def test_shore_fitting_e0():
gtab = get_gtab_taiwan_dsi()
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angl = [(0, 0), (60, 0)]
S, sticks = MultiTensor(gtab, mevals, S0=100.0, angles=angl,
fractions=[50, 50], snr=None)
radial_order = 8
zeta = 700
lambdaN = 1e-12
lambdaL = 1e-12
asm = ShoreModel(gtab, radial_order=radial_order,
zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
asmfit = asm.fit(S)
assert_almost_equal(compute_e0(asmfit), 1)
asm = ShoreModel(gtab, radial_order=radial_order,
zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL,
constrain_e0=True)
asmfit = asm.fit(S)
assert_almost_equal(compute_e0(asmfit), 1.)
def fit_data(data, bvals, bvecs, model_type='3D-SHORE', calculate_peak_image=False):
""" Fits the defined model to the input dMRI and returns an MITK compliant ODF image.
"""
# create dipy Sphere
sphere = get_mitk_sphere()
odf = None
model = None
gtab = gradient_table(bvals, bvecs)
# fit selected model
if model_type == '3D-SHORE':
print('Fitting 3D-SHORE')
radial_order = 6
zeta = 700
lambdaN = 1e-8
lambdaL = 1e-8
model = ShoreModel(gtab, radial_order=radial_order, zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
asmfit = model.fit(data)
odf = asmfit.odf(sphere)
elif model_type == 'CSA-QBALL':
print('Fitting CSA-QBALL')
model = CsaOdfModel(gtab, 4)
odf = model.fit(data).odf(sphere)
odf = np.clip(odf, 0, np.max(odf, -1)[..., None])
elif model_type == 'SFM':
print('Fitting SFM')
response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
model = sfm.SparseFascicleModel(gtab, sphere=sphere,
l1_ratio=0.5, alpha=0.001,
response=response[0])
odf = model.fit(data).odf(sphere)
elif model_type == 'CSD':
print('Fitting CSD')
response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=0.7)
model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
odf = model.fit(data).odf(sphere)
else:
raise ValueError('Model type not supported. Available models: 3D-SHORE, CSA-QBALL, SFM, CSD')
print('Preparing ODF image')
# swap axes to obtain MITK compliant format.
# odf = odf.swapaxes(3, 2)
# odf = odf.swapaxes(2, 1)
# odf = odf.swapaxes(1, 0)
odf_image = sitk.Image([data.shape[2], data.shape[1], data.shape[0]], sitk.sitkVectorFloat32, len(sphere.vertices))
for x in range(data.shape[2]):
for y in range(data.shape[1]):
for z in range(data.shape[0]):
odf_image.SetPixel(x,y,z, odf[z,y,x,:])
# if not calculate_peak_image:
return odf_image, None
def test_shore_odf():
gtab = get_isbi2013_2shell_gtab()
# load repulsion 724 sphere
sphere = default_sphere
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
data, golden_directions = sticks_and_ball(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
asm = ShoreModel(gtab,
radial_order=6,
zeta=700,
lambdaN=1e-8,
lambdaL=1e-8)
# repulsion724
asmfit = asm.fit(data)
odf = asmfit.odf(sphere)
odf_sh = asmfit.odf_sh()
odf_from_sh = sh_to_sf(odf_sh, sphere, 6, basis_type=None, legacy=True)
npt.assert_almost_equal(odf, odf_from_sh, 10)
expected_phi = shore_matrix(radial_order=6, zeta=700, gtab=gtab)
npt.assert_array_almost_equal(np.dot(expected_phi, asmfit.shore_coeff),
asmfit.fitted_signal())
directions, _, _ = peak_directions(odf, sphere, .35, 25)
npt.assert_equal(len(directions), 2)
npt.assert_almost_equal(angular_similarity(directions, golden_directions),
2, 1)
# 5 subdivisions
odf = asmfit.odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
npt.assert_equal(len(directions), 2)
npt.assert_almost_equal(angular_similarity(directions, golden_directions),
2, 1)
sb_dummies = sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
asmfit = asm.fit(data)
odf = asmfit.odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
if len(directions) <= 3:
npt.assert_equal(len(directions), len(golden_directions))
if len(directions) > 3:
npt.assert_equal(gfa(odf) < 0.1, True)
def test_shore_odf():
gtab = get_isbi2013_2shell_gtab()
# load symmetric 724 sphere
sphere = get_sphere('symmetric724')
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
data, golden_directions = SticksAndBall(gtab,
d=0.0015,
S0=100,
angles=[(0, 0), (90, 0)],
fractions=[50, 50],
snr=None)
asm = ShoreModel(gtab,
radial_order=6,
zeta=700,
lambdaN=1e-8,
lambdaL=1e-8)
# symmetric724
asmfit = asm.fit(data)
odf = asmfit.odf(sphere)
odf_sh = asmfit.odf_sh()
odf_from_sh = sh_to_sf(odf_sh, sphere, 6, basis_type=None)
assert_almost_equal(odf, odf_from_sh, 10)
directions, _, _ = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
# 5 subdivisions
odf = asmfit.odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(angular_similarity(directions, golden_directions), 2,
1)
sb_dummies = sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
asmfit = asm.fit(data)
odf = asmfit.odf(sphere2)
directions, _, _ = peak_directions(odf, sphere2, .35, 25)
if len(directions) <= 3:
assert_equal(len(directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(odf) < 0.1, True)
def test_shore_fitting_constrain_e0():
asm = ShoreModel(data.gtab, radial_order=data.radial_order,
zeta=data.zeta, lambdaN=data.lambdaN,
lambdaL=data.lambdaL,
constrain_e0=True)
asmfit = asm.fit(data.S)
assert_almost_equal(compute_e0(asmfit), 1)
def shore_odf(gtab, data, affine, mask, sphere):
radial_order = 6
zeta = 700
lambdaN = 1e-8
lambdaL = 1e-8
model = ShoreModel(gtab, radial_order=radial_order, zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
return model.fit(data).odf(sphere)
def test_shore_fitting_constrain_e0():
asm = ShoreModel(data.gtab, radial_order=data.radial_order,
zeta=data.zeta, lambdaN=data.lambdaN,
lambdaL=data.lambdaL,
constrain_e0=True)
asmfit = asm.fit(data.S)
npt.assert_almost_equal(compute_e0(asmfit), 1)
def dMRI2ODF_DTI(PATH):
'''
Input the dMRI data
return the ODF
'''
print(PATH)
if os.path.exists(PATH + 'processed_data.npz'):
return None
dMRI_path = PATH + 'data.nii.gz'
mask_path = PATH + 'nodif_brain_mask.nii.gz'
dMRI_img = nib.load(dMRI_path)
dMRI_data = dMRI_img.get_fdata()
mask_img = nib.load(mask_path)
mask = mask_img.get_fdata()
########## subsample ##########
# in the main paper, to train the full 3D brain
# We downsample the data into around 32x32x32
# If not downsample, it can process the full-size brain image
# but cannot fit into GPU memory
dMRI_data = dMRI_data[::4, ::4, ::4, ...]
