def compute_reconstruction(src_dmri_dir, subj_name):
src_dmri_file = os.path.join(src_dmri_dir, subj_name + par_iso_suffix)
src_bval_file = src_dmri_dir + [each for each in os.listdir(src_dmri_dir) if each.endswith('.bval')][0]
src_bvec_file = src_dmri_dir + [each for each in os.listdir(src_dmri_dir) if each.endswith('.bvec')][0]
img = nib.load(src_dmri_file)
bvals = np.loadtxt(src_bval_file)
bvecs = np.loadtxt(src_bvec_file).T
data = img.get_data()
affine = img.get_affine()
gradients = gradient_table(bvals,bvecs)
tensor_model = dti.TensorModel(gradients)
tensors = tensor_model.fit(data)
FA = dti.fractional_anisotropy(tensors.evals)
FA[np.isnan(FA)] = 0
Color_FA = np.array(255*(dti.color_fa(FA, tensors.evecs)),'uint8')
out_evecs_file = os.path.join(src_dmri_dir, subj_name + par_evecs_suffix)
evecs_img = nib.Nifti1Image(tensors.evecs.astype(np.float32), affine)
nib.save(evecs_img, out_evecs_file)
out_fa_file = os.path.join(src_dmri_dir, subj_name + par_fa_suffix)
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, out_fa_file)
out_cfa_file = os.path.join(src_dmri_dir, subj_name + par_cfa_tome_suffix)
cfa_img = nib.Nifti1Image(Color_FA, affine)
nib.save(cfa_img, out_cfa_file)
dt = np.dtype([('R', 'u1'), ('G', 'u1'), ('B', 'u1')])
out_cfa_file = os.path.join(src_dmri_dir, subj_name + par_cfa_trkvis_suffix)
cfa_img = nib.Nifti1Image((Color_FA.view((dt)).reshape(Color_FA.shape[:3])), affine)
nib.save(cfa_img, out_cfa_file)
def afficher_tenseurs(fa_,evec,eva) :
cfa = dti.color_fa(fa_, evec)
sphere = dpd.default_sphere
ren = window.Renderer()
ren.add(actor.tensor_slicer(eva, evec, scalar_colors=cfa, sphere=sphere,
scale=0.5))
window.record(ren, out_path='tensor.png', size=(1200, 1200))
def visualize(evals,evecs,viz_scale=0.5, fname='tensor_ellipsoids.png', size=(1000,1000)):
# Do vizualisation
interactive = True
ren = window.Scene()
from dipy.data import get_sphere
#sphere = get_sphere('symmetric362')
#sphere = get_sphere('repulsion724')
sphere = get_sphere('symmetric642')
# Calculate the colors. See dipy documentation.
from dipy.reconst.dti import fractional_anisotropy, color_fa
FA = fractional_anisotropy(evals)
#print(FA)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, evecs)
k=0
cfa = RGB[:, :, k:k+1]
# Normalizing like this increases the contrast, but this will make the contrast different across plots
#cfa /= cfa.max()
# imgplot = plt.imshow(FA, cmap='gray')
# plt.show()
ren.add(actor.tensor_slicer(evals, evecs, sphere=sphere, scalar_colors=cfa, scale=viz_scale, norm=False))
if interactive:
window.show(ren)
window.record(ren, n_frames=1, out_path=fname, size=(1000, 1000))
def test_color_fa():
data, gtab = dsi_voxels()
dm = dti.TensorModel(gtab, 'LS')
dmfit = dm.fit(data)
fa = fractional_anisotropy(dmfit.evals)
cfa = color_fa(fa, dmfit.evecs)
fa = np.ones((3, 3, 3))
# evecs should be of shape (fa, 3, 3)
evecs = np.zeros(fa.shape + (3, 2))
npt.assert_raises(ValueError, color_fa, fa, evecs)
evecs = np.zeros(fa.shape + (3, 3))
evecs[..., :, :] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
assert_equal(fa.shape, evecs[..., 0, 0].shape)
assert_equal((3, 3), evecs.shape[-2:])
# 3D test case
fa = np.ones((3, 3, 3))
evecs = np.zeros(fa.shape + (3, 3))
evecs[..., :, :] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
cfa = color_fa(fa, evecs)
cfa_truth = np.array([1, 0, 0])
true_cfa = np.reshape(np.tile(cfa_truth, 27), [3, 3, 3, 3])
assert_array_equal(cfa, true_cfa)
# 2D test case
fa = np.ones((3, 3))
evecs = np.zeros(fa.shape + (3, 3))
evecs[..., :, :] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
cfa = color_fa(fa, evecs)
cfa_truth = np.array([1, 0, 0])
true_cfa = np.reshape(np.tile(cfa_truth, 9), [3, 3, 3])
