def run(self):
'''
'''
#pdb.set_trace()
# define polarity based on the warp requested
self.polarity = 1.
if self.warp:
self.polarity = -1.
# Evaluate spherical harmonics on a smaller grid
dv, g_xyz2rcs = self.eval_spharm_grid(self.vendor, self.coeffs)
# transform RAS-coordinates into LAI-coordinates
m_ras2lai = np.array([[-1.0, 0.0, 0.0, 0.0], [0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, -1.0, 0.0], [0.0, 0.0, 0.0, 1.0]],
dtype=np.float)
m_rcs2lai = np.dot(m_ras2lai, self.m_rcs2ras)
m_rcs2lai_nohalf = m_rcs2lai[:, :]
# indices of image volume
'''
nr, nc, ns = self.vol.shape[:3]
vc3, vr3, vs3 = utils.meshgrid(np.arange(nr), np.arange(nc), np.arange(ns), dtype=np.float32)
vrcs = CV(x=vr3, y=vc3, z=vs3)
vxyz = utils.transform_coordinates(vrcs, m_rcs2lai)
'''
# account for half-voxel shift in R and C directions
halfvox = np.zeros((4, 4))
halfvox[0, 3] = m_rcs2lai[0, 0] / 2.0
halfvox[1, 3] = m_rcs2lai[1, 1] / 2.0
#m_rcs2lai = m_rcs2lai + halfvox
# extract rotational and scaling parts of the transformation matrix
# ignore the translation part
r_rcs2lai = np.eye(4, 4)
r_rcs2lai[:3, :3] = m_rcs2lai[:3, :3]
# Jon Polimeni:
# Since partial derivatives in Jacobian Matrix are differences
# that depend on the ordering of the elements of the 3D array, the
# coordinates may increase in the opposite direction from the array
# indices, in which case the differential element should be negative.
# The differentials can be determined by mapping a vector of 1s
# through rotation and scaling, where any mirror
# will impose a negation
ones = CV(1., 1., 1.)
dxyz = utils.transform_coordinates_old(ones, r_rcs2lai)
# do the nonlinear unwarp
if self.vendor == 'siemens':
self.out, self.vjacout = self.non_linear_unwarp_siemens(
self.vol.shape, dv, dxyz, m_rcs2lai, m_rcs2lai_nohalf,
g_xyz2rcs)
def run(self):
'''
'''
#pdb.set_trace()
# define polarity based on the warp requested
self.polarity = 1.
if self.warp:
self.polarity = -1.
# Evaluate spherical harmonics on a smaller grid
dv, g_xyz2rcs = self.eval_spharm_grid(self.vendor, self.coeffs)
# transform RAS-coordinates into LAI-coordinates
m_ras2lai = np.array([[-1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, -1.0, 0.0],
[0.0, 0.0, 0.0, 1.0]], dtype=np.float)
m_rcs2lai = np.dot(m_ras2lai, self.m_rcs2ras)
m_rcs2lai_nohalf = m_rcs2lai[:, :]
# indices of image volume
'''
nr, nc, ns = self.vol.shape[:3]
vc3, vr3, vs3 = utils.meshgrid(np.arange(nr), np.arange(nc), np.arange(ns), dtype=np.float32)
vrcs = CV(x=vr3, y=vc3, z=vs3)
vxyz = utils.transform_coordinates(vrcs, m_rcs2lai)
'''
# account for half-voxel shift in R and C directions
halfvox = np.zeros((4, 4))
halfvox[0, 3] = m_rcs2lai[0, 0] / 2.0
halfvox[1, 3] = m_rcs2lai[1, 1] / 2.0
#m_rcs2lai = m_rcs2lai + halfvox
# extract rotational and scaling parts of the transformation matrix
# ignore the translation part
r_rcs2lai = np.eye(4, 4)
r_rcs2lai[:3, :3] = m_rcs2lai[:3, :3]
# Jon Polimeni:
# Since partial derivatives in Jacobian Matrix are differences
# that depend on the ordering of the elements of the 3D array, the
# coordinates may increase in the opposite direction from the array
# indices, in which case the differential element should be negative.
# The differentials can be determined by mapping a vector of 1s
# through rotation and scaling, where any mirror
# will impose a negation
ones = CV(1., 1., 1.)