mask = mask[::4, ::4, ::4, ...]
bval = PATH + "bvals"
bvec = PATH + "bvecs"
radial_order = 6
zeta = 700
lambdaN = 1e-8
lambdaL = 1e-8
###### process the ODF data ######
# size is 32x32x32x(362)
gtab = gradient_table(bvals=bval, bvecs=bvec)
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
asmfit = asm.fit(dMRI_data, mask=mask)
sphere = get_sphere('symmetric362')
dMRI_odf = asmfit.odf(sphere)
dMRI_odf[dMRI_odf <= 0] = 0 # remove the numerical issue
###### process the DTI data ######
# size is 32x32x32x(3x3)
tenmodel = dti.TensorModel(gtab)
dMRI_dti = tenmodel.fit(dMRI_data, mask)
dMRI_dti = dMRI_dti.quadratic_form
name = PATH.split('/')[2] # here, might be affected by the path
# change the [2] here for the correct index
np.savez(PATH + 'processed_data.npz',
DTI=dMRI_dti,
ODF=dMRI_odf,
mask=mask,
name=name)
return None
def test_shore_odf():
gtab = get_isbi2013_2shell_gtab()
# load symmetric 724 sphere
sphere = get_sphere('symmetric724')
# load icosahedron sphere
sphere2 = create_unit_sphere(5)
data, golden_directions = SticksAndBall(gtab, d=0.0015,
S0=100, angles=[(0, 0), (90, 0)],
fractions=[50, 50], snr=None)
asm = ShoreModel(gtab, radial_order=6,
zeta=700, lambdaN=1e-8, lambdaL=1e-8)
# symmetric724
asmfit = asm.fit(data)
odf = asmfit.odf(sphere)
odf_sh = asmfit.odf_sh()
odf_from_sh = sh_to_sf(odf_sh, sphere, 6, basis_type=None)
assert_almost_equal(odf, odf_from_sh, 10)
directions, _ , _ = peak_directions(odf, sphere, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(
angular_similarity(directions, golden_directions), 2, 1)
# 5 subdivisions
odf = asmfit.odf(sphere2)
directions, _ , _ = peak_directions(odf, sphere2, .35, 25)
assert_equal(len(directions), 2)
assert_almost_equal(
angular_similarity(directions, golden_directions), 2, 1)
sb_dummies = sticks_and_ball_dummies(gtab)
for sbd in sb_dummies:
data, golden_directions = sb_dummies[sbd]
asmfit = asm.fit(data)
odf = asmfit.odf(sphere2)
directions, _ , _ = peak_directions(odf, sphere2, .35, 25)
if len(directions) <= 3:
assert_equal(len(directions), len(golden_directions))
if len(directions) > 3:
assert_equal(gfa(odf) < 0.1, True)
def test_multivox_shore():
gtab = get_3shell_gtab()
data = np.random.random([20, 30, 1, gtab.gradients.shape[0]])
radial_order = 4
zeta = 700
asm = ShoreModel(gtab, radial_order=radial_order, zeta=zeta, lambdaN=1e-8, lambdaL=1e-8)
asmfit = asm.fit(data)
c_shore=asmfit.shore_coeff
assert_equal(c_shore.shape[0:3], data.shape[0:3])
assert_equal(np.alltrue(np.isreal(c_shore)), True)
def test_shore_fitting_no_constrain_e0():
asm = ShoreModel(data.gtab,
radial_order=data.radial_order,
zeta=data.zeta,
lambdaN=data.lambdaN,
lambdaL=data.lambdaL)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
asmfit = asm.fit(data.S)
npt.assert_almost_equal(compute_e0(asmfit), 1)
def shore_scalarmaps(data, gtab, outpath_fig, verbose=None):
#bvecs[1:] = (bvecs[1:] /
# np.sqrt(np.sum(bvecs[1:] * bvecs[1:], axis=1))[:, None])
#gtab = gradient_table(bvals, bvecs)
if verbose:
print('data.shape (%d, %d, %d, %d)' % data.shape)
asm = ShoreModel(gtab)
#Let’s just use only one slice only from the data.
dataslice = data[30:70, 20:80, data.shape[2] // 2]
#Fit the signal with the model and calculate the SHORE coefficients.
asmfit = asm.fit(dataslice)
#Calculate the analytical RTOP on the signal that corresponds to the integral of the signal.
if verbose:
print('Calculating... rtop_signal')
rtop_signal = asmfit.rtop_signal()
#Now we calculate the analytical RTOP on the propagator, that corresponds to its central value.
if verbose:
print('Calculating... rtop_pdf')
rtop_pdf = asmfit.rtop_pdf()
#In theory, these two measures must be equal, to show that we calculate the mean square error on this two measures.
mse = np.sum((rtop_signal - rtop_pdf)**2) / rtop_signal.size
if verbose:
print("MSE = %f" % mse)
MSE = 0.000000
#Let’s calculate the analytical mean square displacement on the propagator.
if verbose:
print('Calculating... msd')
msd = asmfit.msd()
#Show the maps and save them to a file.
