calc_U(prev_U, d_T, d_O, d_I, u_l, u_lm, c_l, c_lm, c_I,
fiber_p, tract_pair_sets, s_I, s_T_x_p, s_T_x_m,
s_O_x_m, s_O_x_p, brain_mask, N_t, N_o, x_m_s_T,
x_p_s_T, x_m_s_O, x_p_s_O)
curr_segmentation = _calc_segmentation(curr_U)
change_in_labels = (np.nansum(prev_segmentation != curr_segmentation) /
np.nansum(brain_mask))
bt = time.time()
print('Iteration ' + str(iteration) + ' took ' + str(bt - at) +
' seconds')
print('Total U = ' + str(np.nansum(curr_U)))
print('Fraction of changed labels = ' + str(change_in_labels))
print('Finished maximizing U')
# Calculate posterior probabilities
print('Calculating posterior probabilities')
fiber_posterior = np.zeros(fiber_p.shape)
curr_U[curr_U == 0] = np.nan
for l in range(N_t):
print(str(np.round((l / N_t) * 100, 0)) + ' %', end="\r")
fiber_posterior[:, :, :, l] = calc_posterior_probability(l, curr_U, 1)
fiber_posterior[fiber_posterior == 0] = np.nan
fiber_posterior[np.isinf(fiber_posterior)] = np.nan
curr_U[np.isnan(curr_U)] = 0
print('Finished calculating posterior probabilities')
# Save results
if save_data:
save_volume(seg_file, nb.Nifti1Image(curr_segmentation, DWI_affine))
save_volume(proba_file, nb.Nifti1Image(fiber_posterior, DWI_affine))
return {'segmentation': seg_file, 'posterior': proba_file}
else:
# Return results
return {
'segmentation': curr_segmentation,
'posterior': fiber_posterior
}
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 dipy_align(static,
static_grid2world,
moving,
moving_grid2world,
prealign=None):
r''' Full rigid registration with Dipy's imaffine module
Here we implement an extra optimization heuristic: move the geometric
centers of the images to the origin. Imaffine does not do this by default
because we want to give the user as much control of the optimization
process as possible.
'''
# Bring the center of the moving image to the origin
c_moving = tuple(0.5 * np.array(moving.shape, dtype=np.float64))
c_moving = moving_grid2world.dot(c_moving + (1, ))
correction_moving = np.eye(4, dtype=np.float64)
correction_moving[:3, 3] = -1 * c_moving[:3]
centered_moving_aff = correction_moving.dot(moving_grid2world)
# Bring the center of the static image to the origin
c_static = tuple(0.5 * np.array(static.shape, dtype=np.float64))
c_static = static_grid2world.dot(c_static + (1, ))
correction_static = np.eye(4, dtype=np.float64)
correction_static[:3, 3] = -1 * c_static[:3]
centered_static_aff = correction_static.dot(static_grid2world)
dim = len(static.shape)
metric = MutualInformationMetric(nbins=32, sampling_proportion=0.3)
level_iters = [10000, 1000, 100]
affr = AffineRegistration(metric=metric, level_iters=level_iters)
affr.verbosity = VerbosityLevels.DEBUG
#metric.verbosity = VerbosityLevels.DEBUG
# Registration schedule: center-of-mass then translation, then rigid and then affine
if prealign is None:
prealign = 'mass'
transforms = ['TRANSLATION', 'RIGID', 'AFFINE']
sol = np.eye(dim + 1)
for transform_name in transforms:
transform = regtransforms[(transform_name, dim)]
print('Optimizing: %s' % (transform_name, ))
x0 = None
sol = affr.optimize(static,
moving,
transform,
x0,
centered_static_aff,
centered_moving_aff,
starting_affine=prealign)
prealign = sol.affine.copy()
# Now bring the geometric centers back to their original location
fixed = np.linalg.inv(correction_moving).dot(
sol.affine.dot(correction_static))
sol.set_affine(fixed)
sol.domain_grid2world = static_grid2world
sol.codomain_grid2world = moving_grid2world
return sol
as in ANTS). The reason why it is useful is that registration is a non-convex
optimization problem (it may have more than one local optima), which means that
it is very important to initialize as close to the solution as possible. For
example, lets start with our (previously computed) rough transformation
aligning the centers of mass of our images, and then refine it in three stages.
First look for an optimal translation. The dictionary regtransforms contains
all available transforms, we obtain one of them by providing its name and the
dimension (either 2 or 3) of the image we are working with (since we are
aligning volumes, the dimension is 3)
"""
transform = TranslationTransform3D()
params0 = None
starting_affine = c_of_mass.affine
translation = affreg.optimize(static, moving, transform, params0,
static_grid2world, moving_grid2world,
starting_affine=starting_affine)
"""
If we look at the result, we can see that this translation is much better than
simply aligning the centers of mass
"""
transformed = translation.transform(moving)
regtools.overlay_slices(static, transformed, None, 0,
"Static", "Transformed", "transformed_trans_0.png")
regtools.overlay_slices(static, transformed, None, 1,
"Static", "Transformed", "transformed_trans_1.png")
regtools.overlay_slices(static, transformed, None, 2,
"Static", "Transformed", "transformed_trans_2.png")
"""
As well as the optimization strategy:
"""
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)
transformed = translation.transform(moving)
transform = RigidTransform3D()
rigid_map = affine_reg.optimize(static,
moving,
transform,
params0,
static_affine,
moving_affine,
starting_affine=translation.affine)
transformed = rigid_map.transform(moving)
transform = AffineTransform3D()
"""
We bump up the iterations to get a more exact fit:
def ROI_registration(datapath, template, t1, b0, roi):
t1_path = datapath + '/' + t1
b0_path = datapath + '/' + b0
roi_path = datapath + '/' + roi
template_path = datapath + '/' + template
template_img, template_affine = load_nifti(template_path)
t1_img, t1_affine = load_nifti(t1_path)
b0_img, b0_affine = load_nifti(b0_path)
roi_img, roi_affine = load_nifti(roi_path)
#diff2struct affine registartion
moving = b0_img
moving_grid2world = b0_affine
static = t1_img
static_grid2world = t1_affine
affine_path = datapath + '/' + 'diff2struct_affine.mat'
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
sigmas = [3.0, 1.0, 0.0]
level_iters = [10000, 1000, 100]
factors = [4, 2, 1]
affreg_diff2struct = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = AffineTransform3D()
params0 = None
affine_diff2struct = affreg_diff2struct.optimize(static,
moving,
transform,
params0,
static_grid2world,
moving_grid2world,
starting_affine=None)
saveAffineMat(affine_diff2struct, affine_path)