assert_array_equal(cfa, true_cfa)
# 1D test case
fa = np.ones((3))
evecs = np.zeros(fa.shape + (3, 3))
evecs[..., :, :] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
cfa = color_fa(fa, evecs)
cfa_truth = np.array([1, 0, 0])
true_cfa = np.reshape(np.tile(cfa_truth, 3), [3, 3])
assert_array_equal(cfa, true_cfa)
def test_color_fa():
data, gtab = dsi_voxels()
dm = dti.TensorModel(gtab, 'LS')
dmfit = dm.fit(data)
fa = fractional_anisotropy(dmfit.evals)
cfa = color_fa(fa, dmfit.evecs)
fa = np.ones((3, 3, 3))
# evecs should be of shape (fa, 3, 3)
evecs = np.zeros(fa.shape + (3, 2))
npt.assert_raises(ValueError, color_fa, fa, evecs)
evecs = np.zeros(fa.shape + (3, 3))
evecs[..., :, :] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
assert_equal(fa.shape, evecs[..., 0, 0].shape)
assert_equal((3, 3), evecs.shape[-2:])
# 3D test case
fa = np.ones((3, 3, 3))
evecs = np.zeros(fa.shape + (3, 3))
evecs[..., :, :] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
cfa = color_fa(fa, evecs)
cfa_truth = np.array([1, 0, 0])
true_cfa = np.reshape(np.tile(cfa_truth, 27), [3, 3, 3, 3])
assert_array_equal(cfa, true_cfa)
# 2D test case
fa = np.ones((3, 3))
evecs = np.zeros(fa.shape + (3, 3))
evecs[..., :, :] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
cfa = color_fa(fa, evecs)
cfa_truth = np.array([1, 0, 0])
true_cfa = np.reshape(np.tile(cfa_truth, 9), [3, 3, 3])
assert_array_equal(cfa, true_cfa)
# 1D test case
fa = np.ones((3))
evecs = np.zeros(fa.shape + (3, 3))
evecs[..., :, :] = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
cfa = color_fa(fa, evecs)
cfa_truth = np.array([1, 0, 0])
true_cfa = np.reshape(np.tile(cfa_truth, 3), [3, 3])
assert_array_equal(cfa, true_cfa)
def compute_colored_fa(self):
print('Computing colored FA')
fa = np.clip(self.fa, 0, 1)
rgb = color_fa(fa, self.tenfit.evecs)
nib.save(
nib.Nifti1Image(np.array(255 * rgb, 'uint8'), self.img.affine),
'tensor_rgb.nii.gz')
return rgb
def FA_RGB(data, gtab):
"""
Input : data, gtab taken from the load_data.py script.
Return : FA and RGB as two nd numpy array
"""
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tenfit.evecs)
return FA, RGB
def compute_anisotropy(self, tenfit: dti.TensorFit):
print("Computing anisotropy measures (FA, MD, RGB)")
mother_dir = os.path.dirname(self.denoised)
FA = dti.fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
fa_img = nib.Nifti1Image(FA.astype(np.float32), self.img.affine)
nib.save(fa_img, f"{mother_dir}/tensor_fa.nii.gz")
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), self.img.affine)
nib.save(evecs_img, f"{mother_dir}/tensor_evecs.nii.gz")
MD1 = dti.mean_diffusivity(tenfit.evals)
MD1_img = nib.Nifti1Image(MD1.astype(np.float32), self.img.affine)
nib.save(MD1_img, f"{mother_dir}/tensors_md.nii.gz")
FA = np.clip(FA, 0, 1)
RGB = dti.color_fa(FA, tenfit.evecs)
RGB_img = nib.Nifti1Image(np.array(255 * RGB, "uint8"), self.img.affine)
nib.save(RGB_img, f"{mother_dir}/tensor_rgb.nii.gz")
def segment_from_cfa(tensor_fit, roi, threshold, return_cfa=False):
"""
Segment the cfa inside roi using the values from threshold as bounds.
Parameters
-------------
tensor_fit : TensorFit object
TensorFit object
roi : ndarray
A binary mask, which contains the bounding box for the segmentation.
threshold : array-like
An iterable that defines the min and max values to use for the
thresholding.
The values are specified as (R_min, R_max, G_min, G_max, B_min, B_max)
return_cfa : bool, optional
If True, the cfa is also returned.
Returns
----------
mask : ndarray
Binary mask of the segmentation.
cfa : ndarray, optional
Array with shape = (..., 3), where ... is the shape of tensor_fit.