fig = plt.figure(figsize=(6, 6))
ax1 = fig.add_subplot(2, 2, 1, title='rtop_signal')
ax1.set_axis_off()
ind = ax1.imshow(rtop_signal.T, interpolation='nearest', origin='lower')
plt.colorbar(ind)
ax2 = fig.add_subplot(2, 2, 2, title='rtop_pdf')
ax2.set_axis_off()
ind = ax2.imshow(rtop_pdf.T, interpolation='nearest', origin='lower')
plt.colorbar(ind)
ax3 = fig.add_subplot(2, 2, 3, title='msd')
ax3.set_axis_off()
ind = ax3.imshow(msd.T, interpolation='nearest', origin='lower', vmin=0)
plt.colorbar(ind)
print("about to save")
plt.savefig(outpath_fig)
print("save done")
def test_shore_positive_constrain():
asm = ShoreModel(data.gtab,
radial_order=data.radial_order,
zeta=data.zeta,
lambdaN=data.lambdaN,
lambdaL=data.lambdaL,
constrain_e0=True,
positive_constraint=True,
pos_grid=11,
pos_radius=20e-03)
asmfit = asm.fit(data.S)
eap = asmfit.pdf_grid(11, 20e-03)
assert_almost_equal(eap[eap < 0].sum(), 0, 3)
def test_shore_positive_constrain():
asm = ShoreModel(data.gtab,
radial_order=data.radial_order,
zeta=data.zeta,
lambdaN=data.lambdaN,
lambdaL=data.lambdaL,
constrain_e0=True,
positive_constraint=True,
pos_grid=11,
pos_radius=20e-03)
asmfit = asm.fit(data.S)
eap = asmfit.pdf_grid(11, 20e-03)
assert_almost_equal(eap[eap < 0].sum(), 0, 3)
def test_shore_positive_constrain():
asm = ShoreModel(data.gtab,
radial_order=data.radial_order,
zeta=data.zeta,
lambdaN=data.lambdaN,
lambdaL=data.lambdaL,
constrain_e0=True,
positive_constraint=True,
pos_grid=11,
pos_radius=20e-03)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
asmfit = asm.fit(data.S)
eap = asmfit.pdf_grid(11, 20e-03)
npt.assert_almost_equal(eap[eap < 0].sum(), 0, 3)
def test_shore_metrics():
fetch_taiwan_ntu_dsi()
img, gtab = read_taiwan_ntu_dsi()
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angl = [(0, 0), (60, 0)]
S, sticks = MultiTensor(gtab, mevals, S0=100, angles=angl,
fractions=[50, 50], snr=None)
S = S / S[0, None].astype(np.float)
radial_order = 8
zeta = 800
lambdaN = 1e-12
lambdaL = 1e-12
asm = ShoreModel(gtab, radial_order=radial_order, zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
asmfit = asm.fit(S)
c_shore= asmfit.shore_coeff
cmat = SHOREmatrix(radial_order, zeta, gtab)
S_reconst = np.dot(cmat, c_shore)
nmse_signal = np.sqrt(np.sum((S - S_reconst) ** 2)) / (S.sum())
assert_almost_equal(nmse_signal, 0.0, 4)
mevecs2 = np.zeros((2, 3, 3))
angl = np.array(angl)
for i in range(2):
mevecs2[i] = all_tensor_evecs(sticks[i]).T
sphere = get_sphere('symmetric724')
v = sphere.vertices
radius = 10e-3
pdf_shore = asmfit.pdf(v * radius)
pdf_mt = multi_tensor_pdf(v * radius, [.5, .5], mevals=mevals, mevecs=mevecs2)
nmse_pdf = np.sqrt(np.sum((pdf_mt - pdf_shore) ** 2)) / (pdf_mt.sum())
assert_almost_equal(nmse_pdf, 0.0, 2)
rtop_shore_signal = asmfit.rtop_signal()
rtop_shore_pdf = asmfit.rtop_pdf()
assert_almost_equal(rtop_shore_signal, rtop_shore_pdf, 9)
rtop_mt = multi_tensor_rtop([.5, .5], mevals=mevals)
assert_equal(rtop_mt/rtop_shore_signal < 1.12 and rtop_mt/rtop_shore_signal > 0.9 , True)
msd_mt = multi_tensor_msd([.5, .5], mevals=mevals)
msd_shore = asmfit.msd()
assert_equal(msd_mt/msd_shore < 1.05 and msd_mt/msd_shore > 0.95 , True)
def test_multivox_shore():
gtab = get_3shell_gtab()
data = np.random.random([20, 30, 1, gtab.gradients.shape[0]])
radial_order = 4
zeta = 700
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=1e-8,
lambdaL=1e-8)
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
message=descoteaux07_legacy_msg,
category=PendingDeprecationWarning)
asmfit = asm.fit(data)
c_shore = asmfit.shore_coeff
npt.assert_equal(c_shore.shape[0:3], data.shape[0:3])
npt.assert_equal(np.alltrue(np.isreal(c_shore)), True)
def test_shore_positive_constrain():
gtab = get_gtab_taiwan_dsi()
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angl = [(0, 0), (60, 0)]
S, sticks = MultiTensor(gtab, mevals, S0=100.0, angles=angl,
fractions=[50, 50], snr=None)
radial_order = 6
zeta = 700
lambdaN = 1e-12
lambdaL = 1e-12
asm = ShoreModel(gtab, radial_order=radial_order,
zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL,
constrain_e0=True, positive_constraint=True, pos_grid=11, pos_radius=20e-03)
asmfit = asm.fit(S)
eap = asmfit.pdf_grid(11, 20e-03)
assert_equal(eap[eap<0].sum(),0)
num_vox = 5
odf_stack = np.zeros((num_vox, 1, 1, 724))
for i in range(num_vox):
radial_order = 6
lambdaN = 1e-8
lambdaL = 1e-8
print('Zeta Value: \n')
print(zeta)
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
asmfit = asm.fit(one_vox)
sphere = get_sphere('symmetric724')
odf = asmfit.odf(sphere)
odf = np.reshape(odf, [10, 1, 1, 724])
odf_stack[i, :, :, :] = odf[0, :, :, :]
print('odf.shape (%d, %d, %d, %d)' % odf.shape)
zeta = zeta + 200
# Enables/disables interactive visualization
interactive = True
ren = window.Renderer()
sfu = actor.odf_slicer(odf_stack[0:5, None, 0],
sphere=sphere,
mask = sitk.GetArrayFromImage(mask)
print(mask.shape)
# fit selected model
if model_type == '3D-SHORE':
print('Fitting 3D-SHORE')
print("radial_order: ", radial_order)
print("zeta: ", zeta)
print("lambdaN: ", lambdaN)
print("lambdaL: ", lambdaL)
model = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
asmfit = model.fit(data)
odf = asmfit.odf(sphere)
elif model_type == 'CSA-QBALL':
print('Fitting CSA-QBALL')
print("sh_order: ", sh_order)
print("smooth: ", smooth)
model = CsaOdfModel(gtab=gtab, sh_order=sh_order, smooth=smooth)
odf = model.fit(data, mask=mask).odf(sphere)
odf = np.clip(odf, 0, np.max(odf, -1)[..., None])
elif model_type == 'SFM':
print('Fitting SFM')
print("fa_thr: ", fa_thr)
response, ratio = auto_response(gtab,
data,
roi_radius=10,
fa_thr=fa_thr)
For details regarding these four parameters see (Cheng J. et al, MICCAI workshop 2011) and
(Merlet S. et al, Medical Image Analysis 2013).