# struct2standard affine registartion
moving = t1_img
moving_grid2world = t1_affine
static = template_img
static_grid2world = template_affine
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
sigmas = [3.0, 1.0, 0.0]
level_iters = [10000, 1000, 100]
factors = [4, 2, 1]
affreg_struct2standard = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = AffineTransform3D()
params0 = None
affine_struct2standard = affreg_struct2standard.optimize(
static,
moving,
transform,
params0,
static_grid2world,
moving_grid2world,
starting_affine=None)
# struct2standard SyN registartion
pre_align = affine_struct2standard.get_affine()
metric = CCMetric(3)
level_iters = [10, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(static, moving, static_grid2world,
moving_grid2world, pre_align)
warped = mapping.transform_inverse(template_img)
warped = affine_diff2struct.transform_inverse(warped)
template_diff_path = datapath + '/' + 'MNI152_diff'
save_nifti(template_diff_path, warped, b0_affine)
warped_roi = mapping.transform_inverse(roi_img)
warped_roi = affine_diff2struct.transform_inverse(warped_roi)
roi_diff_path = datapath + '/' + roi + '_diff.nii.gz'
save_nifti(roi_diff_path, warped_roi, b0_affine)
print(" Done! ")
def affine_registration(moving,
static,
moving_affine=None,
static_affine=None,
pipeline=None,
starting_affine=None,
metric='MI',
level_iters=None,
sigmas=None,
factors=None,
ret_metric=False,
**metric_kwargs):
"""
Find the affine transformation between two 3D images. Alternatively, find
the combination of several linear transformations.
Parameters
----------
moving : array, nifti image or str
Containing the data for the moving object, or full path to a nifti file
with the moving data.
static : array, nifti image or str
Containing the data for the static object, or full path to a nifti file
with the moving data.
moving_affine : 4x4 array, optional
An affine transformation associated with the moving object. Required if
data is provided as an array. If provided together with nifti/path,
will over-ride the affine that is in the nifti.
static_affine : 4x4 array, optional
An affine transformation associated with the static object. Required if
data is provided as an array. If provided together with nifti/path,
will over-ride the affine that is in the nifti.
pipeline : list of str, optional
Sequence of transforms to use in the gradual fitting. Default: gradual
fit of the full affine (executed from left to right):
``["center_of_mass", "translation", "rigid", "affine"]``
Alternatively, any other combination of the following registration
methods might be used: center_of_mass, translation, rigid,
rigid_isoscaling, rigid_scaling and affine.
starting_affine: 4x4 array, optional
Initial guess for the transformation between the spaces.
Default: identity.
metric : str, optional.
Currently only supports 'MI' for MutualInformationMetric.
level_iters : sequence, optional
AffineRegistration key-word argument: the number of iterations at each
scale of the scale space. `level_iters[0]` corresponds to the coarsest
scale, `level_iters[-1]` the finest, where n is the length of the
sequence. By default, a 3-level scale space with iterations
sequence equal to [10000, 1000, 100] will be used.
sigmas : sequence of floats, optional
AffineRegistration key-word argument: custom smoothing parameter to
build the scale space (one parameter for each scale). By default,
the sequence of sigmas will be [3, 1, 0].
factors : sequence of floats, optional
AffineRegistration key-word argument: custom scale factors to build the
scale space (one factor for each scale). By default, the sequence of
factors will be [4, 2, 1].
ret_metric : boolean, optional
Set it to True to return the value of the optimized coefficients and
the optimization quality metric.
nbins : int, optional
MutualInformationMetric key-word argument: the number of bins to be
used for computing the intensity histograms. The default is 32.
sampling_proportion : None or float in interval (0, 1], optional
MutualInformationMetric key-word argument: There are two types of
sampling: dense and sparse. Dense sampling uses all voxels for
estimating the (joint and marginal) intensity histograms, while
sparse sampling uses a subset of them. If `sampling_proportion` is
None, then dense sampling is used. If `sampling_proportion` is a
floating point value in (0,1] then sparse sampling is used,
where `sampling_proportion` specifies the proportion of voxels to
be used. The default is None (dense sampling).
Returns
-------
transformed : array with moving data resampled to the static space
after computing the affine transformation
affine : the affine 4x4 associated with the transformation.
xopt : the value of the optimized coefficients.
fopt : the value of the optimization quality metric.
Notes
-----
Performs a gradual registration between the two inputs, using a pipeline
that gradually approximates the final registration. If the final default
step (`affine`) is ommitted, the resulting affine may not have all 12
degrees of freedom adjusted.
"""
pipeline = pipeline or ["center_of_mass", "translation", "rigid", "affine"]
level_iters = level_iters or [10000, 1000, 100]
sigmas = sigmas or [3, 1, 0.0]
factors = factors or [4, 2, 1]
static, static_affine, moving, moving_affine, starting_affine = \
_handle_pipeline_inputs(moving, static,
moving_affine=moving_affine,
static_affine=static_affine,
starting_affine=starting_affine)
# Define the Affine registration object we'll use with the chosen metric.
# For now, there is only one metric (mutual information)
use_metric = affine_metric_dict[metric](**metric_kwargs)
affreg = AffineRegistration(metric=use_metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
# Convert pipeline to sanitized list of str
pipeline = list(pipeline)
for fi, func in enumerate(pipeline):
if callable(func):
for key, val in _METHOD_DICT.items():
if func is val[0]: # if they passed the callable equiv.
pipeline[fi] = func = key
break
if not isinstance(func, str) or func not in _METHOD_DICT:
raise ValueError(f'pipeline[{fi}] must be one of '
f'{list(_METHOD_DICT)}, got {repr(func)}')
if pipeline == ["center_of_mass"] and ret_metric:
raise ValueError("center of mass registration cannot return any "
"quality metric.")
