def test_sensitivity_extraction_2D(self):
""" This test ensures that the output of the non cartesian kspace
extraction is same a that of mimicked cartesian extraction in 2D
"""
_mask = np.ones((self.N, self.N))
_samples = convert_mask_to_locations(_mask)
fourier_op = NonCartesianFFT(samples=_samples, shape=(self.N, self.N))
Img = (np.random.randn(self.num_channel, self.N, self.N) +
1j * np.random.randn(self.num_channel, self.N, self.N))
F_img = np.asarray(
[fourier_op.op(Img[i]) for i in np.arange(self.num_channel)])
Smaps_gridding, SOS_Smaps = get_Smaps(k_space=F_img,
img_shape=(self.N, self.N),
samples=_samples,
thresh=(0.4, 0.4),
mode='gridding',
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
n_cpu=1)
Smaps_NFFT, SOS_Smaps = get_Smaps(k_space=F_img,
img_shape=(self.N, self.N),
thresh=(0.4, 0.4),
samples=_samples,
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
mode='NFFT')
np.testing.assert_allclose(Smaps_gridding, Smaps_NFFT)
def test_NFFT_2D(self):
"""Test the adjoint operator for the 2D non-Cartesian Fourier
transform, with density compensator set to 1, to vet the code
path, the test is unchanged otherwise
"""
for num_channels in self.num_channels:
print("Testing with num_channels=" + str(num_channels))
for i in range(self.max_iter):
_mask = np.random.randint(2, size=(self.N, self.N))
_samples = convert_mask_to_locations(_mask)
print("Process NFFT in 2D test '{0}'...", i)
fourier_op = NonCartesianFFT(samples=_samples,
shape=(self.N, self.N),
n_coils=num_channels,
implementation='cpu',
density_comp=np.ones(
(num_channels,
_samples.shape[0])))
Img = np.random.randn(num_channels, self.N, self.N) + \
1j * np.random.randn(num_channels, self.N, self.N)
f = np.random.randn(num_channels, _samples.shape[0]) + \
1j * np.random.randn(num_channels, _samples.shape[0])
f_p = fourier_op.op(Img)
I_p = fourier_op.adj_op(f)
x_d = np.vdot(Img, I_p)
x_ad = np.vdot(f_p, f)
np.testing.assert_allclose(x_d, x_ad, rtol=1e-10)
print(" NFFT in 2D adjoint test passes")
def test_NUFFT_3D(self):
"""Test the adjoint operator for the 3D non-Cartesian Fourier transform
on GPU
"""
for num_channels in self.num_channels:
for platform in self.platforms:
_mask = np.random.randint(2, size=(self.N, self.N, self.N))
_samples = convert_mask_to_locations(_mask)
fourier_op_dir = NonCartesianFFT(samples=_samples,
shape=(self.N, self.N,
self.N),
implementation=platform,
n_coils=num_channels)
Img = (np.random.randn(num_channels, self.N, self.N, self.N) +
1j * np.random.randn(num_channels, self.N, self.N,
self.N))
f = (np.random.randn(num_channels, _samples.shape[0]) +
1j * np.random.randn(num_channels, _samples.shape[0]))
f_p = fourier_op_dir.op(Img)
I_p = fourier_op_dir.adj_op(f)
x_d = np.vdot(Img, I_p)
x_ad = np.vdot(f_p, f)
np.testing.assert_allclose(x_d, x_ad, rtol=1e-5)
print("NFFT in 3D adjoint test passes on GPU with"
"num_channels = " + str(num_channels) + " on "
"platform " + platform)
def test_similarity_stack_3D_nfft(self):
"""Test the similarity of stacked implementation of Fourier transform
to that of NFFT
"""
for channel in self.num_channels:
print("Testing with num_channels=" + str(channel))
for N in [16, 32]:
# Nz is the number of slices, this would check both N=Nz
# and N!=Nz
Nz = 16
_mask = np.random.randint(2, size=(N, N))
# Generate random mask along z
sampling_z = np.random.randint(2, size=Nz)
_mask3D = np.zeros((N, N, Nz))
for idx, acq_z in enumerate(sampling_z):
_mask3D[:, :, idx] = _mask * acq_z
_samples = convert_mask_to_locations(_mask3D)
print("Process Stack-3D similarity with NFFT for N=" + str(N))
fourier_op_stack = Stacked3DNFFT(kspace_loc=_samples,
shape=(N, N, Nz),
implementation='cpu',
n_coils=channel)
fourier_op_nfft = NonCartesianFFT(samples=_samples,