"""
radial_order = 6
zeta = 700
lambdaN=1e-8
lambdaL=1e-8
asm = ShoreModel(gtab, radial_order=radial_order, zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
"""
Fit the SHORE model to the data
"""
asmfit = asm.fit(data_small)
"""
Load an odf reconstruction sphere
"""
sphere = get_sphere('symmetric724')
"""
Compute the ODF
"""
odf = asmfit.odf(sphere)
print('odf.shape (%d, %d, %d)' % odf.shape)
"""
def test_shore_metrics():
gtab = get_gtab_taiwan_dsi()
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angl = [(0, 0), (60, 0)]
S, sticks = MultiTensor(gtab, mevals, S0=100.0, angles=angl,
fractions=[50, 50], snr=None)
# test shore_indices
n = 7
l = 6
m = -4
radial_order, c = shore_order(n, l, m)
n2, l2, m2 = shore_indices(radial_order, c)
assert_equal(n, n2)
assert_equal(l, l2)
assert_equal(m, m2)
radial_order = 6
c = 41
n, l, m = shore_indices(radial_order, c)
radial_order2, c2 = shore_order(n, l, m)
assert_equal(radial_order, radial_order2)
assert_equal(c, c2)
# since we are testing without noise we can use higher order and lower lambdas, with respect to the default.
radial_order = 8
zeta = 700
lambdaN = 1e-12
lambdaL = 1e-12
asm = ShoreModel(gtab, radial_order=radial_order,
zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
asmfit = asm.fit(S)
c_shore = asmfit.shore_coeff
cmat = shore_matrix(radial_order, zeta, gtab)
S_reconst = np.dot(cmat, c_shore)
# test the signal reconstruction
S = S / S[0]
nmse_signal = np.sqrt(np.sum((S - S_reconst) ** 2)) / (S.sum())
assert_almost_equal(nmse_signal, 0.0, 4)
# test if the analytical integral of the pdf is equal to one
integral = 0
for n in range(int((radial_order)/2 +1)):
integral += c_shore[n] * (np.pi**(-1.5) * zeta **(-1.5) * genlaguerre(n,0.5)(0)) ** 0.5
assert_almost_equal(integral, 1.0, 10)
# test if the integral of the pdf calculated on a discrete grid is equal to one
pdf_discrete = asmfit.pdf_grid(17, 40e-3)
integral = pdf_discrete.sum()
assert_almost_equal(integral, 1.0, 1)
# compare the shore pdf with the ground truth multi_tensor pdf
sphere = get_sphere('symmetric724')
v = sphere.vertices
radius = 10e-3
pdf_shore = asmfit.pdf(v * radius)
pdf_mt = multi_tensor_pdf(v * radius, mevals=mevals,
angles=angl, fractions= [50, 50])
nmse_pdf = np.sqrt(np.sum((pdf_mt - pdf_shore) ** 2)) / (pdf_mt.sum())
assert_almost_equal(nmse_pdf, 0.0, 2)
# compare the shore rtop with the ground truth multi_tensor rtop
rtop_shore_signal = asmfit.rtop_signal()
rtop_shore_pdf = asmfit.rtop_pdf()
assert_almost_equal(rtop_shore_signal, rtop_shore_pdf, 9)
rtop_mt = multi_tensor_rtop([.5, .5], mevals=mevals)
assert_equal(rtop_mt / rtop_shore_signal <1.10 and rtop_mt / rtop_shore_signal > 0.95, True)
# compare the shore msd with the ground truth multi_tensor msd
msd_mt = multi_tensor_msd([.5, .5], mevals=mevals)
msd_shore = asmfit.msd()
assert_equal(msd_mt / msd_shore < 1.05 and msd_mt / msd_shore > 0.95, True)
print('Output Data all loaded and ready for use ...')
print('Fitting Shore to both models')
radial_order = 6
zeta = 700
lambdaN = 1e-8
lambdaL = 1e-8
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
asmfit = asm.fit(actual_data)
print('Shore Model Coeffs Generated ...')
print('Transforming object shape to save to a .mat file')
shore_input_matrix = np.zeros((57267, 50))
for i in range(0, 57267):
temp_shore_coeffs = asmfit.fit_array[i]._shore_coef
shore_input_matrix[i, :] = temp_shore_coeffs
if (i % 1000 == 0):
print(i)
"""
radial_order = 6
zeta = 700
lambdaN = 1e-8
lambdaL = 1e-8
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
"""
Fit the SHORE model to the data
"""
asmfit = asm.fit(data_small)
"""
Load an odf reconstruction sphere
"""
sphere = get_sphere('symmetric724')
"""
Compute the ODFs
"""
odf = asmfit.odf(sphere)
print('odf.shape (%d, %d, %d)' % odf.shape)
"""
Display the ODFs
"""
def prepare_data(csv_file, label_name):
Names = []
labels = []
Visit_ = []
# csv_file = 'data/ad_data/adrc-subject-list.csv'
with open(csv_file, 'rb') as f:
reader = csv.reader(f)
count = 0
for row in reader:
if count == 0:
# label_idx = row.index(label_name) # normal should use this, APOE is different
label_idx1 = 3
label_idx2 = 4
count = count + 1
continue
else:
Names.append(row[0])
labels.append(row[label_idx1] == '4' or row[label_idx2] == '4')
# labels.append(1*(row[2]=="Male"))
count = count + 1
# Labels = np.zeros([len(labels) , 2])
# for labelid in range(len(labels)):
# Labels[ labelid , labels[labelid] ] = 1
# pdb.set_trace()
spd_data_folder = "data/ad_data/data/"
spd_data_name = "/cor_DTI_SPD.nii"
dMRI_data_name = "/dMRI.nii.gz"
track_names = [
"fmajor_PP.avg33_mni_bbr_track_image.nii.gz",
"fminor_PP.avg33_mni_bbr_track_image.nii.gz",
"lh.atr_PP.avg33_mni_bbr_track_image.nii.gz",
"lh.cab_PP.avg33_mni_bbr_track_image.nii.gz",
"lh.ccg_PP.avg33_mni_bbr_track_image.nii.gz",
"lh.cst_AS.avg33_mni_bbr_track_image.nii.gz",
"lh.ilf_AS.avg33_mni_bbr_track_image.nii.gz",
"lh.slfp_PP.avg33_mni_bbr_track_image.nii.gz",
"lh.slft_PP.avg33_mni_bbr_track_image.nii.gz",
"lh.unc_AS.avg33_mni_bbr_track_image.nii.gz",
"rh.atr_PP.avg33_mni_bbr_track_image.nii.gz",
"rh.cab_PP.avg33_mni_bbr_track_image.nii.gz",
"rh.ccg_PP.avg33_mni_bbr_track_image.nii.gz",
"rh.cst_AS.avg33_mni_bbr_track_image.nii.gz",
"rh.ilf_AS.avg33_mni_bbr_track_image.nii.gz",
"rh.slfp_PP.avg33_mni_bbr_track_image.nii.gz",
"rh.slft_PP.avg33_mni_bbr_track_image.nii.gz",
"rh.unc_AS.avg33_mni_bbr_track_image.nii.gz",
]
Traces = [[] for _ in range(len(track_names))]
Traces_ODF = [[] for _ in range(len(track_names))]
for pid in range(len(Names)):
name = Names[pid]
for vi in ["_v2", "_v3", "_v1", "_v4"]:
spd_path = spd_data_folder + name + vi + spd_data_name
if os.path.isfile(spd_path):
break
if not os.path.isfile(spd_path):
print(
"There's something wrong in this dataset, the data is not v1/v2/v3."