The color fractional anisotropy, ordered as a nd array with the last
dimension of size 3 for the R, G and B channels.
"""
FA = fractional_anisotropy(tensor_fit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1) # Clamp the FA to remove degenerate tensors
cfa = color_fa(FA, tensor_fit.evecs)
roi = np.asarray(roi, dtype=bool)
include = ((cfa >= threshold[0::2]) &
(cfa <= threshold[1::2]) &
roi[..., None])
mask = np.all(include, axis=-1)
if return_cfa:
return mask, cfa
return mask
def segment_from_cfa(tensor_fit, roi, threshold, return_cfa=False):
"""
Segment the cfa inside roi using the values from threshold as bounds.
Parameters
----------
tensor_fit : TensorFit object
TensorFit object
roi : ndarray
A binary mask, which contains the bounding box for the segmentation.
threshold : array-like
An iterable that defines the min and max values to use for the
thresholding.
The values are specified as (R_min, R_max, G_min, G_max, B_min, B_max)
return_cfa : bool, optional
If True, the cfa is also returned.
Returns
-------
mask : ndarray
Binary mask of the segmentation.
cfa : ndarray, optional
Array with shape = (..., 3), where ... is the shape of tensor_fit.
The color fractional anisotropy, ordered as a nd array with the last
dimension of size 3 for the R, G and B channels.
"""
FA = fractional_anisotropy(tensor_fit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1) # Clamp the FA to remove degenerate tensors
cfa = color_fa(FA, tensor_fit.evecs)
roi = np.asarray(roi, dtype=bool)
include = ((cfa >= threshold[0::2]) &
(cfa <= threshold[1::2]) &
roi[..., None])
mask = np.all(include, axis=-1)
if return_cfa:
return mask, cfa
return mask
def plot_as_dti(gtab, data, params, dipy_sph, output_dir="."):
logging.info("Fitting data to DTI model...")
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(data[params['slice']])
FA = dti.fractional_anisotropy(tenfit.evals)
FA = np.clip(FA, 0, 1)
RGB = dti.color_fa(FA, tenfit.evecs)
cfa = RGB
cfa /= cfa.max()
logging.info("Recording DTI plot...")
camera_params, long_ax, view_ax, stack_ax = \
prepare_plot_camera(data[params['slice']].shape[:-1], scale=2.2)
r = fvtk.ren()
fvtk.add(r, fvtk.tensor(tenfit.evals, tenfit.evecs, cfa, dipy_sph))
r.set_camera(**camera_params)
fname = os.path.join(output_dir, "plot-dti-0.png")
fvtk.snapshot(r, size=(1500, 1500), offscreen=True, fname=fname)
def DTImaps(ImgPath, Bvalpath, Bvecpath, tracto=True):
data, affine = resli(ImgPath)
data = Nonlocal(data, affine)
b0_mask, mask = otsu(data, affine) #maask binary
evals, evecs = DTImodel(b0_mask, affine, mask, gtab(Bvalpath, Bvecpath))
print('--> Calculando el mapa de anisotropia fraccional')
FA = fractional_anisotropy(evals)
FA[np.isnan(FA)] = 0
nib.save(nib.Nifti1Image(FA.astype(np.float32), affine),
"Mapa_anisotropia_fraccional")
print('--> Calculando el mapa de anisotropia fraccional RGB')
FA2 = np.clip(FA, 0, 1)
RGB = color_fa(FA2, evecs)
nib.save(nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine),
"Mapa_anisotropia_fraccional RGB")
print('--> Calculando el mapa de difusividad media')
MD1 = dti.mean_diffusivity(evals)
nib.save(nib.Nifti1Image(MD1.astype(np.float32), affine),
"Mapa_difusividad_media")
if tracto:
sphere = get_sphere('symmetric724')
peak_indices = quantize_evecs(evecs, sphere.vertices)
eu = EuDX(FA.astype('f8'),
peak_indices,
seeds=500000,
odf_vertices=sphere.vertices,
a_low=0.15)
tensor_streamlines = [streamline for streamline in eu]
new_vox_sz = (2., 2., 2.)