# Go through the selected transformation:
for func in pipeline:
if func == "center_of_mass":
transform = transform_centers_of_mass(static, static_affine,
moving, moving_affine)
starting_affine = transform.affine
else:
transform = _METHOD_DICT[func][1]()
xform, xopt, fopt \
= affreg.optimize(static, moving, transform, None,
static_affine, moving_affine,
starting_affine=starting_affine,
ret_metric=True)
starting_affine = xform.affine
# After doing all that, resample once at the end:
affine_map = AffineMap(starting_affine, static.shape, static_affine,
moving.shape, moving_affine)
resampled = affine_map.transform(moving)
# Return the optimization metric only if requested
if ret_metric:
return resampled, starting_affine, xopt, fopt
return resampled, starting_affine
moving_grid2world = moving_hdr.affine
nbins = 50
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [2000, 200, 20]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
rigid_transform = RigidTransform3D()
params0_rigid = None
starting_affine = None
rigid = affreg.optimize(static, moving,
rigid_transform, params0_rigid,
static_grid2world, moving_grid2world,
starting_affine=starting_affine)
affine_transform = AffineTransform3D()
params0_affine = None
affine = affreg.optimize(static, moving,
affine_transform, params0_affine,
static_grid2world, moving_grid2world,
starting_affine=rigid.affine)
transformed = affine.transform(moving)
print()
def warp_syn_dipy(static_fname, moving_fname):
import os
import numpy as np
import nibabel as nb
from dipy.align.metrics import CCMetric
from dipy.align.imaffine import (transform_centers_of_mass, AffineMap,
MutualInformationMetric,
AffineRegistration)
from dipy.align.transforms import (TranslationTransform3D,
RigidTransform3D, AffineTransform3D)
from dipy.align.imwarp import (DiffeomorphicMap,
SymmetricDiffeomorphicRegistration)
from nipype.utils.filemanip import fname_presuffix
static = nb.load(static_fname)
moving = nb.load(moving_fname)
c_of_mass = transform_centers_of_mass(static.get_data(), static.affine,
moving.get_data(), moving.affine)
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [10000, 1000, 100]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = TranslationTransform3D()
params0 = None
starting_affine = c_of_mass.affine
translation = affreg.optimize(static.get_data(),
moving.get_data(),
transform,
params0,
static.affine,
moving.affine,
starting_affine=starting_affine)
transform = RigidTransform3D()
params0 = None
starting_affine = translation.affine
rigid = affreg.optimize(static.get_data(),
moving.get_data(),
transform,
params0,
static.affine,
moving.affine,
starting_affine=starting_affine)
transform = AffineTransform3D()
params0 = None
starting_affine = rigid.affine
affine = affreg.optimize(static.get_data(),
moving.get_data(),
transform,
params0,
static.affine,
moving.affine,
starting_affine=starting_affine)
metric = CCMetric(3, sigma_diff=3.)
level_iters = [25, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
starting_affine = affine.affine
mapping = sdr.optimize(static.get_data(), moving.get_data(), static.affine,
moving.affine, starting_affine)
warped_filename = os.path.abspath(
fname_presuffix(moving_fname,
newpath='./',
suffix='_warped',
use_ext=True))
warped = nb.Nifti1Image(mapping.transform(moving.get_data()),
static.affine)
warped.to_filename(warped_filename)
warp_filename = os.path.abspath(
fname_presuffix(moving_fname,
newpath='./',
suffix='_warp.npz',
use_ext=False))
np.savez(warp_filename,
prealign=mapping.prealign,
forward=mapping.forward,
backward=mapping.backward)
return warp_filename, warped_filename
def registration(diff, affine_diff, anat, affine_anat):
#Affine trasformation beetween diffuson and anatomical data
static = np.squeeze(diff)[..., 0]
static_grid2world = affine_diff
moving = anat
moving_grid2world = affine_anat
identity = np.eye(4)
affine_map = AffineMap(identity, static.shape, static_grid2world,
moving.shape, moving_grid2world)
resampled = affine_map.transform(moving)
regtools.overlay_slices(static, resampled, None, 0, "Static", "Moving",
"resampled_0.png")
regtools.overlay_slices(static, resampled, None, 1, "Static", "Moving",
"resampled_1.png")
regtools.overlay_slices(static, resampled, None, 2, "Static", "Moving",
"resampled_2.png")
c_of_mass = transform_centers_of_mass(static, static_grid2world, moving,
moving_grid2world)
transformed = c_of_mass.transform(moving)
regtools.overlay_slices(static, transformed, None, 0, "Static",
"Transformed", "transformed_com_0.png")
regtools.overlay_slices(static, transformed, None, 1, "Static",
"Transformed", "transformed_com_1.png")
regtools.overlay_slices(static, transformed, None, 2, "Static",
"Transformed", "transformed_com_2.png")
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [10000, 1000, 100]
factors = [4, 2, 1]
sigmas = [3.0, 1.0, 0.0]
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = TranslationTransform3D()
params0 = None
starting_affine = c_of_mass.affine
translation = affreg.optimize(static,
moving,
transform,
params0,
static_grid2world,
moving_grid2world,
starting_affine=starting_affine)
transformed = translation.transform(moving)
regtools.overlay_slices(static, transformed, None, 0, "Static",
"Transformed", "transformed_trans_0.png")
regtools.overlay_slices(static, transformed, None, 1, "Static",
"Transformed", "transformed_trans_1.png")
regtools.overlay_slices(static, transformed, None, 2, "Static",
"Transformed", "transformed_trans_2.png")
transform = RigidTransform3D()
params0 = None
starting_affine = translation.affine
rigid = affreg.optimize(static,
moving,
transform,
params0,
static_grid2world,
moving_grid2world,
starting_affine=starting_affine)
transformed = rigid.transform(moving)
regtools.overlay_slices(static, transformed, None, 0, "Static",