)
exit()
Visit_.append(vi)
spd_img = nib.load(spd_path)
spd_data = spd_img.get_data()
mtx_whole = vec2mtx(spd_data)
dMRI_path = spd_data_folder + name + vi + dMRI_data_name
dMRI_img = nib.load(dMRI_path)
dMRI_data = dMRI_img.get_data()
############### ODF
radial_order = 6
zeta = 700
lambdaN = 1e-8
lambdaL = 1e-8
bval = "./data/ad_data/Bval_vec/dti_bvals.bval"
bvec = "./data/ad_data/Bval_vec/" + name + vi + "/bvecs_eddy.rotated.bvecs"
gtab = gtab = gradient_table(bvals=bval, bvecs=bvec)
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
############### ODF
for track_id in range(len(track_names)):
track_sub_name = track_names[track_id]
# for vi in ["_v2","_v3","_v1","_v4"]: ########## \tab for the rest 3 lines
track_path = spd_data_folder + name + vi + "/" + track_sub_name
# if os.path.isfile(track_path):
# break
if os.path.isfile(track_path):
Pos = read_fiber(track_path)
# pdb.set_trace()
minx = min(Pos[:, 0])
maxx = max(Pos[:, 0])
miny = min(Pos[:, 1])
maxy = max(Pos[:, 1])
minz = min(Pos[:, 2])
maxz = max(Pos[:, 2])
# pdb.set_trace()
asmfit = asm.fit(dMRI_data[minx:maxx + 1, miny:maxy + 1,
minz:maxz + 1])
# pdb.set_trace()
sphere = get_sphere('symmetric724')
dMRI_odf = asmfit.odf(sphere)
# pdb.set_trace()
Traces_ODF[track_id].append(dMRI_odf[(Pos[:, 0] - minx,
Pos[:, 1] - miny,
Pos[:, 2] - minz)])
Traces[track_id].append(mtx_whole[(Pos[:, 0], Pos[:,
1], Pos[:,
2])])
else:
Traces[track_id].append([])
Traces = np.asarray(Traces)
# Traces_ODF = None
Traces_ODF = np.asarray(Traces_ODF)
Labels = np.asarray(labels)
# pdb.set_trace()
return Traces, Traces_ODF, Labels, track_names, Names, Visit_
Instantiate the Model.
"""
asm = ShoreModel(gtab)
"""
Let's just use only one slice only from the data.
"""
dataslice = data[30:70, 20:80, data.shape[2] // 2]
"""
Fit the signal with the model and calculate the SHORE coefficients.
"""
asmfit = asm.fit(dataslice)
"""
Calculate the analytical rtop on the signal
that corresponds to the integral of the signal.
"""
print('Calculating... rtop_signal')
rtop_signal = asmfit.rtop_signal()
"""
Now we calculate the analytical rtop on the propagator,
that corresponds to its central value.
"""
print('Calculating... rtop_pdf')
# fit selected model
sh_coeffs = None
odf = None
if model_type == '3D-SHORE':
print('Fitting 3D-SHORE')
print("radial_order: ", radial_order)
print("zeta: ", zeta)
print("lambdaN: ", lambdaN)
print("lambdaL: ", lambdaL)
model = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
asmfit = model.fit(data)
odf = asmfit.odf(sphere)
elif model_type == 'CSA-QBALL':
print('Fitting CSA-QBALL')
print("sh_order: ", sh_order)
print("smooth: ", smooth)
model = CsaOdfModel(gtab=gtab, sh_order=sh_order, smooth=smooth)
sh_coeffs = model.fit(data, mask=mask).shm_coeff
elif model_type == 'SFM':
print('Fitting SFM')
print("fa_thr: ", fa_thr)
response, ratio = auto_response(gtab,
data,
roi_radius=10,
fa_thr=fa_thr)
model = sfm.SparseFascicleModel(gtab,
def dMRI2ODF_DTI(PATH):
'''
Input the dMRI data
return the ODF
'''
dMRI_path = PATH + 'data.nii.gz'
mask_path = PATH + 'nodif_brain_mask.nii.gz'
dMRI_img = nib.load(dMRI_path)
dMRI_data = dMRI_img.get_fdata()
mask_img = nib.load(mask_path)
mask = mask_img.get_fdata()
########## subsample ##########
# dMRI_data = dMRI_data[45:-48,50:-65,51:-54,...]
# mask = mask[45:-48,50:-65,51:-54]
# breakpoint()
dMRI_data = dMRI_data[:, 87, ...]
mask = mask[:, 87, ...]