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = new_vox_sz
hdr['voxel_order'] = 'LAS'
hdr['dim'] = FA.shape
tensor_streamlines_trk = ((sl, None, None)
for sl in tensor_streamlines)
ten_sl_fname = "Tracto.trk"
nib.trackvis.write(ten_sl_fname,
tensor_streamlines_trk,
hdr,
points_space='voxel')
return FA
def compute_reconstruction(src_dmri_dir, subj_name):
src_dmri_file = os.path.join(src_dmri_dir, subj_name + par_iso_suffix)
src_bval_file = src_dmri_dir + [
each for each in os.listdir(src_dmri_dir) if each.endswith('.bval')
][0]
src_bvec_file = src_dmri_dir + [
each for each in os.listdir(src_dmri_dir) if each.endswith('.bvec')
][0]
img = nib.load(src_dmri_file)
bvals = np.loadtxt(src_bval_file)
bvecs = np.loadtxt(src_bvec_file).T
data = img.get_data()
affine = img.get_affine()
gradients = gradient_table(bvals, bvecs)
tensor_model = dti.TensorModel(gradients)
tensors = tensor_model.fit(data)
FA = dti.fractional_anisotropy(tensors.evals)
FA[np.isnan(FA)] = 0
Color_FA = np.array(255 * (dti.color_fa(FA, tensors.evecs)), 'uint8')
out_evecs_file = os.path.join(src_dmri_dir, subj_name + par_evecs_suffix)
evecs_img = nib.Nifti1Image(tensors.evecs.astype(np.float32), affine)
nib.save(evecs_img, out_evecs_file)
out_fa_file = os.path.join(src_dmri_dir, subj_name + par_fa_suffix)
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, out_fa_file)
out_cfa_file = os.path.join(src_dmri_dir, subj_name + par_cfa_tome_suffix)
cfa_img = nib.Nifti1Image(Color_FA, affine)
nib.save(cfa_img, out_cfa_file)
dt = np.dtype([('R', 'u1'), ('G', 'u1'), ('B', 'u1')])
out_cfa_file = os.path.join(src_dmri_dir,
subj_name + par_cfa_trkvis_suffix)
cfa_img = nib.Nifti1Image((Color_FA.view(
(dt)).reshape(Color_FA.shape[:3])), affine)
nib.save(cfa_img, out_cfa_file)
def tensorFitting(context, dwi_path, gtab):
configuration = Application().configuration
cmds = ['fsleyes', 'fslview']
viewers = [
find_executable(configuration.FSL.fsl_commands_prefix + cmd)
for cmd in cmds
]
img = nib.load(dwi_path)
data = img.get_data()
tenmodel = dti.TensorModel(gtab) # instantiate tensor model
tenfit = tenmodel.fit(data) # fit data to tensor model
FA = dti.fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0 # correct for background value
evecs = tenfit.evecs.astype(np.float32)
e1 = evecs[..., 0]
rgb = dti.color_fa(FA, evecs)
tensor_fa = context.temporary('NIFTI-1 image')
tensor_evecs = context.temporary('NIFTI-1 image')
path_fa = tensor_fa.fullPath() + '_' + 'FA.nii.gz'
path_e1 = tensor_evecs.fullPath() + '_' + 'first_eigenvector.nii.gz'
fa_img = nib.Nifti1Image(FA.astype(np.float32), img.get_affine())
nib.save(fa_img, path_fa)
e1_img = nib.Nifti1Image(e1, img.get_affine())
nib.save(e1_img, path_e1)
context.write(
'If color coding of FA map is not right, swap axes and run again')
context.write(
'If orientation of principal diffusion direction does not look right, flip the axis along which slices look good'
)
if viewers[0]:
#display first eigen vector as line coloured by orientation and modulated by FA superimposed onto the FA volume (fsleyes only)
cmd = [
'fsleyes', path_fa, path_e1, '-ot', 'linevector', '-mr', '0 1',
'-mo', path_fa
]
else:
#display FA and First Eigen vector as volumes (old way, display needs to be done by hand)
cmd = ['fslview', path_fa, path_e1]
context.system(*cmd)
return FA, evecs, rgb
def tensor2fa(tensors, tensor_name, dwi, derivdir, qcdir):
'''
outdir: location of output directory.
fname: name of output fa map file. default is none (name created based on
input file)
'''
dwi_data = nb.load(dwi)
affine = dwi_data.get_affine()
dwi_data = dwi_data.get_data()
# create FA map
FA = fractional_anisotropy(tensors.evals)
FA[np.isnan(FA)] = 0
# generate the RGB FA map
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tensors.evecs)
fname = os.path.split(tensor_name)[1].split(".")[0] + '_fa_rgb.nii.gz'
fa = nb.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nb.save(fa, derivdir + fname)
fa_pngs(fa, fname, qcdir)
def tensor2fa(tensors, tensor_name, dti, derivdir, qcdir):
'''
outdir: location of output directory.