dxyz = utils.transform_coordinates_old(ones, r_rcs2lai)
# do the nonlinear unwarp
if self.vendor == 'siemens':
self.out, self.vjacout = self.non_linear_unwarp_siemens(self.vol.shape, dv, dxyz,
m_rcs2lai, m_rcs2lai_nohalf, g_xyz2rcs)
def run(self):
'''
'''
#pdb.set_trace()
# define polarity based on the warp requested
self.polarity = 1.
if self.warp:
self.polarity = -1.
# transform RAS-coordinates into LAI-coordinates
m_ras2lai = np.array([[-1.0, 0.0, 0.0, 0.0],
[0.0, 1.0, 0.0, 0.0],
[0.0, 0.0, -1.0, 0.0],
[0.0, 0.0, 0.0, 1.0]], dtype=np.float)
m_rcs2lai = np.dot(m_ras2lai, self.m_rcs2ras)
# indices of image volume
nr, nc, ns = self.vol.shape[:3]
vc3, vr3, vs3 = utils.meshgrid(np.arange(nr), np.arange(nc), np.arange(ns), dtype=np.float32)
vrcs = CV(x=vr3, y=vc3, z=vs3)
vxyz = utils.transform_coordinates(vrcs, m_rcs2lai)
# account for half-voxel shift in R and C directions
halfvox = np.zeros((4, 4))
halfvox[0, 3] = m_rcs2lai[0, 0] / 2.0
halfvox[1, 3] = m_rcs2lai[1, 1] / 2.0
m_rcs2lai = m_rcs2lai + halfvox
# extract rotational and scaling parts of the transformation matrix
# ignore the translation part
r_rcs2lai = np.eye(4, 4)
r_rcs2lai[:3, :3] = m_rcs2lai[:3, :3]
# Jon Polimeni:
# Since partial derivatives in Jacobian Matrix are differences
# that depend on the ordering of the elements of the 3D array, the
# coordinates may increase in the opposite direction from the array
# indices, in which case the differential element should be negative.
# The differentials can be determined by mapping a vector of 1s
# through rotation and scaling, where any mirror
# will impose a negation
ones = CV(1., 1., 1.)
dxyz = utils.transform_coordinates_old(ones, r_rcs2lai)
# # convert image vol coordinates from RAS to LAI
# vxyz = utils.transform_coordinates(vrcs, m_rcs2lai)
# # compute new coordinates and the jacobian determinant
# # TODO still not clear about what to return
# self.out, self.vjacmult_lps = self.non_linear_unwarp(vxyz, dxyz,
# m_rcs2lai)
# for each slice
'''
for slice in xrange(ns):
sys.stdout.flush()
if (slice + 1) % 10 == 0:
print(slice + 1),
else:
print('.'),
# we are doing it slice by slice
vs = np.ones(vr.shape) * slice
vrcs2d = CV(vr, vc, slice)
# rcs2lai
vxyz2d = utils.transform_coordinates(vrcs2d, m_rcs2lai)
# compute new coordinates and the jacobian determinant
moddv, modxyz = eval_spherical_harmonics(self.coeffs, self.vendor, vxyz2d)
dvx[..., slice] = moddv.x
dvy[..., slice] = moddv.y
dvz[..., slice] = moddv.z
'''
print
# Evaluate spherical harmonics on a smaller grid
dv, grcs, g_xyz2rcs = self.eval_spharm_grid(self.vendor, self.coeffs)
# do the nonlinear unwarp
self.out, self.vjacmult_lps = self.non_linear_unwarp(vxyz, grcs, dv, dxyz,
m_rcs2lai, g_xyz2rcs)