shape=(N, N, Nz),
implementation='cpu',
n_coils=channel)
Img = np.random.random((channel, N, N, Nz)) + \
1j * np.random.random((channel, N, N, Nz))
f = np.random.random((channel, _samples.shape[0])) + \
1j * np.random.random((channel, _samples.shape[0]))
start_time = time.time()
stack_f_p = fourier_op_stack.op(Img)
stack_I_p = fourier_op_stack.adj_op(f)
stack_runtime = time.time() - start_time
start_time = time.time()
nfft_f_p = fourier_op_nfft.op(Img)
nfft_I_p = fourier_op_nfft.adj_op(f)
np.testing.assert_allclose(stack_f_p, nfft_f_p, rtol=1e-9)
np.testing.assert_allclose(stack_I_p, nfft_I_p, rtol=1e-9)
nfft_runtime = time.time() - start_time
print("For N=" + str(N) + " Speedup = " +
str(nfft_runtime / stack_runtime))
print("Stacked FFT in 3D adjoint test passes")
def generate_test_NFFT(self, shape, field_scale):
""" Factorized code to test 2D and 3D wrapped NFFT with
different homogeneous B0 field shifts at constant time.
"""
for L, i, n_coils in product(self.L, range(self.max_iter),
self.n_coils):
mask = np.random.randint(2, size=shape)
samples = convert_mask_to_locations(mask)
samples = samples[:samples.shape[0]
- (samples.shape[0] % shape[0])]
field_shift = field_scale * np.random.randint(-150, 150)
field_map = field_shift * np.ones(shape)
# Prepare reference and wrapper operators
fourier_op = NonCartesianFFT(
samples=samples,
shape=shape,
n_coils=n_coils,
implementation='cpu',
density_comp=np.ones((n_coils, samples.shape[0]))
)
wrapper_op = ORCFFTWrapper(fourier_op, field_map=field_map,
time_vec=np.ones(shape[0]),
mask=np.ones(shape),
num_interpolators=L,
n_bins=self.n_bins)
# Forward operator
img = np.squeeze(np.random.randn(n_coils, *shape) \
+ 1j * np.random.randn(n_coils, *shape))
ksp_fft = fourier_op.op(img)
ksp_wra = wrapper_op.op(img * np.exp(-2j * np.pi * field_shift))
np.testing.assert_allclose(ksp_fft, ksp_wra, rtol=self.rtol)
# Adjoint operator
ksp = np.squeeze(np.random.randn(n_coils, samples.shape[0]) \
+ 1j * np.random.randn(n_coils, samples.shape[0]))
img_fft = fourier_op.adj_op(ksp)
img_wra = wrapper_op.adj_op(ksp * np.exp(2j * np.pi * field_shift))
np.testing.assert_allclose(img_fft, img_wra, rtol=self.rtol)
def test_NFFT_3D(self):
"""Test the adjoint operator for the 3D non-Cartesian Fourier transform
"""
for num_channels in self.num_channels:
print("Testing with num_channels=" + str(num_channels))
for i in range(self.max_iter):
_mask = np.random.randint(2, size=(self.N, self.N, self.N))
_samples = convert_mask_to_locations(_mask)
print("Process NFFT test in 3D '{0}'...", i)
fourier_op = NonCartesianFFT(samples=_samples,
shape=(self.N, self.N, self.N),
n_coils=num_channels,
implementation='cpu')
Img = np.random.randn(num_channels, self.N, self.N, self.N) + \
1j * np.random.randn(num_channels, self.N, self.N, self.N)
f = np.random.randn(num_channels, _samples.shape[0]) + \
1j * np.random.randn(num_channels, _samples.shape[0])
f_p = fourier_op.op(Img)
I_p = fourier_op.adj_op(f)
x_d = np.vdot(Img, I_p)
x_ad = np.vdot(f_p, f)
np.testing.assert_allclose(x_d, x_ad, rtol=1e-10)
print(" NFFT in 3D adjoint test passes")
def test_sensitivity_extraction_2D(self):
""" This test ensures that the output of the non cartesian kspace
extraction is same a that of mimicked cartesian extraction in 2D
"""
mask = np.ones((self.N, self.N))
samples = convert_mask_to_locations(mask)
fourier_op = NonCartesianFFT(samples=samples, shape=(self.N, self.N))
Img = (np.random.randn(self.num_channel, self.N, self.N) +
1j * np.random.randn(self.num_channel, self.N, self.N))
F_img = np.asarray(
[fourier_op.op(Img[i]) for i in np.arange(self.num_channel)])
Smaps_gridding, SOS_Smaps = get_Smaps(k_space=F_img,
img_shape=(self.N, self.N),