"Transformed", "transformed_rigid_0.png")
regtools.overlay_slices(static, transformed, None, 1, "Static",
"Transformed", "transformed_rigid_1.png")
regtools.overlay_slices(static, transformed, None, 2, "Static",
"Transformed", "transformed_rigid_2.png")
transform = AffineTransform3D()
params0 = None
starting_affine = rigid.affine
affine = affreg.optimize(static,
moving,
transform,
params0,
static_grid2world,
moving_grid2world,
starting_affine=starting_affine)
transformed = affine.transform(moving)
regtools.overlay_slices(static, transformed, None, 0, "Static",
"Transformed", "transformed_affine_0.png")
regtools.overlay_slices(static, transformed, None, 1, "Static",
"Transformed", "transformed_affine_1.png")
regtools.overlay_slices(static, transformed, None, 2, "Static",
"Transformed", "transformed_affine_2.png")
inverse_map = AffineMap(starting_affine, static.shape, static_grid2world,
moving.shape, moving_grid2world)
resampled_inverse = inverse_map.transform_inverse(transformed,
resample_only=True)
nib.save(nib.Nifti1Image(resampled_inverse, affine_diff),
'brain.coreg.nii.gz')
return transformed
c_of_mass = transform_centers_of_mass(t1_s, t1_s_grid2world, t1_m,
t1_m_grid2world)
nbins = 32
# prepare affine registration
affreg = AffineRegistration(metric=MutualInformationMetric(nbins, None),
level_iters=niter_affine,
sigmas=[3.0, 1.0, 0.0],
factors=[4, 2, 1])
# translation
translation = affreg.optimize(t1_s,
t1_m,
TranslationTransform3D(),
None,
t1_s_grid2world,
t1_m_grid2world,
starting_affine=c_of_mass.affine)
# rigid body transform (translation + rotation)
rigid = affreg.optimize(t1_s,
t1_m,
RigidTransform3D(),
None,
t1_s_grid2world,
t1_m_grid2world,
starting_affine=translation.affine)
# affine transform (translation + rotation + scaling)
affine = affreg.optimize(t1_s,
# Create the optimizer
level_iters = [10000, 1000, 100]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
# Translation
transform = regtransforms[('TRANSLATION', 3)]
params0 = None
starting_affine = com.affine
trans = affreg.optimize(static, moving, transform, params0,
new_aff_static, new_aff_moving,
starting_affine=starting_affine)
warped = trans.transform(moving)
rt.overlay_slices(static, warped, None, 0, "Static", "Warped", "warped_trans_0.png")
rt.overlay_slices(static, warped, None, 1, "Static", "Warped", "warped_trans_1.png")
rt.overlay_slices(static, warped, None, 2, "Static", "Warped", "warped_trans_2.png")
# Rigid
transform = regtransforms[('RIGID', 3)]
params0 = None
starting_affine = trans.affine
rigid = affreg.optimize(static, moving, transform, params0,
new_aff_static, new_aff_moving,
starting_affine=starting_affine)
# fix solution
backup = rigid.affine
def main():
# reads the tractography data in trk format
# extracts streamlines and the file header. Streamlines should be in the same coordinate system as the FA map (used later).
# input example: '/home/Example_data/tracts.trk'
tractography_file = input(
"Please, specify the file with tracts that you would like to analyse. File should be in the trk format. "
)
streams, hdr = load_trk(tractography_file) # for old DIPY version
# sft = load_trk(tractography_file, tractography_file)
# streams = sft.streamlines
streams_array = np.asarray(streams)
print('imported tractography data:' + tractography_file)
# load T1fs_conform image that operates in the same coordinates as simnibs except for the fact the center of mesh
# is located at the image center
# T1fs_conform image should be generated in advance during the head meshing procedure
# input example: fname_T1='/home/Example_data/T1fs_conform.nii.gz'
fname_T1 = input(
"Please, specify the T1fs_conform image that has been generated during head meshing procedure. "
)
data_T1, affine_T1 = load_nifti(fname_T1)
# load FA image in the same coordinates as tracts
# input example:fname_FA='/home/Example_data/DTI_FA.nii'
fname_FA = input("Please, specify the FA image. ")
data_FA, affine_FA = load_nifti(fname_FA)
print('loaded T1fs_conform.nii and FA images')
# specify the head mesh file that is used later in simnibs to simulate induced electric field
# input example:'/home/Example_data/SUBJECT_MESH.msh'
global mesh_path
mesh_path = input("Please, specify the head mesh file. ")
last_slach = max([i for i, ltr in enumerate(mesh_path) if ltr == '/']) + 1
global subject_name
subject_name = mesh_path[last_slach:-4]
# specify the directory where you would like to save your simulation results
# input example:'/home/Example_data/Output'
global out_dir
out_dir = input(
"Please, specify the directory where you would like to save your simulation results. "
)
out_dir = out_dir + '/simulation_at_pos_'
# Co-registration of T1fs_conform and FA images. Performed in 4 steps.
# Step 1. Calculation of the center of mass transform. Used later as starting transform.
c_of_mass = transform_centers_of_mass(data_T1, affine_T1, data_FA,
affine_FA)
print('calculated c_of_mass transformation')
# Step 2. Calculation of a 3D translation transform. Used in the next step as starting transform.
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [10000, 1000, 100]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = TranslationTransform3D()
params0 = None
starting_affine = c_of_mass.affine
translation = affreg.optimize(data_T1,
data_FA,
transform,
params0,
affine_T1,
affine_FA,
starting_affine=starting_affine)
print('calculated 3D translation transform')
# Step 3. Calculation of a Rigid 3D transform. Used in the next step as starting transform
transform = RigidTransform3D()
params0 = None
starting_affine = translation.affine
rigid = affreg.optimize(data_T1,
data_FA,
transform,
params0,
affine_T1,
affine_FA,
starting_affine=starting_affine)
print('calculated Rigid 3D transform')
