def exampleDipy():
# example obtained from: http://nipy.org/dipy/examples_built/syn_registration_2d.html
import ssl
if hasattr(ssl, '_create_unverified_context'):
ssl._create_default_https_context = ssl._create_unverified_context
from dipy.data import fetch_stanford_hardi, read_stanford_hardi
fetch_stanford_hardi()
nib_stanford, gtab_stanford = read_stanford_hardi()
stanford_b0 = np.squeeze(nib_stanford.get_data())[..., 0]
from dipy.data.fetcher import fetch_syn_data, read_syn_data
fetch_syn_data()
nib_syn_t1, nib_syn_b0 = read_syn_data()
syn_b0 = np.array(nib_syn_b0.get_data())
from dipy.segment.mask import median_otsu
stanford_b0_masked, stanford_b0_mask = median_otsu(stanford_b0, 4, 4)
syn_b0_masked, syn_b0_mask = median_otsu(syn_b0, 4, 4)
static = stanford_b0_masked
static_affine = nib_stanford.affine
moving = syn_b0_masked
moving_affine = nib_syn_b0.affine
pre_align = np.array(
[[1.02783543e+00, -4.83019053e-02, -6.07735639e-02, -2.57654118e+00],
[4.34051706e-03, 9.41918267e-01, -2.66525861e-01, 3.23579799e+01],
[5.34288908e-02, 2.90262026e-01, 9.80820307e-01, -1.46216651e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])
from dipy.align.imaffine import AffineMap
affine_map = AffineMap(pre_align,
static.shape, static_affine,
moving.shape, moving_affine)
resampled = affine_map.transform(moving)
metric = CCMetric(3)
level_iters = [10, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(static, moving, static_affine, moving_affine,
pre_align)
warped_moving = mapping.transform(moving)
for slice in range(41 - 12, 41 + 13):
regtools.overlay_slices(static, resampled, slice, 1, 'Static',
'Pre Moving',
'GIFexample1/' + str(slice) + 'T1pre.png')
regtools.overlay_slices(static, warped_moving, slice, 1, 'Static',
'Post moving',
'GIFexample1/' + str(slice) + 'T1post.png')
AffineTransform3D)
"""
Let's fetch two b0 volumes, the static image will be the b0 from the Stanford
HARDI dataset
"""
fetch_stanford_hardi()
nib_stanford, gtab_stanford = read_stanford_hardi()
static = np.squeeze(nib_stanford.get_data())[..., 0]
static_grid2world = nib_stanford.affine
"""
Now the moving image
"""
fetch_syn_data()
nib_syn_t1, nib_syn_b0 = read_syn_data()
moving = np.array(nib_syn_b0.get_data())
moving_grid2world = nib_syn_b0.affine
"""
We can see that the images are far from aligned by drawing one on top of
the other. The images don't even have the same number of voxels, so in order
to draw one on top of the other we need to resample the moving image on a grid
of the same dimensions as the static image, we can do this by "transforming"
the moving image using an identity transform
"""
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",
"""
Let's fetch two b0 volumes, the static image will be the b0 from the Stanford
HARDI dataset
"""
fetch_stanford_hardi()
nib_stanford, gtab_stanford = read_stanford_hardi()
static = np.squeeze(nib_stanford.get_data())[..., 0]
static_grid2world = nib_stanford.affine
"""
Now the moving image
"""
fetch_syn_data()
nib_syn_t1, nib_syn_b0 = read_syn_data()
moving = np.array(nib_syn_b0.get_data())
moving_grid2world = nib_syn_b0.affine
"""
We can see that the images are far from aligned by drawing one on top of
the other. The images don't even have the same number of voxels, so in order
to draw one on top of the other we need to resample the moving image on a grid
of the same dimensions as the static image, we can do this by "transforming"
the moving image using an identity transform
"""
identity = np.eye(4)
affine_map = AffineMap(identity,
static.shape, static_grid2world,
moving.shape, moving_grid2world)
#get the current images from the metric
wmoving = sdr.metric.moving_image
wstatic = sdr.metric.static_image
#draw the images on top of each other with different colors
regtools.overlay_images(wmoving, wstatic, 'Warped moving', 'Overlay', 'Warped static')
"""
Now we are ready to configure and run the registration. First load the data
"""
from dipy.data.fetcher import fetch_syn_data, read_syn_data
from dipy.segment.mask import median_otsu
fetch_syn_data()
t1, b0 = read_syn_data()
data = np.array(b0.get_data(), dtype=np.float64)
"""
We first remove the skull from the b0 volume
"""
b0_mask, mask = median_otsu(data, median_radius=4, numpass=4)
"""
And select two slices to try the 2D registration
"""
static = b0_mask[:, :, 40]
moving = b0_mask[:, :, 38]
#get the current images from the metric
wmoving = sdr.metric.moving_image
wstatic = sdr.metric.static_image
#draw the images on top of each other with different colors
regtools.overlay_images(wmoving, wstatic, 'Warped moving', 'Overlay', 'Warped static')
"""
Now we are ready to configure and run the registration. First load the data
"""
from dipy.data.fetcher import fetch_syn_data, read_syn_data
from dipy.segment.mask import median_otsu
fetch_syn_data()
t1, b0 = read_syn_data()
data = np.array(b0.get_data(), dtype=np.float64)
"""
We first remove the skull from the b0 volume
"""
b0_mask, mask = median_otsu(data, 4, 4)
"""
And select two slices to try the 2D registration
"""
static = b0_mask[:, :, 40]
moving = b0_mask[:, :, 38]