fname: name of output fa map file. default is none (name created based on
input file)
'''
dti_data = nb.load(dti)
affine = dti_data.get_affine()
dti_data = dti_data.get_data()
# create FA map
FA = fractional_anisotropy(tensors.evals)
FA[np.isnan(FA)] = 0
# generate the RGB FA map
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tensors.evecs)
fname = os.path.split(tensor_name)[1].split(".")[0] + '_fa_rgb.nii.gz'
fa = nb.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nb.save(fa, derivdir + fname)
fa_pngs(fa, fname, qcdir)
def saveAllImagesFromDWI(fpath, outputFolder):
arr = nib.load(fpath).get_fdata().squeeze()
matArr = np.apply_along_axis(six2mat, -1, arr)
evalArr, evecArr = np.linalg.eig(matArr)
faArr = fractional_anisotropy(evalArr)
colorImg = color_fa(faArr, evecArr)
fileName = os.path.basename(fpath).split('_')[0]
outPath = os.path.join(outputFolder, fileName)
os.makedirs(outPath, exist_ok=True)
os.makedirs(os.path.join(outPath, "x"), exist_ok=True)
for i in range(colorImg.shape[0]):
img = Image.fromarray((colorImg[i, :, :] * 255).astype(np.uint8))
img.save(os.path.join(outPath, "x", f"{fileName}_x_slice_{i}.png"))
os.makedirs(os.path.join(outPath, "y"), exist_ok=True)
for i in range(colorImg.shape[1]):
img = Image.fromarray((colorImg[:, i, :] * 255).astype(np.uint8))
img.save(os.path.join(outPath, "y", f"{fileName}_y_slice_{i}.png"))
os.makedirs(os.path.join(outPath, "z"), exist_ok=True)
for i in range(colorImg.shape[2]):
img = Image.fromarray((colorImg[:, :, i] * 255).astype(np.uint8))
img.save(os.path.join(outPath, "z", f"{fileName}_z_slice_{i}.png"))
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
from dipy.reconst.dti import TensorModel
ten = TensorModel(gtab)
tenfit = ten.fit(data,mask)
from dipy.reconst.dti import fractional_anisotropy
fa = fractional_anisotropy(tenfit.evals)
fa[np.isnan(fa)] = 0
from dipy.reconst.dti import color_fa
Rgbv = color_fa(fa, tenfit.evecs)
fa = np.clip(fa, 0,1)
#save FA to image
nib.save(nib.Nifti1Image(np.array(255*Rgbv,'uint8'),img.get_affine()),'tensor_rgb.nii.gz')
import matplotlib.pyplot as plt
axial_middle = data.shape[2] / 2
plt.figure('Showing the datasets')
plt.subplot(1, 2, 1).set_axis_off()
plt.imshow(data[:, :, axial_middle, 0].T, cmap='gray', origin='lower')
plt.subplot(1, 2, 2).set_axis_off()
plt.imshow(data[:, :, axial_middle, 10].T, cmap='gray', origin='lower')
def run(self,
input_files,
bvalues,
bvectors,
mask_files,
b0_threshold=0.0,
save_metrics=[],
out_dir='',
out_tensor='tensors.nii.gz',
out_fa='fa.nii.gz',
out_ga='ga.nii.gz',
out_rgb='rgb.nii.gz',
out_md='md.nii.gz',
out_ad='ad.nii.gz',
out_rd='rd.nii.gz',
out_mode='mode.nii.gz',
out_evec='evecs.nii.gz',
out_eval='evals.nii.gz'):
""" Workflow for tensor reconstruction and for computing DTI metrics.