# Step 4. Calculation of an affine transform. Used for co-registration of T1 and FA images.
transform = AffineTransform3D()
params0 = None
starting_affine = rigid.affine
affine = affreg.optimize(data_T1,
data_FA,
transform,
params0,
affine_T1,
affine_FA,
starting_affine=starting_affine)
print('calculated Affine 3D transform')
identity = np.eye(4)
inv_affine_FA = np.linalg.inv(affine_FA)
inv_affine_T1 = np.linalg.inv(affine_T1)
inv_affine = np.linalg.inv(affine.affine)
# transforming streamlines to FA space
new_streams_FA = streamline.transform_streamlines(streams, inv_affine_FA)
new_streams_FA_array = np.asarray(new_streams_FA)
T1_to_FA = np.dot(inv_affine_FA, np.dot(affine.affine, affine_T1))
FA_to_T1 = np.linalg.inv(T1_to_FA)
# transforming streamlines from FA to T1 space
new_streams_T1 = streamline.transform_streamlines(new_streams_FA, FA_to_T1)
global new_streams_T1_array
new_streams_T1_array = np.asarray(new_streams_T1)
# calculating amline derivatives along the streamlines to get the local orientation of the streamlines
global streams_array_derivative
streams_array_derivative = copy.deepcopy(new_streams_T1_array)
print('calculating amline derivatives')
for stream in range(len(new_streams_T1_array)):
my_steam = new_streams_T1_array[stream]
for t in range(len(my_steam[:, 0])):
streams_array_derivative[stream][t,
0] = my_deriv(t, my_steam[:, 0])
streams_array_derivative[stream][t,
1] = my_deriv(t, my_steam[:, 1])
streams_array_derivative[stream][t,
2] = my_deriv(t, my_steam[:, 2])
deriv_norm = np.linalg.norm(streams_array_derivative[stream][t, :])
streams_array_derivative[stream][
t, :] = streams_array_derivative[stream][t, :] / deriv_norm
# to create a torus representing a coil in an interactive window
torus = vtk.vtkParametricTorus()
torus.SetRingRadius(5)
torus.SetCrossSectionRadius(2)
torusSource = vtk.vtkParametricFunctionSource()
torusSource.SetParametricFunction(torus)
torusSource.SetScalarModeToPhase()
torusMapper = vtk.vtkPolyDataMapper()
torusMapper.SetInputConnection(torusSource.GetOutputPort())
torusMapper.SetScalarRange(0, 360)
torusActor = vtk.vtkActor()
torusActor.SetMapper(torusMapper)
torus_pos_x = 100
torus_pos_y = 129
torus_pos_z = 211
torusActor.SetPosition(torus_pos_x, torus_pos_y, torus_pos_z)
list_streams_T1 = list(new_streams_T1)
# adding one fictive bundle of length 1 with coordinates [0,0,0] to avoid some bugs with actor.line during visualization
list_streams_T1.append(np.array([0, 0, 0]))
global bundle_native
bundle_native = list_streams_T1
# generating a list of colors to visualize later the stimualtion effects
effect_max = 0.100
effect_min = -0.100
global colors
colors = [
np.random.rand(*current_streamline.shape)
for current_streamline in bundle_native
]
for my_streamline in range(len(bundle_native) - 1):
my_stream = copy.deepcopy(bundle_native[my_streamline])
for point in range(len(my_stream)):
colors[my_streamline][point] = vtkplotter.colors.colorMap(
(effect_min + effect_max) / 2,
name='jet',
vmin=effect_min,
vmax=effect_max)
colors[my_streamline + 1] = vtkplotter.colors.colorMap(effect_min,
name='jet',
vmin=effect_min,
vmax=effect_max)
# Vizualization of fibers over T1
# i_coord = 0
# j_coord = 0
# k_coord = 0
# global number_of_stimulations
number_of_stimulations = 0
actor_line_list = []
scene = window.Scene()
scene.clear()
scene.background((0.5, 0.5, 0.5))
world_coords = False
shape = data_T1.shape
lut = actor.colormap_lookup_table(scale_range=(effect_min, effect_max),
hue_range=(0.4, 1.),
saturation_range=(1, 1.))
# # the lines below is for a non-interactive demonstration run only.
# # they should remain commented unless you set "interactive" to False
# lut, colors = change_TMS_effects(torus_pos_x, torus_pos_y, torus_pos_z)
# bar = actor.scalar_bar(lut)
# bar.SetTitle("TMS effect")
# bar.SetHeight(0.3)
# bar.SetWidth(0.10)
# bar.SetPosition(0.85, 0.3)
# scene.add(bar)
actor_line_list.append(
actor.line(bundle_native,
colors,
linewidth=5,
fake_tube=True,
lookup_colormap=lut))
if not world_coords:
image_actor_z = actor.slicer(data_T1, identity)
else:
image_actor_z = actor.slicer(data_T1, identity)
slicer_opacity = 0.6
image_actor_z.opacity(slicer_opacity)
image_actor_x = image_actor_z.copy()
x_midpoint = int(np.round(shape[0] / 2))
image_actor_x.display_extent(x_midpoint, x_midpoint, 0, shape[1] - 1, 0,
shape[2] - 1)
image_actor_y = image_actor_z.copy()
y_midpoint = int(np.round(shape[1] / 2))
image_actor_y.display_extent(0, shape[0] - 1, y_midpoint, y_midpoint, 0,
shape[2] - 1)
"""
Connect the actors with the scene.
"""
scene.add(actor_line_list[0])
scene.add(image_actor_z)
scene.add(image_actor_x)
scene.add(image_actor_y)
show_m = window.ShowManager(scene, size=(1200, 900))
show_m.initialize()
"""
Create sliders to move the slices and change their opacity.
"""
line_slider_z = ui.LineSlider2D(min_value=0,
max_value=shape[2] - 1,
initial_value=shape[2] / 2,
text_template="{value:.0f}",
length=140)
line_slider_x = ui.LineSlider2D(min_value=0,
max_value=shape[0] - 1,
initial_value=shape[0] / 2,
text_template="{value:.0f}",
length=140)
line_slider_y = ui.LineSlider2D(min_value=0,
max_value=shape[1] - 1,
initial_value=shape[1] / 2,
text_template="{value:.0f}",
length=140)
opacity_slider = ui.LineSlider2D(min_value=0.0,
max_value=1.0,
initial_value=slicer_opacity,
length=140)
"""
Сallbacks for the sliders.
