def from_kspace_to_nc_kspace_and_traj(images, kspaces):
if acq_type == 'radial':
traj = get_radial_trajectory(image_size, **acq_kwargs)
elif acq_type == 'cartesian':
traj = get_debugging_cartesian_trajectory()
elif acq_type == 'spiral':
traj = get_spiral_trajectory(image_size, **acq_kwargs)
else:
raise NotImplementedError(f'{acq_type} dataset not implemented yet.')
interpob = nfft_ob._extract_nufft_interpob()
nufftob_back = kbnufft_adjoint(interpob, multiprocessing=multiprocessing)
nufftob_forw = kbnufft_forward(interpob, multiprocessing=multiprocessing)
dcomp = calculate_density_compensator(
interpob,
nufftob_forw,
nufftob_back,
traj[0],
)
traj = tf.repeat(traj, tf.shape(images)[0], axis=0)
orig_image_channels = tf_ortho_ifft2d(kspaces)
nc_kspace = nufft(nfft_ob, orig_image_channels, traj, image_size, multiprocessing=multiprocessing)
nc_kspace_scaled = nc_kspace * scale_factor
images_scaled = images * scale_factor
images_channeled = images_scaled[..., None]
nc_kspaces_channeled = nc_kspace_scaled[..., None]
orig_shape = tf.ones([tf.shape(kspaces)[0]], dtype=tf.int32) * tf.shape(kspaces)[-1]
dcomp = tf.ones([tf.shape(kspaces)[0], tf.shape(dcomp)[0]], dtype=dcomp.dtype) * dcomp[None, :]
extra_args = (orig_shape, dcomp)
smaps = non_cartesian_extract_smaps(nc_kspace, traj, dcomp, nufftob_back, orig_shape)
return (nc_kspaces_channeled, traj, smaps, extra_args), images_channeled
def from_volume_to_nc_kspace_and_traj(volume):
if acq_type == 'radial_stacks':
traj = get_stacks_of_radial_trajectory(volume_size, **acq_kwargs)
elif acq_type == 'spiral_stacks':
traj = get_stacks_of_spiral_trajectory(volume_size, **acq_kwargs)
else:
raise NotImplementedError(
f'{acq_type} dataset not implemented yet.')
if compute_dcomp:
interpob = nfft_ob._extract_nufft_interpob()
nufftob_back = kbnufft_adjoint(interpob)
nufftob_forw = kbnufft_forward(interpob)
dcomp = calculate_density_compensator(
interpob,
nufftob_forw,
nufftob_back,
traj[0],
)
# need to add batch and coil dimension to the volume
nc_kspace = nufft(nfft_ob, volume[None, None, ...], traj, volume_size)
nc_kspace_scaled = nc_kspace * scale_factor
volume_scaled = tf.abs(volume * scale_factor)
volume_channeled = volume_scaled[None, ..., None]
nc_kspaces_channeled = nc_kspace_scaled[..., None]
orig_shape = tf.shape(volume)[None, ...]
extra_args = (orig_shape, )
if compute_dcomp:
dcomp = tf.ones([1, tf.shape(dcomp)[0]],
dtype=dcomp.dtype) * dcomp[None, :]
extra_args += (dcomp, )
return (nc_kspaces_channeled, traj, extra_args), volume_channeled
def test_density_compensators_tf():
# This is a simple test to ensure that the code works only!
# We still dont have a method to test if the results are correct
ktraj, nufft_ob, torch_forward, torch_backward = setup()
interpob = nufft_ob._extract_nufft_interpob()
tf_ktraj = tf.convert_to_tensor(ktraj)
nufftob_back = kbnufft_adjoint(interpob)
nufftob_forw = kbnufft_forward(interpob)
tf_dcomp = calculate_density_compensator(interpob, nufftob_forw,
nufftob_back, tf_ktraj)
def __init__(self, **kwargs):
super().__init__(**kwargs)
interpob = nufft_ob._extract_nufft_interpob()
self.nufftob_back = kbnufft_adjoint(interpob,
multiprocessing=False)
self.nufftob_forw = kbnufft_forward(interpob,
multiprocessing=False)
if acq_type == 'radial':
self.traj = get_radial_trajectory(image_size, af=af)
elif acq_type == 'cartesian':
self.traj = get_debugging_cartesian_trajectory()
elif acq_type == 'spiral':
self.traj = get_spiral_trajectory(image_size, af=af)
else:
raise NotImplementedError(
f'{acq_type} dataset not implemented yet.')