for cnt in range(10):
fig = plt.imshow(dMRI_data[:, :, cnt].transpose(1, 0),
cmap='Greys',
interpolation='nearest')
plt.axis('off')
# plt.imshow(dMRI_data[:,15,:,cnt].transpose(1,0),cmap='Greys')
plt.savefig(str(cnt) + '.png',
bbox_inches='tight',
dpi=300,
transparent=True,
pad_inches=0)
# breakpoint()
bval = PATH + "bvals"
bvec = PATH + "bvecs"
radial_order = 6
zeta = 700
lambdaN = 1e-8
lambdaL = 1e-8
gtab = gradient_table(bvals=bval, bvecs=bvec)
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
asmfit = asm.fit(dMRI_data, mask=mask)
sphere = get_sphere('symmetric362')
dMRI_odf = asmfit.odf(sphere)
dMRI_odf[dMRI_odf <= 0] = 0
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(dMRI_data, mask)
dMRI_dti = tenfit.quadratic_form
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tenfit.evecs)
evals = tenfit.evals + 1e-20
evecs = tenfit.evecs
cfa = RGB + 1e-20
cfa /= cfa.max()
evals = np.expand_dims(evals, 2)
evecs = np.expand_dims(evecs, 2)
cfa = np.expand_dims(cfa, 2)
ren = window.Scene()
sphere = get_sphere('symmetric362')
ren.add(
actor.tensor_slicer(evals,
evecs,
scalar_colors=cfa,
sphere=sphere,
scale=0.5))
window.record(ren,
n_frames=1,
out_path='../data/tensor.png',
size=(5000, 5000))
odf_ = dMRI_odf
ren = window.Scene()
sfu = actor.odf_slicer(np.expand_dims(odf_, 2),
sphere=sphere,
colormap="plasma",
scale=0.5)
ren.add(sfu)
window.record(ren,
n_frames=1,
out_path='../data/odfs.png',
size=(5000, 5000))
return None
if mask is not None:
mask = sitk.GetArrayFromImage(mask)
print(mask.shape)
# fit selected model
sh_coeffs = None
odf = None
if model_type == '3D-SHORE':
print('Fitting 3D-SHORE')
print("radial_order: ", radial_order)
print("zeta: ", zeta)
print("lambdaN: ", lambdaN)
print("lambdaL: ", lambdaL)
model = ShoreModel(gtab, radial_order=radial_order, zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
asmfit = model.fit(data)
odf = asmfit.odf(sphere)
elif model_type == 'CSA-QBALL':
print('Fitting CSA-QBALL')
print("sh_order: ", sh_order)
print("smooth: ", smooth)
model = CsaOdfModel(gtab=gtab, sh_order=sh_order, smooth=smooth)
sh_coeffs = model.fit(data, mask=mask).shm_coeff
elif model_type == 'SFM':
print('Fitting SFM')
print("fa_thr: ", fa_thr)
response, ratio = auto_response(gtab, data, roi_radius=10, fa_thr=fa_thr)
model = sfm.SparseFascicleModel(gtab, sphere=sphere,
l1_ratio=0.5, alpha=0.001,
response=response[0])
odf = model.fit(data, mask=mask).odf(sphere)
zeta = 150
mse_vol_stack = np.zeros((dims[0], dims[1], dims[2], 5))
for i in range(5):
radial_order = 6
lambdaN = 1e-8
lambdaL = 1e-8
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
asmfit = asm.fit(nifti_img, mask)
print('Shore Model Coeffs Generated ... \n')
print('Reconstructing Signal \n')
signal_recon = asmfit.fitted_signal()
mse_volume = np.zeros((dims[0], dims[1], dims[2]))
for x in range(dims[0]):
for y in range(dims[1]):
for z in range(dims[2]):
if mask[x, y, z] == 1:
orig_sig = nifti_img[x, y, z, :]
recon_sig = signal_recon[x, y, z, :]
min_separation_angle=25,
mask=mask,
return_odf=False,
normalize_peaks=True)
GFA = csapeaks.gfa
GFA_img = nib.Nifti1Image(GFA.astype(np.float32), affine)
print GFA.shape
nib.save(GFA_img, os.getcwd()+'/zhibiao/'+f_name+'_GFA.nii.gz')
print('Saving "GFA.nii.gz" sucessful.')
from dipy.reconst.shore import ShoreModel
asm = ShoreModel(gtab)
print('Calculating...SHORE msd')
asmfit = asm.fit(data,mask)
msd = asmfit.msd()
msd[np.isnan(msd)] = 0
#print GFA[:,:,slice].T
print('Saving msd_img.png')
print msd.shape
msd_img = nib.Nifti1Image(msd.astype(np.float32), affine)
nib.save(msd_img, os.getcwd()+'/zhibiao/'+f_name+'_MSD.nii.gz')
"""Rtop """
print('Calculating... rtop_signal')
rtop_signal = asmfit.rtop_signal()
rtop_signal[np.isnan(rtop_signal)] = 0
#print GFA[:,:,slice].T
print('Saving rtop_signal_img.png')
# load dmri data
print("Loading data and mask.")
data = nib.load(data_path).get_data()
print("Data shape: ", data.shape)
# load mask
mask = nib.load(mask_path).get_data()
print("Mask shape: ", mask.shape)
st = time.time()
radial_border = 4
print(
"Started fitting SHORE model with radial_border={}".format(radial_border))
asm = ShoreModel(gtab, radial_order=radial_border)
asmfit = asm.fit(data, mask)
shore_coeff = asmfit.shore_coeff
print("SHORE coefficients shape: ", shore_coeff.shape)
# replace nan with 0
print("Replacing with nan with 0. This happens because of inaccurate mask.")
shore_coeff = np.nan_to_num(shore_coeff, 0)
print("Fitting time: {:.5f}".format(time.time() - st))
save_path = join(
exp_dir, subj_id,
'shore_features/shore_coefficients_radial_border_{}.npz'.format(
radial_border))
np.savez(save_path, data=shore_coeff)
plt.imshow(nifti_img[:, :, axial_middle, 10].T, cmap='gray', origin='lower')
plt.savefig('vishabyte_mid_axial_slices.png', bbox_inches='tight')
print('Working on fitting SHORE to the Data \n')
radial_order = 6
zeta = 3000
lambdaN = 1e-12
lambdaL = 1e-12
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
mask = nifti_img[:, :, :, 0]
asmfit = asm.fit(log_nifti_img, mask)
print('Shore Model Coeffs Generated ... \n')
print('Reconstructing Signal \n')
signal_recon = asmfit.fitted_signal()
print('Signal Reconstructed \n')
shore_input_matrix = np.zeros((dims[0], dims[1], dims[2], 50))
for i in range(0, dims[0]):
for j in range(0, dims[1]):
for k in range(0, dims[2]):
if (mask[i, j, k] == 1):
print('Data all loaded and ready for use')
print('data.shape (%d, %d)' % actual_data.shape)
radial_order = 6
zeta = 700
lambdaN = 1e-8
lambdaL = 1e-8
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
asmfit = asm.fit(actual_data)
print('Shore Model Coeffs Generated ...')