"""
def change_slice_z(slider):
z = int(np.round(slider.value))
image_actor_z.display_extent(0, shape[0] - 1, 0, shape[1] - 1, z, z)
def change_slice_x(slider):
x = int(np.round(slider.value))
image_actor_x.display_extent(x, x, 0, shape[1] - 1, 0, shape[2] - 1)
def change_slice_y(slider):
y = int(np.round(slider.value))
image_actor_y.display_extent(0, shape[0] - 1, y, y, 0, shape[2] - 1)
def change_opacity(slider):
slicer_opacity = slider.value
image_actor_z.opacity(slicer_opacity)
image_actor_x.opacity(slicer_opacity)
image_actor_y.opacity(slicer_opacity)
line_slider_z.on_change = change_slice_z
line_slider_x.on_change = change_slice_x
line_slider_y.on_change = change_slice_y
opacity_slider.on_change = change_opacity
"""
Сreate text labels to identify the sliders.
"""
def build_label(text):
label = ui.TextBlock2D()
label.message = text
label.font_size = 18
label.font_family = 'Arial'
label.justification = 'left'
label.bold = False
label.italic = False
label.shadow = False
label.background = (0, 0, 0)
label.color = (1, 1, 1)
return label
line_slider_label_z = build_label(text="Z Slice")
line_slider_label_x = build_label(text="X Slice")
line_slider_label_y = build_label(text="Y Slice")
opacity_slider_label = build_label(text="Opacity")
"""
Create a ``panel`` to contain the sliders and labels.
"""
panel = ui.Panel2D(size=(300, 200),
color=(1, 1, 1),
opacity=0.1,
align="right")
panel.center = (1030, 120)
panel.add_element(line_slider_label_x, (0.1, 0.75))
panel.add_element(line_slider_x, (0.38, 0.75))
panel.add_element(line_slider_label_y, (0.1, 0.55))
panel.add_element(line_slider_y, (0.38, 0.55))
panel.add_element(line_slider_label_z, (0.1, 0.35))
panel.add_element(line_slider_z, (0.38, 0.35))
panel.add_element(opacity_slider_label, (0.1, 0.15))
panel.add_element(opacity_slider, (0.38, 0.15))
scene.add(panel)
"""
Create a ``panel`` to show the value of a picked voxel.
"""
label_position = ui.TextBlock2D(text='Position:')
label_value = ui.TextBlock2D(text='Value:')
result_position = ui.TextBlock2D(text='')
result_value = ui.TextBlock2D(text='')
text2 = ui.TextBlock2D(text='Calculate')
panel_picking = ui.Panel2D(size=(250, 125),
color=(1, 1, 1),
opacity=0.1,
align="left")
panel_picking.center = (200, 120)
panel_picking.add_element(label_position, (0.1, 0.75))
panel_picking.add_element(label_value, (0.1, 0.45))
panel_picking.add_element(result_position, (0.45, 0.75))
panel_picking.add_element(result_value, (0.45, 0.45))
panel_picking.add_element(text2, (0.1, 0.15))
icon_files = []
icon_files.append(('left', read_viz_icons(fname='circle-left.png')))
button_example = ui.Button2D(icon_fnames=icon_files, size=(100, 30))
panel_picking.add_element(button_example, (0.5, 0.1))
def change_text_callback(i_ren, obj, button):
text2.message = str(i_coord) + ' ' + str(j_coord) + ' ' + str(k_coord)
torusActor.SetPosition(i_coord, j_coord, k_coord)
print(i_coord, j_coord, k_coord)
lut, colors = change_TMS_effects(i_coord, j_coord, k_coord)
scene.rm(actor_line_list[0])
actor_line_list.append(
actor.line(bundle_native,
colors,
linewidth=5,
fake_tube=True,
lookup_colormap=lut))
scene.add(actor_line_list[1])
nonlocal number_of_stimulations
global bar
if number_of_stimulations > 0:
scene.rm(bar)
else:
number_of_stimulations = number_of_stimulations + 1
bar = actor.scalar_bar(lut)
bar.SetTitle("TMS effect")
bar.SetHeight(0.3)
bar.SetWidth(0.10) # the width is set first
bar.SetPosition(0.85, 0.3)
scene.add(bar)
actor_line_list.pop(0)
i_ren.force_render()
button_example.on_left_mouse_button_clicked = change_text_callback
scene.add(panel_picking)
scene.add(torusActor)
def left_click_callback(obj, ev):
"""Get the value of the clicked voxel and show it in the panel."""
event_pos = show_m.iren.GetEventPosition()
obj.picker.Pick(event_pos[0], event_pos[1], 0, scene)
global i_coord, j_coord, k_coord
i_coord, j_coord, k_coord = obj.picker.GetPointIJK()
print(i_coord, j_coord, k_coord)
result_position.message = '({}, {}, {})'.format(
str(i_coord), str(j_coord), str(k_coord))
result_value.message = '%.8f' % data_T1[i_coord, j_coord, k_coord]
torusActor.SetPosition(i_coord, j_coord, k_coord)
image_actor_z.AddObserver('LeftButtonPressEvent', left_click_callback, 1.0)
global size
size = scene.GetSize()
def win_callback(obj, event):
global size
if size != obj.GetSize():
size_old = size
size = obj.GetSize()
size_change = [size[0] - size_old[0], 0]
panel.re_align(size_change)
show_m.initialize()
"""
Set the following variable to ``True`` to interact with the datasets in 3D.
"""
interactive = True
scene.zoom(2.0)
scene.reset_clipping_range()
scene.set_camera(position=(-642.07, 495.40, 148.49),
focal_point=(127.50, 127.50, 127.50),
view_up=(0.02, -0.01, 1.00))
if interactive:
show_m.add_window_callback(win_callback)
show_m.render()
show_m.start()
else:
window.record(scene,
out_path=out_dir + '/bundles_and_effects.png',
size=(1200, 900),
reset_camera=True)
class ImAffine:
def __init__(self):
self.nbins = 32
self.sampling_prop = None #.25
self.level_iters = [1000, 500, 250, 125]
self.factors = [8, 4, 2, 1]
self.sigmas = [3.0, 2.0, 1.0, 0.0]
self.ss_sigma_factor = None
self.verbosity = 0
self.tx_mat = None
self.params0 = None
self.options = {
'maxcor': 10,
'ftol': 1e-7,
'gtol': 1e-5,
'eps': 1e-8,
'maxiter': 1000,
'disp': True
}
# ftol: The iteration stops when (f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= ftol.
# gtol: The iteration will stop when max{|proj g_i | i = 1, ..., n} <= gtol where pg_i is the i-th component of the projected gradient.