Performs a tensor reconstruction on the files by 'globing'
``input_files`` and saves the DTI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions (default 0.0)
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
(default [] (all))
out_dir : string, optional
Output directory (default input file directory)
out_tensor : string, optional
Name of the tensors volume to be saved (default 'tensors.nii.gz')
out_fa : string, optional
Name of the fractional anisotropy volume to be saved
(default 'fa.nii.gz')
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved
(default 'ga.nii.gz')
out_rgb : string, optional
Name of the color fa volume to be saved (default 'rgb.nii.gz')
out_md : string, optional
Name of the mean diffusivity volume to be saved
(default 'md.nii.gz')
out_ad : string, optional
Name of the axial diffusivity volume to be saved
(default 'ad.nii.gz')
out_rd : string, optional
Name of the radial diffusivity volume to be saved
(default 'rd.nii.gz')
out_mode : string, optional
Name of the mode volume to be saved (default 'mode.nii.gz')
out_evec : string, optional
Name of the eigenvectors volume to be saved
(default 'evecs.nii.gz')
out_eval : string, optional
Name of the eigenvalues to be saved (default 'evals.nii.gz')
"""
io_it = self.get_io_iterator()
for dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad, \
omode, oevecs, oevals in io_it:
logging.info('Computing DTI metrics for {0}'.format(dwi))
img = nib.load(dwi)
data = img.get_data()
affine = img.get_affine()
if mask is None:
mask = None
else:
mask = nib.load(mask).get_data().astype(np.bool)
tenfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold)
if not save_metrics:
save_metrics = [
'fa', 'md', 'rd', 'ad', 'ga', 'rgb', 'mode', 'evec',
'eval', 'tensor'
]
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'tensor' in save_metrics:
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(
tensor_vals_reordered.astype(np.float32), affine)
nib.save(fiber_tensors, otensor)
if 'fa' in save_metrics:
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, ofa)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(tenfit.evals)
ga_img = nib.Nifti1Image(GA.astype(np.float32), affine)
nib.save(ga_img, oga)
if 'rgb' in save_metrics:
RGB = color_fa(FA, tenfit.evecs)
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, orgb)
if 'md' in save_metrics:
MD = mean_diffusivity(tenfit.evals)
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, omd)
if 'ad' in save_metrics:
AD = axial_diffusivity(tenfit.evals)
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, oad)
if 'rd' in save_metrics:
RD = radial_diffusivity(tenfit.evals)
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, orad)
if 'mode' in save_metrics:
MODE = get_mode(tenfit.quadratic_form)
mode_img = nib.Nifti1Image(MODE.astype(np.float32), affine)
nib.save(mode_img, omode)
if 'evec' in save_metrics:
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32),
affine)
nib.save(evecs_img, oevecs)
if 'eval' in save_metrics:
evals_img = nib.Nifti1Image(tenfit.evals.astype(np.float32),
affine)
nib.save(evals_img, oevals)
logging.info('DTI metrics saved in {0}'.format(
os.path.dirname(oevals)))
def run(self, input_files, bvalues, bvectors, mask_files, b0_threshold=0.0,
save_metrics=[],
out_dir='', out_tensor='tensors.nii.gz', out_fa='fa.nii.gz',
out_ga='ga.nii.gz', out_rgb='rgb.nii.gz', out_md='md.nii.gz',
out_ad='ad.nii.gz', out_rd='rd.nii.gz', out_mode='mode.nii.gz',
out_evec='evecs.nii.gz', out_eval='evals.nii.gz'):
""" Workflow for tensor reconstruction and for computing DTI metrics.
Performs a tensor reconstruction on the files by 'globing'
``input_files`` and saves the DTI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions (default 0.0)
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
(default [] (all))
out_dir : string, optional
Output directory (default input file directory)
out_tensor : string, optional
Name of the tensors volume to be saved (default 'tensors.nii.gz')
out_fa : string, optional
Name of the fractional anisotropy volume to be saved
(default 'fa.nii.gz')
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved
(default 'ga.nii.gz')
out_rgb : string, optional
Name of the color fa volume to be saved (default 'rgb.nii.gz')
out_md : string, optional
Name of the mean diffusivity volume to be saved
(default 'md.nii.gz')
out_ad : string, optional
Name of the axial diffusivity volume to be saved
(default 'ad.nii.gz')
out_rd : string, optional
Name of the radial diffusivity volume to be saved
(default 'rd.nii.gz')
out_mode : string, optional
Name of the mode volume to be saved (default 'mode.nii.gz')
out_evec : string, optional
Name of the eigenvectors volume to be saved
(default 'evecs.nii.gz')
out_eval : string, optional
Name of the eigenvalues to be saved (default 'evals.nii.gz')
"""
io_it = self.get_io_iterator()
for dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad, \
omode, oevecs, oevals in io_it:
logging.info('Computing DTI metrics for {0}'.format(dwi))
img = nib.load(dwi)
data = img.get_data()
affine = img.get_affine()
if mask is None:
mask = None
else:
mask = nib.load(mask).get_data().astype(np.bool)
tenfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold)
if not save_metrics:
save_metrics = ['fa', 'md', 'rd', 'ad', 'ga', 'rgb', 'mode',
'evec', 'eval', 'tensor']
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'tensor' in save_metrics:
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(tensor_vals_reordered.astype(
np.float32), affine)
nib.save(fiber_tensors, otensor)
if 'fa' in save_metrics:
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, ofa)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(tenfit.evals)
ga_img = nib.Nifti1Image(GA.astype(np.float32), affine)
nib.save(ga_img, oga)
if 'rgb' in save_metrics:
RGB = color_fa(FA, tenfit.evecs)
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, orgb)
if 'md' in save_metrics:
MD = mean_diffusivity(tenfit.evals)
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, omd)
if 'ad' in save_metrics:
AD = axial_diffusivity(tenfit.evals)
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, oad)
if 'rd' in save_metrics:
RD = radial_diffusivity(tenfit.evals)
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, orad)
if 'mode' in save_metrics:
MODE = get_mode(tenfit.quadratic_form)
mode_img = nib.Nifti1Image(MODE.astype(np.float32), affine)
nib.save(mode_img, omode)
if 'evec' in save_metrics:
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)
nib.save(evecs_img, oevecs)
if 'eval' in save_metrics:
evals_img = nib.Nifti1Image(tenfit.evals.astype(np.float32), affine)
nib.save(evals_img, oevals)
logging.info('DTI metrics saved in {0}'.
