d = DTdirs[di]
# Repeat simulations for the given directions
for s_i in np.arange(di * nrep, (di + 1) * nrep):
# Multi-compartmental simulations using
# Dipy's function multi_tensor
signal, sticks = multi_tensor(gtab,
mevals,
S0=100,
angles=[d, (1, 0, 0)],
fractions=fracts,
snr=snr)
DWI_simulates[fa_i, snr_i, s_i, :] = signal
# Fit all simulated signals for given set of parameters
params = nls_fit_tensor(gtab, DWI_simulates, Diso=Dwater)
fw_params[acq_i, ...] = params
# Display progress
prog = (acq_i + 1.0) / nacq * 100
time.sleep(1)
sys.stdout.write("\r%f%%" % prog)
sys.stdout.flush()
# ----------------------------------------------------------------
print('Compute and plot MSE...')
# ----------------------------------------------------------------
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(13, 10))
# Defining the figure's acquisition labels
data = img.get_data()
affine = img.get_affine()
# ---------------------------------------------------------------
print('Estimate a brain mask...')
# ---------------------------------------------------------------
maskdata, mask = median_otsu(data, 4, 2, False, vol_idx=[0, 1], dilate=1)
# ---------------------------------------------------------------
print('Fitting the free water DTI model...')
# ---------------------------------------------------------------
t0 = time.time()
fw_params = nls_fit_tensor(gtab, data, mask)
dt = time.time() - t0
print("This step took %f seconds to run" % dt)
# ----------------------------------------------------------------
print('Compute tensor statistic from the fitted parameters...')
# ----------------------------------------------------------------
evals = fw_params[..., :3]
FA = dti.fractional_anisotropy(evals)
MD = dti.mean_diffusivity(evals)
F = fw_params[..., 12]
# ----------------------------------------------------------------
print('Compute standard DTI for comparison...')
# ----------------------------------------------------------------
# Select a diffusion tensor direction
d = DTdirs[di]
# Repeat simulations for the given directions
for s_i in np.arange(di * nrep, (di+1) * nrep):
# Multi-compartmental simulations using
# Dipy's function multi_tensor
signal, sticks = multi_tensor(gtab, mevals,
S0=100,
angles=[d, (1, 0, 0)],
fractions=fracts,
snr=snr)
DWI_simulates[fa_i, snr_i, s_i, :] = signal
# Fit all simulated signals for given set of parameters
params = nls_fit_tensor(gtab, DWI_simulates, Diso=Dwater)
fw_params[acq_i, ...] = params
# Display progress
prog = (acq_i+1.0) / nacq * 100
time.sleep(1)
sys.stdout.write("\r%f%%" % prog)
sys.stdout.flush()
# ----------------------------------------------------------------
print('Compute and plot MSE...')
# ----------------------------------------------------------------
fig, axs = plt.subplots(nrows=2, ncols=3, figsize=(13, 10))
# Defining the figure's acquisition labels
d = DTdirs[di]
# Repeat each direction 100 times
for s_i in np.arange(di * nrep, (di+1) * nrep):
# Multi-compartmental simulations are done using
# Dipy's function multi_tensor
signal, sticks = multi_tensor(gtab, mevals, S0=100,
angles=[d, (1, 0, 0)],
fractions=fractions,
snr=SNRa)
rep_simulates[s_i, :] = signal
# Process NLS fitting for all simulation repetitions of
# a given pair of minimum-maximun b-value
fw_params[bmin_i, bmax_i, :, :] = nls_fit_tensor(gtab,
rep_simulates)
# Process computing progress
prog = (bmax_i*1.0)/bmax.size + (bmin_i+1.0)/(bmax.size*bmin.size)
time.sleep(1)
sys.stdout.write("\r%f%%" % prog * 100)
sys.stdout.flush()
# ----------------------------------------------------------------
print('Compute FA and f-value statistics and save results...')
# ----------------------------------------------------------------
# select computed diffusion eigenvalues
evals = fw_params[..., :3]
# Compute the tissue's diffusion tensor fractional anisotropy
for s_i in np.arange(di * nrep, (di + 1) * nrep):
# Multi-compartmental simulations are done using
# Dipy's function multi_tensor
signal, sticks = multi_tensor(gtab,
mevals,
S0=100,
angles=[d, (1, 0, 0)],
fractions=fractions,
snr=SNRa)
rep_simulates[s_i, :] = signal
# Process NLS fitting for all simulation repetitions of
# a given pair of minimum-maximun b-value
fw_params[bmin_i,
bmax_i, :, :] = nls_fit_tensor(gtab, rep_simulates)
# Process computing progress
prog = (bmax_i * 1.0) / bmax.size + (bmin_i + 1.0) / (bmax.size *
bmin.size)
time.sleep(1)
sys.stdout.write("\r%f%%" % prog * 100)
sys.stdout.flush()
# ----------------------------------------------------------------
print('Compute FA and f-value statistics and save results...')
# ----------------------------------------------------------------
# select computed diffusion eigenvalues
evals = fw_params[..., :3]
data = img.get_data()
affine = img.get_affine()
# ---------------------------------------------------------------
print('Estimate a brain mask...')
# ---------------------------------------------------------------
maskdata, mask = median_otsu(data, 4, 2, False, vol_idx=[0, 1], dilate=1)
# ---------------------------------------------------------------
print('Fitting the free water DTI model...')
# ---------------------------------------------------------------
t0 = time.time()
fw_params = nls_fit_tensor(gtab, data, mask)
dt = time.time() - t0
print("This step took %f seconds to run" % dt)
# ----------------------------------------------------------------
print('Compute tensor statistic from the fitted parameters...')
# ----------------------------------------------------------------
evals = fw_params[..., :3]
FA = dti.fractional_anisotropy(evals)
MD = dti.mean_diffusivity(evals)
F = fw_params[..., 12]
# ----------------------------------------------------------------
print('Compute standard DTI for comparison...')
# ----------------------------------------------------------------