# eps: Step size used for numerical approximation of the jacobian.
# disp: Set to True to print convergence messages.
self.method = 'L-BFGS-B'
self.metric = MutualInformationMetric(self.nbins, self.sampling_prop)
self.affmap = AffineRegistration(metric=self.metric,
level_iters=self.level_iters,
sigmas=self.sigmas,
factors=self.factors,
method=self.method,
ss_sigma_factor=self.ss_sigma_factor,
options=self.options,
verbosity=self.verbosity)
self.update_map = True
def estimate_translation2d(self, fixed, moving):
assert len(moving.shape) == len(fixed.shape)
trans = TranslationTransform2D()
if self.update_map:
self.metric = MutualInformationMetric(self.nbins,
self.sampling_prop)
self.affmap = AffineRegistration(
metric=self.metric,
level_iters=self.level_iters,
sigmas=self.sigmas,
factors=self.factors,
method=self.method,
ss_sigma_factor=self.ss_sigma_factor,
options=self.options,
verbosity=self.verbosity)
return self.affmap.optimize(fixed, moving, trans, self.params0)
def estimate_rigid2d(self, fixed, moving, tx_tr=None):
assert len(moving.shape) == len(fixed.shape)
trans = RigidTransform2D()
if self.update_map:
self.metric = MutualInformationMetric(self.nbins,
self.sampling_prop)
self.affmap = AffineRegistration(
metric=self.metric,
level_iters=self.level_iters,
sigmas=self.sigmas,
factors=self.factors,
method=self.method,
ss_sigma_factor=self.ss_sigma_factor,
options=self.options,
verbosity=self.verbosity)
if tx_tr is None:
self.update_map = False
tx_tr = self.estimate_translation2d(fixed, moving)
self.update_map = True
if isinstance(tx_tr, AffineMap):
tx_tr = tx_tr.affine
return self.affmap.optimize(fixed,
moving,
trans,
self.params0,
starting_affine=tx_tr)
def estimate_affine2d(self, fixed, moving, tx_tr=None):
assert len(moving.shape) == len(fixed.shape)
trans = AffineTransform3D()
if self.update_map:
self.metric = MutualInformationMetric(self.nbins,
self.sampling_prop)
self.affmap = AffineRegistration(
metric=self.metric,
level_iters=self.level_iters,
sigmas=self.sigmas,
factors=self.factors,
method=self.method,
ss_sigma_factor=self.ss_sigma_factor,
options=self.options,
verbosity=self.verbosity)
if tx_tr is None:
self.update_map = False
tx_tr = self.estimate_rigid2d(fixed, moving)
self.update_map = True
if isinstance(tx_tr, AffineMap):
tx_tr = tx_tr.affine
return self.affmap.optimize(fixed,
moving,
trans,
self.params0,
starting_affine=tx_tr)
def estimate_translation3d(self, fixed, moving):
assert len(moving.shape) == len(fixed.shape)
tx_tr = self.estimate_translation2d(fixed.mean(axis=0),
moving.mean(axis=0))
tx_tr = tx_tr.affine
tmp = np.eye(4)
tmp[1:, 1:] = tx_tr
trans = TranslationTransform3D()
if self.update_map:
self.metric = MutualInformationMetric(self.nbins,
self.sampling_prop)
self.affmap = AffineRegistration(
metric=self.metric,
level_iters=self.level_iters,
sigmas=self.sigmas,
factors=self.factors,
method=self.method,
ss_sigma_factor=self.ss_sigma_factor,
options=self.options,
verbosity=self.verbosity)
return self.affmap.optimize(fixed,
moving,
trans,
self.params0,
starting_affine=tmp)
def estimate_rigid3d(self, fixed, moving, tx_tr=None):
assert len(moving.shape) == len(fixed.shape)
trans = RigidTransform3D()
if self.update_map:
self.metric = MutualInformationMetric(self.nbins,
self.sampling_prop)
self.affmap = AffineRegistration(
metric=self.metric,
level_iters=self.level_iters,
sigmas=self.sigmas,
factors=self.factors,
method=self.method,
ss_sigma_factor=self.ss_sigma_factor,
options=self.options,
verbosity=self.verbosity)
if tx_tr is None:
tmp = self.estimate_rigid2d(fixed.mean(axis=0),
moving.mean(axis=0))
tmp = tmp.affine
tx_tr = np.eye(4)
tx_tr[1:, 1:] = tmp
if isinstance(tx_tr, AffineMap):
tx_tr = tx_tr.affine
return self.affmap.optimize(fixed,
moving,
trans,
self.params0,
starting_affine=tx_tr)
def estimate_rigidxy(self, fixed, moving, tx_tr=None):
assert len(moving.shape) == len(fixed.shape)
trans = TranslationTransform3D()
if self.update_map:
self.metric = MutualInformationMetric(self.nbins,
self.sampling_prop)
self.affmap = AffineRegistration(
metric=self.metric,
level_iters=self.level_iters,
sigmas=self.sigmas,
factors=self.factors,
method=self.method,
ss_sigma_factor=self.ss_sigma_factor,
options=self.options,
verbosity=self.verbosity)
if tx_tr is None:
tmp = self.estimate_rigid2d(fixed.mean(axis=0),
moving.mean(axis=0))
tmp = tmp.affine
tx_tr = np.eye(4)
tx_tr[1:, 1:] = tmp
if isinstance(tx_tr, AffineMap):
tx_tr = tx_tr.affine
trans2d = AffineMap(tx_tr,
domain_grid_shape=fixed.shape,
codomain_grid_shape=moving.shape)
moving_ = trans2d.transform(fixed)
transz = self.affmap.optimize(moving_, moving, trans, self.params0)
print(transz.affine)
tx_tr[0, 3] = transz.affine[0, 3]
return AffineMap(tx_tr,
domain_grid_shape=fixed.shape,
codomain_grid_shape=moving.shape)
def estimate_rigid_projz(self, fixed, moving, tx_tr=None):
# this returns a 3d rotation matrix
assert len(moving.shape) == len(fixed.shape)