format(os.path.dirname(oevals)))
def main():
parser = _build_args_parser()
args = parser.parse_args()
if not args.not_all:
args.fa = args.fa or 'fa.nii.gz'
args.ga = args.ga or 'ga.nii.gz'
args.rgb = args.rgb or 'rgb.nii.gz'
args.md = args.md or 'md.nii.gz'
args.ad = args.ad or 'ad.nii.gz'
args.rd = args.rd or 'rd.nii.gz'
args.mode = args.mode or 'mode.nii.gz'
args.norm = args.norm or 'tensor_norm.nii.gz'
args.tensor = args.tensor or 'tensor.nii.gz'
args.evecs = args.evecs or 'tensor_evecs.nii.gz'
args.evals = args.evals or 'tensor_evals.nii.gz'
args.residual = args.residual or 'dti_residual.nii.gz'
args.p_i_signal =\
args.p_i_signal or 'physically_implausible_signals_mask.nii.gz'
args.pulsation = args.pulsation or 'pulsation_and_misalignment.nii.gz'
outputs = [args.fa, args.ga, args.rgb, args.md, args.ad, args.rd,
args.mode, args.norm, args.tensor, args.evecs, args.evals,
args.residual, args.p_i_signal, args.pulsation]
if args.not_all and not any(outputs):
parser.error('When using --not_all, you need to specify at least ' +
'one metric to output.')
assert_inputs_exist(
parser, [args.input, args.bvals, args.bvecs], args.mask)
assert_outputs_exist(parser, args, outputs)
img = nib.load(args.input)
data = img.get_data()
affine = img.get_affine()
if args.mask is None:
mask = None
else:
mask = nib.load(args.mask).get_data().astype(np.bool)
# Validate bvals and bvecs
logging.info('Tensor estimation with the %s method...', args.method)
bvals, bvecs = read_bvals_bvecs(args.bvals, args.bvecs)
if not is_normalized_bvecs(bvecs):
logging.warning('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
check_b0_threshold(args, bvals.min())
gtab = gradient_table(bvals, bvecs, b0_threshold=bvals.min())
# Get tensors
if args.method == 'restore':
sigma = ne.estimate_sigma(data)
tenmodel = TensorModel(gtab, fit_method=args.method, sigma=sigma,
min_signal=_get_min_nonzero_signal(data))
else:
tenmodel = TensorModel(gtab, fit_method=args.method,
min_signal=_get_min_nonzero_signal(data))
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if args.tensor:
# Get the Tensor values and format them for visualisation
# in the Fibernavigator.
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(
tensor_vals_reordered.astype(np.float32), affine)
nib.save(fiber_tensors, args.tensor)
if args.fa:
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, args.fa)
if args.ga:
GA = geodesic_anisotropy(tenfit.evals)
GA[np.isnan(GA)] = 0
ga_img = nib.Nifti1Image(GA.astype(np.float32), affine)
nib.save(ga_img, args.ga)
if args.rgb:
RGB = color_fa(FA, tenfit.evecs)
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, args.rgb)
if args.md:
MD = mean_diffusivity(tenfit.evals)
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, args.md)
if args.ad:
AD = axial_diffusivity(tenfit.evals)
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, args.ad)
if args.rd:
RD = radial_diffusivity(tenfit.evals)
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, args.rd)
if args.mode:
# Compute tensor mode
inter_mode = dipy_mode(tenfit.quadratic_form)