self.dcomp = calculate_density_compensator(
interpob,
self.nufftob_forw,
self.nufftob_back,
self.traj[0],
)
def preprocessing(self, image, kspace):
traj = self.generate_trajectory()
interpob = self.nufft_obj._extract_nufft_interpob()
nufftob_forw = kbnufft_forward(interpob, multiprocessing=True)
nufftob_back = kbnufft_adjoint(interpob, multiprocessing=True)
if self.dcomp:
dcomp = calculate_density_compensator(
interpob,
nufftob_forw,
nufftob_back,
traj[0],
)
traj = tf.repeat(traj, tf.shape(image)[0], axis=0)
orig_image_channels = ortho_ifft2d(kspace)
if self.crop_image_data:
image = adjust_image_size(image, self.image_size)
nc_kspace = nufft(self.nufft_obj, orig_image_channels, traj, self.image_size, multicoil=self.multicoil)
nc_kspace, image = scale_tensors(nc_kspace, image, scale_factor=self.scale_factor)
image = image[..., None]
nc_kspaces_channeled = nc_kspace[..., None]
orig_shape = tf.ones([tf.shape(kspace)[0]], dtype=tf.int32) * self.image_size[-1]
if not self.crop_image_data:
output_shape = tf.shape(image)[1:][None, :]
output_shape = tf.tile(output_shape, [tf.shape(image)[0], 1])
extra_args = (orig_shape,)
if self.dcomp:
dcomp = tf.ones(
[tf.shape(kspace)[0], tf.shape(dcomp)[0]],
dtype=dcomp.dtype,
) * dcomp[None, :]
extra_args += (dcomp,)
model_inputs = (nc_kspaces_channeled, traj)
if self.multicoil:
smaps = non_cartesian_extract_smaps(nc_kspace, traj, dcomp, nufftob_back, self.image_size)
model_inputs += (smaps,)
if not self.crop_image_data:
model_inputs += (output_shape,)
model_inputs += (extra_args,)
return model_inputs, image
def from_kspace_to_nc_kspace_and_traj(images, kspaces):
if acq_type == 'radial':
traj = get_radial_trajectory(image_size, **acq_kwargs)
elif acq_type == 'cartesian':
traj = get_debugging_cartesian_trajectory()
elif acq_type == 'spiral':
traj = get_spiral_trajectory(image_size, **acq_kwargs)
else:
raise NotImplementedError(f'{acq_type} dataset not implemented yet.')
if compute_dcomp:
interpob = nfft_ob._extract_nufft_interpob()
nufftob_back = kbnufft_adjoint(interpob, multiprocessing=True)
nufftob_forw = kbnufft_forward(interpob, multiprocessing=True)
dcomp = calculate_density_compensator(
interpob,
nufftob_forw,
nufftob_back,
traj[0],
)
traj = tf.repeat(traj, tf.shape(images)[0], axis=0)
orig_image = tf_unmasked_adj_op(kspaces[..., None])
nc_kspace = nufft(nfft_ob, orig_image[:, None, ..., 0], traj, image_size)
nc_kspace_scaled = nc_kspace * scale_factor
images_scaled = images * scale_factor
# Here implement gridding
if gridding:
pi = tf.constant(math.pi)
def tf_grid_nc(nc_kspace_traj):
nc_kspace, traj = nc_kspace_traj
X_grid, Y_grid = tf.meshgrid(
tf.range(-pi, pi, 2*pi / image_size[0]),
tf.range(-pi, pi, 2*pi / image_size[1]),
)
nc_kspace = tf.numpy_function(
grid_non_cartesian,
[traj, nc_kspace, X_grid, Y_grid],
tf.complex64,
)
return nc_kspace
nc_kspace_scaled = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(
tf_grid_nc,
(nc_kspace_scaled, traj),
fn_output_signature=tf.complex64,
parallel_iterations=multiprocessing.cpu_count(),
))
nc_kspace_scaled.set_shape([
None,
*image_size,
])
traj = tf.ones_like(nc_kspace_scaled)
images_channeled = images_scaled[..., None]
nc_kspaces_channeled = nc_kspace_scaled[..., None]
orig_shape = tf.ones([tf.shape(kspaces)[0]], dtype=tf.int32) * tf.shape(kspaces)[-1]
extra_args = (orig_shape,)
if compute_dcomp:
dcomp = tf.ones([tf.shape(kspaces)[0], tf.shape(dcomp)[0]], dtype=dcomp.dtype) * dcomp[None, :]
extra_args += (dcomp,)
if gridding:
return (nc_kspaces_channeled, traj), images_channeled
else:
return (nc_kspaces_channeled, traj, extra_args), images_channeled