if tx_tr is None:
tmp = self.estimate_rigid2d(fixed.mean(axis=0),
moving.mean(axis=0))
tmp = tmp.affine
tx_tr = np.eye(4)
tx_tr[1:, 1:] = tmp
else:
if isinstance(tx_tr, AffineMap):
tx_tr = tx_tr.affine
if tx_tr.shape[0] == 3:
tmp = np.eye(4)
tmp[1:, 1:] = tx_tr
tx_tr = tmp
tmp = self.estimate_rigid2d(fixed.mean(axis=0),
moving.mean(axis=0),
tx_tr=tx_tr)
tmp = tmp.affine
tx_tr = np.eye(4)
tx_tr[1:, 1:] = tmp
return AffineMap(tx_tr,
domain_grid_shape=fixed.shape,
codomain_grid_shape=moving.shape)
def estimate_affine3d(self, fixed, moving, tx_tr=None):
assert len(moving.shape) == len(fixed.shape)
trans = AffineTransform3D()
if self.update_map:
self.metric = MutualInformationMetric(self.nbins,
self.sampling_prop)
self.affmap = AffineRegistration(
metric=self.metric,
level_iters=self.level_iters,
sigmas=self.sigmas,
factors=self.factors,
method=self.method,
ss_sigma_factor=self.ss_sigma_factor,
options=self.options,
verbosity=self.verbosity)
if tx_tr is None:
tmp = self.estimate_affine2d(fixed.mean(axis=0),
moving.mean(axis=0))
tmp = tmp.affine
tx_tr = np.eye(4)
tx_tr[1:, 1:] = tmp
if isinstance(tx_tr, AffineMap):
tx_tr = tx_tr.affine()
return self.affmap.optimize(fixed,
moving,
trans,
self.params0,
starting_affine=tx_tr)
def save_affine(fname, affine_):
dio.save_pickle('affine', affine_)
def transform_affine(fname, fix):
out_ = dio.load_pickle(fname)
return out_.transform(fix)
it is very important to initialize as close to the solution as possible. For
example, lets start with our (previously computed) rough transformation
aligning the centers of mass of our images, and then refine it in three stages.
First look for an optimal translation. The dictionary regtransforms contains
all available transforms, we obtain one of them by providing its name and the
dimension (either 2 or 3) of the image we are working with (since we are
aligning volumes, the dimension is 3)
"""
transform = TranslationTransform3D()
params0 = None
starting_affine = c_of_mass.affine
translation = affreg.optimize(static,
moving,
transform,
params0,
static_grid2world,
moving_grid2world,
starting_affine=starting_affine)
"""
If we look at the result, we can see that this translation is much better than
simply aligning the centers of mass
"""
transformed = translation.transform(moving)
regtools.overlay_slices(static, transformed, None, 0, "Static", "Transformed",
"transformed_trans_0.png")
regtools.overlay_slices(static, transformed, None, 1, "Static", "Transformed",
"transformed_trans_1.png")
regtools.overlay_slices(static, transformed, None, 2, "Static", "Transformed",
"transformed_trans_2.png")
def warp_syn_dipy(static_fname, moving_fname):
import os
import numpy as np
import nibabel as nb
from dipy.align.metrics import CCMetric
from dipy.align.imaffine import (transform_centers_of_mass,
AffineMap,
MutualInformationMetric,
AffineRegistration)
from dipy.align.transforms import (TranslationTransform3D,
RigidTransform3D,
AffineTransform3D)
from dipy.align.imwarp import (DiffeomorphicMap,
SymmetricDiffeomorphicRegistration)
from nipype.utils.filemanip import fname_presuffix
static = nb.load(static_fname)
moving = nb.load(moving_fname)
c_of_mass = transform_centers_of_mass(static.get_data(), static.affine,
moving.get_data(), moving.affine)
nbins = 32
sampling_prop = None
metric = MutualInformationMetric(nbins, sampling_prop)
level_iters = [10000, 1000, 100]
sigmas = [3.0, 1.0, 0.0]
factors = [4, 2, 1]
affreg = AffineRegistration(metric=metric,
level_iters=level_iters,
sigmas=sigmas,
factors=factors)
transform = TranslationTransform3D()
params0 = None
starting_affine = c_of_mass.affine
translation = affreg.optimize(static.get_data(), moving.get_data(),
transform, params0,
static.affine, moving.affine,
starting_affine=starting_affine)
transform = RigidTransform3D()
params0 = None
starting_affine = translation.affine
rigid = affreg.optimize(static.get_data(), moving.get_data(), transform, params0,
static.affine, moving.affine,
starting_affine=starting_affine)
transform = AffineTransform3D()
params0 = None
starting_affine = rigid.affine
affine = affreg.optimize(static.get_data(), moving.get_data(), transform, params0,
static.affine, moving.affine,
starting_affine=starting_affine)
metric = CCMetric(3, sigma_diff=3.)
level_iters = [25, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
starting_affine = affine.affine
mapping = sdr.optimize(
static.get_data(), moving.get_data(),
static.affine, moving.affine,
starting_affine)
warped_filename = os.path.abspath(fname_presuffix(moving_fname, newpath='./', suffix='_warped', use_ext=True))
warped = nb.Nifti1Image(mapping.transform(moving.get_data()), static.affine)
warped.to_filename(warped_filename)
warp_filename = os.path.abspath(fname_presuffix(moving_fname, newpath='./', suffix='_warp.npz', use_ext=False))
np.savez(warp_filename,prealign=mapping.prealign,forward=mapping.forward,backward=mapping.backward)
return warp_filename, warped_filename