print('Transforming object shape to save to a .mat file')
shore_input_matrix = np.zeros((57267, 50))
for i in range(0, 57267):
temp_shore_coeffs = asmfit.fit_array[i]._shore_coef
shore_input_matrix[i, :] = temp_shore_coeffs
if (i % 1000 == 0):
print(i)
def dodata(f_name, data_path):
dipy_home = pjoin(os.path.expanduser('~'), 'dipy_data')
folder = pjoin(dipy_home, data_path)
fraw = pjoin(folder, f_name + '.nii.gz')
fbval = pjoin(folder, f_name + '.bval')
fbvec = pjoin(folder, f_name + '.bvec')
flabels = pjoin(folder, f_name + '.nii-label.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
img = nib.load(fraw)
data = img.get_data()
affine = img.get_affine()
label_img = nib.load(flabels)
labels = label_img.get_data()
lap = through_label_sl.label_position(labels, labelValue=1)
dataslice = data[40:80, 20:80, lap[2][2] / 2]
#print lap[2][2]/2
#get_csd_gfa(f_name,data,gtab,dataslice)
maskdata, mask = median_otsu(data,
2,
1,
False,
vol_idx=range(10, 50),
dilate=2) #不去背景
""" get fa and tensor evecs and ODF"""
from dipy.reconst.dti import TensorModel, mean_diffusivity
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
sphere = get_sphere('symmetric724')
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
np.save(os.getcwd() + '\zhibiao' + f_name + '_FA.npy', FA)
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, os.getcwd() + '\zhibiao' + f_name + '_FA.nii.gz')
print('Saving "DTI_tensor_fa.nii.gz" sucessful.')
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)
nib.save(evecs_img,
os.getcwd() + '\zhibiao' + f_name + '_DTI_tensor_evecs.nii.gz')
print('Saving "DTI_tensor_evecs.nii.gz" sucessful.')
MD1 = mean_diffusivity(tenfit.evals)
nib.save(nib.Nifti1Image(MD1.astype(np.float32), img.get_affine()),
os.getcwd() + '\zhibiao' + f_name + '_MD.nii.gz')
#tensor_odfs = tenmodel.fit(data[20:50, 55:85, 38:39]).odf(sphere)
#from dipy.reconst.odf import gfa
#dti_gfa=gfa(tensor_odfs)
wm_mask = (np.logical_or(FA >= 0.4,
(np.logical_and(FA >= 0.15, MD >= 0.0011))))
response = recursive_response(gtab,
data,
mask=wm_mask,
sh_order=8,
peak_thr=0.01,
init_fa=0.08,
init_trace=0.0021,
iter=8,
convergence=0.001,
parallel=False)
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
#csd_fit = csd_model.fit(data)
from dipy.direction import peaks_from_model
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=False)
GFA = csd_peaks.gfa
nib.save(GFA, os.getcwd() + '\zhibiao' + f_name + '_MSD.nii.gz')
print('Saving "GFA.nii.gz" sucessful.')
from dipy.reconst.shore import ShoreModel
asm = ShoreModel(gtab)
print('Calculating...SHORE msd')
asmfit = asm.fit(data, mask)
msd = asmfit.msd()
msd[np.isnan(msd)] = 0
#print GFA[:,:,slice].T
print('Saving msd_img.png')
nib.save(msd, os.getcwd() + '\zhibiao' + f_name + '_GFA.nii.gz')
print('Data all loaded and ready for use')
print('data.shape (%d, %d)' % actual_data.shape)
radial_order = 8
zeta = 700
lambdaN = 1e-8
lambdaL = 1e-8
asm = ShoreModel(gtab,
radial_order=radial_order,
zeta=zeta,
lambdaN=lambdaN,
lambdaL=lambdaL)
asmfit = asm.fit(actual_data)
print('Shore Model Coeffs Generated ...')
print('Transforming object shape to save to a .mat file')
shore_input_matrix = np.zeros((57267, 95))
for i in range(0, 57267):
temp_shore_coeffs = asmfit.fit_array[i]._shore_coef
shore_input_matrix[i, :] = temp_shore_coeffs
if (i % 1000 == 0):
print(i)
print('data.shape (%d, %d, %d, %d)' % data.shape)
"""
Instantiate the Model.
"""
asm = ShoreModel(gtab)
"""
Let's just use only one slice only from the data.
"""
dataslice = data[30:70, 20:80, data.shape[2] // 2]
"""
Fit the signal with the model and calculate the SHORE coefficients.
"""
asmfit = asm.fit(dataslice)
"""
Calculate the analytical RTOP on the signal
that corresponds to the integral of the signal.
"""
print('Calculating... rtop_signal')
rtop_signal = asmfit.rtop_signal()
"""
Now we calculate the analytical RTOP on the propagator,
that corresponds to its central value.
"""
print('Calculating... rtop_pdf')
rtop_pdf = asmfit.rtop_pdf()
"""
def test_shore_metrics():
gtab = get_gtab_taiwan_dsi()
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angl = [(0, 0), (60, 0)]
S, _ = multi_tensor(gtab, mevals, S0=100.0, angles=angl,
fractions=[50, 50], snr=None)
# test shore_indices
n = 7
l = 6
m = -4
radial_order, c = shore_order(n, l, m)
n2, l2, m2 = shore_indices(radial_order, c)
npt.assert_equal(n, n2)
npt.assert_equal(l, l2)
npt.assert_equal(m, m2)
radial_order = 6
c = 41
n, l, m = shore_indices(radial_order, c)
radial_order2, c2 = shore_order(n, l, m)
npt.assert_equal(radial_order, radial_order2)
npt.assert_equal(c, c2)
npt.assert_raises(ValueError, shore_indices, 6, 100)
npt.assert_raises(ValueError, shore_order, m, n, l)