samples=samples,
thresh=(0.4, 0.4),
mode='gridding',
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
n_cpu=1)
Smaps_NFFT_dc, SOS_Smaps_dc = get_Smaps(k_space=F_img,
img_shape=(self.N, self.N),
thresh=(0.4, 0.4),
samples=samples,
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
mode='NFFT',
density_comp=np.ones(
samples.shape[0]))
Smaps_NFFT, SOS_Smaps = get_Smaps(
k_space=F_img,
img_shape=(self.N, self.N),
thresh=(0.4, 0.4),
samples=samples,
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
mode='NFFT',
)
Smaps_hann_NFFT, SOS_Smaps = get_Smaps(
k_space=F_img,
img_shape=(self.N, self.N),
thresh=0.4,
samples=samples,
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
window_fun="Hann",
mode='NFFT',
)
Smaps_hann_gridding, SOS_Smaps = get_Smaps(
k_space=F_img,
img_shape=(self.N, self.N),
thresh=0.4,
samples=samples,
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
window_fun="Hann",
mode='gridding',
)
Smaps_hamming_NFFT, SOS_Smaps = get_Smaps(
k_space=F_img,
img_shape=(self.N, self.N),
thresh=0.4,
samples=samples,
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
window_fun="Hamming",
mode='NFFT',
)
Smaps_hamming_gridding, SOS_Smaps = get_Smaps(
k_space=F_img,
img_shape=(self.N, self.N),
thresh=0.4,
samples=samples,
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
window_fun="Hamming",
mode='gridding',
)
Smaps_call_gridding, SOS_Smaps = get_Smaps(
k_space=F_img,
img_shape=(self.N, self.N),
thresh=0.4,
samples=samples,
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
window_fun=lambda x: 1,
mode='gridding',
)
Smaps_call_NFFT, SOS_Smaps = get_Smaps(
k_space=F_img,
img_shape=(self.N, self.N),
thresh=0.4,
samples=samples,
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
window_fun=lambda x: 1,
mode='NFFT',
)
np.testing.assert_allclose(Smaps_gridding, Smaps_NFFT_dc)
np.testing.assert_allclose(Smaps_gridding, Smaps_NFFT)
np.testing.assert_allclose(Smaps_hann_gridding, Smaps_hann_NFFT)
np.testing.assert_allclose(Smaps_hamming_gridding, Smaps_hamming_NFFT)
np.testing.assert_allclose(Smaps_call_gridding, Smaps_call_NFFT)
# Test that we raise assert for bad mode
np.testing.assert_raises(ValueError,
get_Smaps,
k_space=F_img,
img_shape=(self.N, self.N),
thresh=(0.4, 0.4),
samples=samples,
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
mode='test')
# Test that we raise assert for bad window
np.testing.assert_raises(
ValueError,
get_Smaps,
k_space=F_img,
img_shape=(self.N, self.N),
thresh=(0.4, 0.4),
samples=samples,
min_samples=(-0.5, -0.5),
max_samples=(0.5, 0.5),
window_fun='test',
mode='gridding',
)
# Get the locations of the kspace samples and the associated observations
fourier_op = NonCartesianFFT(
samples=kspace_loc,
shape=image.shape,
implementation='gpuNUFFT',
)
fourier_op_density_comp = NonCartesianFFT(
samples=kspace_loc,
shape=image.shape,
implementation='gpuNUFFT',
density_comp=density_comp
)
# Get the kspace data retrospectively. Note that this can be done with
# `fourier_op_density_comp` as the forward operator is the same
kspace_obs = fourier_op.op(image.data)
# Simple adjoint
image_rec0 = pysap.Image(data=np.abs(fourier_op.adj_op(kspace_obs)))
# image_rec0.show()
base_ssim = ssim(image_rec0, image)
print('The SSIM from Adjoint is : ' + str(base_ssim))
# Density Compensation adjoint:
# This preconditions k-space giving a result closer to inverse
image_rec1 = pysap.Image(data=np.abs(
fourier_op_density_comp.adj_op(kspace_obs))
)
# image_rec1.show()
new_ssim = ssim(image_rec1, image)
print('The SSIM from Density '
def test_gridsearch_single_channel(self):
"""Test Gridsearch script in mri.scripts for
single channel reconstruction this is a test of sanity
and not if the reconstruction is right.
