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 = NFFT(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_extract_k_space_center_3D(self):
""" This test ensures that the output of the non cartesian kspace
extraction is same a that of mimicked cartesian extraction in 3D
"""
_mask = np.ones((self.N, self.N, self.Nz))
_samples = convert_mask_to_locations(_mask)
Img = (np.random.randn(self.num_channel, self.N, self.N, self.Nz) +
1j * np.random.randn(self.num_channel, self.N, self.N, self.Nz))
Nby2_percent = self.N * self.percent / 2
Nzby2_percent = self.Nz * self.percent / 2
low = int(self.N / 2 - Nby2_percent)
high = int(self.N / 2 + Nby2_percent + 1)
lowz = int(self.Nz / 2 - Nzby2_percent)
highz = int(self.Nz / 2 + Nzby2_percent + 1)
center_Img = Img[:, low:high, low:high, lowz:highz]
thresh = self.percent * 0.5
data_thresholded, samples_thresholded = \
extract_k_space_center_and_locations(
data_values=np.reshape(Img, (self.num_channel,
self.N * self.N * self.Nz)),
samples_locations=_samples,
thr=(thresh, thresh, thresh),
img_shape=(self.N, self.N, self.Nz))
np.testing.assert_allclose(center_Img.reshape(data_thresholded.shape),
data_thresholded)
def test_sampling_converters(self):
"""Test the adjoint operator for the 2D non-Cartesian Fourier transform
"""
for i in range(self.max_iter):
print("Process test convert mask to samples test '{0}'...", i)
Nx = numpy.random.randint(8, 512)
Ny = numpy.random.randint(8, 512)
mask = numpy.random.randint(2, size=(Nx, Ny))
samples = convert_mask_to_locations(mask)
recovered_mask = convert_locations_to_mask(samples, (Nx, Ny))
self.assertEqual(mask.all(), recovered_mask.all())
mismatch = 0. + (numpy.mean(numpy.allclose(mask, recovered_mask)))
print(" mismatch = ", mismatch)
print(" Test convert mask to samples and it's adjoint passes for",
" the 2D cases")
def test_sampling_converters_3D(self):
"""Test the adjoint operator for the 3D non-Cartesian Fourier
transform
"""
for i in range(self.max_iter):
print("Process test convert mask to samples test '{0}'...", i)
Nx = numpy.random.randint(8, 512)
Ny = numpy.random.randint(8, 512)
Nz = numpy.random.randint(8, 512)
mask = numpy.random.randint(2, size=(Nx, Ny, Nz))
samples = convert_mask_to_locations(mask)
recovered_mask = convert_locations_to_mask(
samples, (Nx, Ny, Nz))
self.assertEqual(mask.all(), recovered_mask.all())
mismatch = 0. + (numpy.mean(
numpy.allclose(mask, recovered_mask)))
print(" mismatch = ", mismatch)
print(" Test convert mask to samples and it's adjoint passes for",
" the 3D cases")
def test_NFFT_3D(self):
"""Test the adjoint operator for the 3D non-Cartesian Fourier transform
"""
for i in range(self.max_iter):
_mask = numpy.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_dir = NFFT(samples=_samples,
shape=(self.N, self.N, self.N))
fourier_op_adj = NFFT(samples=_samples,
shape=(self.N, self.N, self.N))
Img = numpy.random.randn(self.N, self.N, self.N) + \
1j * numpy.random.randn(self.N, self.N, self.N)
f = numpy.random.randn(_samples.shape[0], 1) + \
1j * numpy.random.randn(_samples.shape[0], 1)
f_p = fourier_op_dir.op(Img)
I_p = fourier_op_adj.adj_op(f)
x_d = numpy.dot(Img.flatten(), numpy.conj(I_p).flatten())
x_ad = numpy.dot(f_p.flatten(), numpy.conj(f).flatten())
self.assertTrue(numpy.isclose(x_d, x_ad, rtol=1e-10))
mismatch = (1. - numpy.mean(
numpy.isclose(x_d, x_ad,
rtol=1e-10)))
print(" mismatch = ", mismatch)
print(" NFFT in 3D adjoint test passes")
def test_FFT2(self):
"""Test the adjoint operator for the 2D non-Cartesian Fourier transform
"""
for i in range(self.max_iter):
_mask = numpy.random.randint(2, size=(self.N, self.N))
_samples = convert_mask_to_locations(_mask)
print("Process FFT2 test '{0}'...", i)
fourier_op_dir = FFT2(samples=_samples, shape=(self.N, self.N))
fourier_op_adj = FFT2(samples=_samples, shape=(self.N, self.N))
Img = (numpy.random.randn(self.N, self.N) +
1j * numpy.random.randn(self.N, self.N))
f = (numpy.random.randn(self.N, self.N) +
1j * numpy.random.randn(self.N, self.N))
f_p = fourier_op_dir.op(Img)
I_p = fourier_op_adj.adj_op(f)
x_d = numpy.vdot(Img, I_p)
x_ad = numpy.vdot(f_p, f)
numpy.testing.assert_allclose(x_d, x_ad, rtol=1e-10)
print(" FFT2 adjoint test passes")
def test_extract_k_space_center_2D(self):
""" Ensure that the extracted k-space center is right"""
_mask = np.ones((self.N, self.N))
_samples = convert_mask_to_locations(_mask)
Img = (np.random.randn(self.num_channel, self.N, self.N) +
1j * np.random.randn(self.num_channel, self.N, self.N))
Nby2_percent = self.N * self.percent / 2
low = int(self.N / 2 - Nby2_percent)
high = int(self.N / 2 + Nby2_percent + 1)
center_Img = Img[:, low:high, low:high]
thresh = self.percent * 0.5
data_thresholded, samples_thresholded = \
extract_k_space_center_and_locations(
data_values=np.reshape(Img, (self.num_channel,
self.N * self.N)),
samples_locations=_samples,
thr=(thresh, thresh),
img_shape=(self.N, self.N))
np.testing.assert_allclose(center_Img.reshape(data_thresholded.shape),
data_thresholded)