# since we are testing without noise we can use higher order and lower
# lambdas, with respect to the default.
radial_order = 8
zeta = 700
lambdaN = 1e-12
lambdaL = 1e-12
asm = ShoreModel(gtab, radial_order=radial_order,
zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
asmfit = asm.fit(S)
c_shore = asmfit.shore_coeff
cmat = shore_matrix(radial_order, zeta, gtab)
S_reconst = np.dot(cmat, c_shore)
# test the signal reconstruction
S = S / S[0]
nmse_signal = np.sqrt(np.sum((S - S_reconst) ** 2)) / (S.sum())
npt.assert_almost_equal(nmse_signal, 0.0, 4)
# test if the analytical integral of the pdf is equal to one
integral = 0
for n in range(int((radial_order)/2 + 1)):
integral += c_shore[n] * (np.pi**(-1.5) * zeta ** (-1.5) *
genlaguerre(n, 0.5)(0)) ** 0.5
npt.assert_almost_equal(integral, 1.0, 10)
# test if the integral of the pdf calculated on a discrete grid is
# equal to one
pdf_discrete = asmfit.pdf_grid(17, 40e-3)
integral = pdf_discrete.sum()
npt.assert_almost_equal(integral, 1.0, 1)
# compare the shore pdf with the ground truth multi_tensor pdf
sphere = get_sphere('symmetric724')
v = sphere.vertices
radius = 10e-3
pdf_shore = asmfit.pdf(v * radius)
pdf_mt = multi_tensor_pdf(v * radius, mevals=mevals,
angles=angl, fractions=[50, 50])
nmse_pdf = np.sqrt(np.sum((pdf_mt - pdf_shore) ** 2)) / (pdf_mt.sum())
npt.assert_almost_equal(nmse_pdf, 0.0, 2)
# compare the shore rtop with the ground truth multi_tensor rtop
rtop_shore_signal = asmfit.rtop_signal()
rtop_shore_pdf = asmfit.rtop_pdf()
npt.assert_almost_equal(rtop_shore_signal, rtop_shore_pdf, 9)
rtop_mt = multi_tensor_rtop([.5, .5], mevals=mevals)
npt.assert_equal(rtop_mt / rtop_shore_signal < 1.10 and
rtop_mt / rtop_shore_signal > 0.95, True)
# compare the shore msd with the ground truth multi_tensor msd
msd_mt = multi_tensor_msd([.5, .5], mevals=mevals)
msd_shore = asmfit.msd()
npt.assert_equal(msd_mt / msd_shore < 1.05 and msd_mt / msd_shore > 0.95,
True)
def dodata(f_name,data_path):
dipy_home = pjoin(os.path.expanduser('~'), 'dipy_data')
folder = pjoin(dipy_home, data_path)
fraw = pjoin(folder, f_name+'.nii.gz')
fbval = pjoin(folder, f_name+'.bval')
fbvec = pjoin(folder, f_name+'.bvec')
flabels = pjoin(folder, f_name+'.nii-label.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
img = nib.load(fraw)
data = img.get_data()
affine = img.get_affine()
label_img = nib.load(flabels)
labels=label_img.get_data()
lap=through_label_sl.label_position(labels, labelValue=1)
dataslice = data[40:80, 20:80, lap[2][2] / 2]
#print lap[2][2]/2
#get_csd_gfa(f_name,data,gtab,dataslice)
maskdata, mask = median_otsu(data, 2, 1, False, vol_idx=range(10, 50), dilate=2) #不去背景
""" get fa and tensor evecs and ODF"""
from dipy.reconst.dti import TensorModel,mean_diffusivity
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
sphere = get_sphere('symmetric724')
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
np.save(os.getcwd()+'\zhibiao'+f_name+'_FA.npy',FA)
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img,os.getcwd()+'\zhibiao'+f_name+'_FA.nii.gz')
print('Saving "DTI_tensor_fa.nii.gz" sucessful.')
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)
nib.save(evecs_img, os.getcwd()+'\zhibiao'+f_name+'_DTI_tensor_evecs.nii.gz')
print('Saving "DTI_tensor_evecs.nii.gz" sucessful.')
MD1 = mean_diffusivity(tenfit.evals)
nib.save(nib.Nifti1Image(MD1.astype(np.float32), img.get_affine()), os.getcwd()+'\zhibiao'+f_name+'_MD.nii.gz')
#tensor_odfs = tenmodel.fit(data[20:50, 55:85, 38:39]).odf(sphere)
#from dipy.reconst.odf import gfa
#dti_gfa=gfa(tensor_odfs)
wm_mask = (np.logical_or(FA >= 0.4, (np.logical_and(FA >= 0.15, MD >= 0.0011))))
response = recursive_response(gtab, data, mask=wm_mask, sh_order=8,
peak_thr=0.01, init_fa=0.08,
init_trace=0.0021, iter=8, convergence=0.001,
parallel=False)
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
#csd_fit = csd_model.fit(data)
from dipy.direction import peaks_from_model
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=False)
GFA = csd_peaks.gfa
nib.save(GFA, os.getcwd()+'\zhibiao'+f_name+'_MSD.nii.gz')
print('Saving "GFA.nii.gz" sucessful.')
from dipy.reconst.shore import ShoreModel
asm = ShoreModel(gtab)
print('Calculating...SHORE msd')
asmfit = asm.fit(data,mask)
msd = asmfit.msd()
msd[np.isnan(msd)] = 0
#print GFA[:,:,slice].T
print('Saving msd_img.png')
nib.save(msd, os.getcwd()+'\zhibiao'+f_name+'_GFA.nii.gz')
nifti_path_5shell = r'D:\MASI LAB WORK\Solid_Harmonics\vishabyte_concat.nii.gz'
bvec_path_5shell = os.path.normpath(bvec_path_5shell)
bval_path_5shell = os.path.normpath(bval_path_5shell)
nifti_path_5shell = os.path.normpath(nifti_path_5shell)
nifti_5 = nib.load(nifti_path_5shell)
nifti_img_5shell = nifti_5.get_data()
bvals_5shell,bvecs_5shell = read_bvals_bvecs(bval_path_5shell, bvec_path_5shell)
gtab_5shell = gradient_table(bvals_5shell, bvecs_5shell)
asm5shell = ShoreModel(gtab_5shell, radial_order=radial_order,
zeta=zeta, lambdaN=lambdaN, lambdaL=lambdaL)
asmfit = asm.fit(nifti_img)
asm5shell_fit = asm5shell.fit(nifti_img_5shell)
asm5shell_fit.fit_array = asmfit.fit_array
print('Shore Model Coeffs Generated ... \n')
print('Reconstructing Signal with new Shore\n')
signal_recon = asm5shell_fit.fitted_signal()
#signal_recon = asmfit.fitted_signal()
print('Signal Reconstructed \n')
#print('Calculating MSE per b-value')