"""
image = get_sample_data('2d-mri')
mask = np.ones(image.shape)
kspace_loc = convert_mask_to_locations(mask)
fourier_op = NonCartesianFFT(samples=kspace_loc, shape=image.shape)
kspace_data = fourier_op.op(image.data)
# Define the keyword dictionaries based on convention
metrics = {
'ssim': {
'metric': ssim,
'mapping': {
'x_new': 'test',
'y_new': None
},
'cst_kwargs': {
'ref': image,
'mask': None
},
'early_stopping': True,
},
}
linear_params = {
'init_class': WaveletN,
'kwargs': {
'wavelet_name': 'sym8',
'nb_scale': 4,
}
}
regularizer_params = {
'init_class': SparseThreshold,
'kwargs': {
'linear': Identity(),
'weights': [0, 1e-5],
}
}
optimizer_params = {
# Just following convention
'kwargs': {
'optimization_alg': 'fista',
'num_iterations': 10,
'metrics': metrics,
}
}
# Call the launch grid function and obtain results
raw_results, test_cases, key_names, best_idx = launch_grid(
kspace_data=kspace_data,
fourier_op=fourier_op,
linear_params=linear_params,
regularizer_params=regularizer_params,
optimizer_params=optimizer_params,
reconstructor_kwargs={'gradient_formulation': 'synthesis'},
reconstructor_class=SingleChannelReconstructor,
compare_metric_details={'metric': 'ssim'},
n_jobs=self.n_jobs,
verbose=1,
)
# In this test we dont undersample the kspace so the
# reconstruction is indeed with mu=0, ie best_idx=0
np.testing.assert_equal(best_idx, 0)
np.testing.assert_allclose(
raw_results[best_idx][0],
image,
atol=1e-7,
)
import numpy as np
from mri.operators import NonCartesianFFT
traj = np.load('/volatile/temp_traj.npy')
fourier = NonCartesianFFT(traj, (384, 384, 208),
'gpuNUFFT',
n_coils=20,
smaps=np.ones((20, 384, 384, 208)),
osf=2)
for i in range(10):
print(i)
K = fourier.op(np.zeros((384, 384, 208)))
I = fourier.adj_op(K)
# mask.show()
#############################################################################
# Generate the kspace
# -------------------
#
# From the 2D brain slice and the acquisition mask, we retrospectively
# undersample the k-space using a non cartesian acquisition mask
# We then grid the kspace to get the gridded solution as a baseline
# Get the kspace observation values for the kspace locations
fourier_op = NonCartesianFFT(samples=kspace_loc,
shape=image.shape,
n_coils=cartesian_ref_image.shape[0],
implementation='gpuNUFFT')
kspace_obs = fourier_op.op(cartesian_ref_image)
# Gridded solution
grid_space = np.linspace(-0.5, 0.5, num=image.shape[0])
grid2D = np.meshgrid(grid_space, grid_space)
grid_soln = np.asarray([
gridded_inverse_fourier_transform_nd(kspace_loc, kspace_obs_ch,
tuple(grid2D), 'linear')
for kspace_obs_ch in kspace_obs
])
image_rec0 = pysap.Image(data=np.sqrt(np.sum(np.abs(grid_soln)**2, axis=0)))
# image_rec0.show()
base_ssim = ssim(image_rec0, image)
print('The Base SSIM is : ' + str(base_ssim))
# Obtain SMaps