cyldir_2))) * 180 / np.pi
# sig_fasc1 = util.rotate_atom(dic_sing_fasc[:, ID_1],
# sch_mat_b0, refdir, cyldir_1,
# WM_DIFF, S0_fasc[:, ID_1])
sig_fasc1 = dic_sing_fasc[:, ID_1]
sig_fasc2 = util.rotate_atom(dic_sing_fasc[:, ID_2], sch_mat_b0,
refdir, cyldir_2, WM_DIFF,
S0_fasc[:, ID_2])
DW_image = nu1 * sig_fasc1 + nu2 * sig_fasc2
# Simulate noise and MRI scanner scaling
DW_image_store[:, i] = DW_image
DW_image_noisy = util.gen_SoS_MRI(DW_image, sigma_g, num_coils)
DW_image_noisy = M0 * DW_image_noisy
DW_noisy_store[:, i] = DW_image_noisy
# Estimate peak directions from noisy signal
peaks = get_csd_peaks(DW_image_noisy, sch_mat_b0, num_fasc)
# Analyze result of CSD (just for displaying progress). You only need to
# store the groundtruth and estimated directions to compute all these
# metrics afterwards.
num_pk_detected = np.sum(np.sum(np.abs(peaks), axis=1) > 0)
if num_pk_detected < num_fasc:
# There should always be at least 1 detected peak because the ODF
# always has a max.
def gen_test_data(num_samples, use_noise=False, SNR_min=80, SNR_max=100):
#SNR_min = 80
#SNR_max = 100
SNR_dist = 'uniform' # 'uniform' or 'triangular'
starttime = time.time()
# Prepare memory
IDs = np.zeros((num_samples, num_fasc), dtype=np.int32)
nus = np.zeros((num_samples, num_fasc))
SNRs = np.zeros(num_samples)
DW_image_store = np.zeros((552, num_samples))
DW_noisy_store = np.zeros((552, num_samples))
orientations = np.zeros((num_samples, num_fasc, 3))
dic_compile = np.zeros((num_samples, num_mris, num_fasc * num_atoms),
dtype=np.float64)
dictionary = np.zeros((num_mris, num_fasc * num_atoms), dtype=np.float64)
dictionary[:, :num_atoms] = dic_sing_fasc #first direction fixed
for i in range(num_samples):
if i % 100 == 0:
print(i)
nu1 = nu_min + (nu_max - nu_min) * np.random.rand()
nu2 = 1 - nu1
ID_1 = np.random.randint(0, num_atoms)
ID_2 = np.random.randint(0, num_atoms)
if SNR_dist == 'triangular':
SNR = np.random.triangular(SNR_min, SNR_min, SNR_max, 1)
elif SNR_dist == 'uniform':
SNR = np.random.uniform(SNR_min, SNR_max, 1)
else:
raise ValueError("Unknown SNR distribution %s" % SNR_dist)
sigma_g = S0_max / SNR
# First fascicle direction fixed, second fascicle rotated on the fly
cyldir_1 = refdir
cyldir_2 = refdir.copy()
while np.dot(refdir, cyldir_2) > np.cos(crossangle_min):
cyldir_2 = np.random.randn(3)
norm_2 = np.sqrt(np.sum(cyldir_2**2))
if norm_2 < 1e-11:
cyldir_2 = refdir
else:
cyldir_2 = cyldir_2 / norm_2
dic_sing_fasc_2 = util.rotate_atom(dic_sing_fasc, sch_mat_b0, refdir,
cyldir_2, WM_DIFF, S0_fasc)
dictionary[:, num_atoms:] = dic_sing_fasc_2
dic_compile[i, :, :] = dictionary
# Assemble synthetic DWI
DW_image = (nu1 * dic_sing_fasc[:, ID_1] +
nu2 * dic_sing_fasc_2[:, ID_2])
# Simulate noise and MRI scanner scaling
DW_image_store[:, i] = DW_image
DW_image_noisy = util.gen_SoS_MRI(DW_image, sigma_g, num_coils)
#DW_image_noisy = M0 * DW_image_noisy
DW_noisy_store[:, i] = DW_image_noisy
# Store
IDs[i, :] = np.array([ID_1, ID_2])
nus[i, :] = np.array([nu1, nu2])
time_elapsed = time.time() - starttime
return DW_image_store, DW_noisy_store, dic_compile, time_elapsed, sch_mat_b0, IDs, nus