# Since the mode computation can generate NANs when not masked,
# we need to remove them.
non_nan_indices = np.isfinite(inter_mode)
mode = np.zeros(inter_mode.shape)
mode[non_nan_indices] = inter_mode[non_nan_indices]
mode_img = nib.Nifti1Image(mode.astype(np.float32), affine)
nib.save(mode_img, args.mode)
if args.norm:
NORM = norm(tenfit.quadratic_form)
norm_img = nib.Nifti1Image(NORM.astype(np.float32), affine)
nib.save(norm_img, args.norm)
if args.evecs:
evecs = tenfit.evecs.astype(np.float32)
evecs_img = nib.Nifti1Image(evecs, affine)
nib.save(evecs_img, args.evecs)
# save individual e-vectors also
e1_img = nib.Nifti1Image(evecs[..., 0], affine)
e2_img = nib.Nifti1Image(evecs[..., 1], affine)
e3_img = nib.Nifti1Image(evecs[..., 2], affine)
nib.save(e1_img, add_filename_suffix(args.evecs, '_v1'))
nib.save(e2_img, add_filename_suffix(args.evecs, '_v2'))
nib.save(e3_img, add_filename_suffix(args.evecs, '_v3'))
if args.evals:
evals = tenfit.evals.astype(np.float32)
evals_img = nib.Nifti1Image(evals, affine)
nib.save(evals_img, args.evals)
# save individual e-values also
e1_img = nib.Nifti1Image(evals[..., 0], affine)
e2_img = nib.Nifti1Image(evals[..., 1], affine)
e3_img = nib.Nifti1Image(evals[..., 2], affine)
nib.save(e1_img, add_filename_suffix(args.evals, '_e1'))
nib.save(e2_img, add_filename_suffix(args.evals, '_e2'))
nib.save(e3_img, add_filename_suffix(args.evals, '_e3'))
if args.p_i_signal:
S0 = np.mean(data[..., gtab.b0s_mask], axis=-1, keepdims=True)
DWI = data[..., ~gtab.b0s_mask]
pis_mask = np.max(S0 < DWI, axis=-1)
if args.mask is not None:
pis_mask *= mask
pis_img = nib.Nifti1Image(pis_mask.astype(np.int16), affine)
nib.save(pis_img, args.p_i_signal)
if args.pulsation:
STD = np.std(data[..., ~gtab.b0s_mask], axis=-1)
if args.mask is not None:
STD *= mask
std_img = nib.Nifti1Image(STD.astype(np.float32), affine)
nib.save(std_img, add_filename_suffix(args.pulsation, '_std_dwi'))
if np.sum(gtab.b0s_mask) <= 1:
logger.info('Not enough b=0 images to output standard '
'deviation map')
else:
if len(np.where(gtab.b0s_mask)) == 2:
logger.info('Only two b=0 images. Be careful with the '
'interpretation of this std map')
STD = np.std(data[..., gtab.b0s_mask], axis=-1)
if args.mask is not None:
STD *= mask
std_img = nib.Nifti1Image(STD.astype(np.float32), affine)
nib.save(std_img, add_filename_suffix(args.pulsation, '_std_b0'))
if args.residual:
# Mean residual image
S0 = np.mean(data[..., gtab.b0s_mask], axis=-1)
data_p = tenfit.predict(gtab, S0)
R = np.mean(np.abs(data_p[..., ~gtab.b0s_mask] -
data[..., ~gtab.b0s_mask]), axis=-1)
if args.mask is not None:
R *= mask
R_img = nib.Nifti1Image(R.astype(np.float32), affine)
nib.save(R_img, args.residual)
# Each volume's residual statistics
if args.mask is None:
logger.info("Outlier detection will not be performed, since no "
"mask was provided.")
stats = [dict.fromkeys(['label', 'mean', 'iqr', 'cilo', 'cihi', 'whishi',
'whislo', 'fliers', 'q1', 'med', 'q3'], [])
for i in range(data.shape[-1])] # stats with format for boxplots
# Note that stats will be computed manually and plotted using bxp
# but could be computed using stats = cbook.boxplot_stats
# or pyplot.boxplot(x)
R_k = np.zeros(data.shape[-1]) # mean residual per DWI
std = np.zeros(data.shape[-1]) # std residual per DWI
q1 = np.zeros(data.shape[-1]) # first quartile per DWI
q3 = np.zeros(data.shape[-1]) # third quartile per DWI
iqr = np.zeros(data.shape[-1]) # interquartile per DWI
percent_outliers = np.zeros(data.shape[-1])
nb_voxels = np.count_nonzero(mask)
for k in range(data.shape[-1]):
x = np.abs(data_p[..., k] - data[..., k])[mask]
R_k[k] = np.mean(x)
std[k] = np.std(x)
q3[k], q1[k] = np.percentile(x, [75, 25])
iqr[k] = q3[k] - q1[k]
stats[k]['med'] = (q1[k] + q3[k]) / 2
stats[k]['mean'] = R_k[k]
stats[k]['q1'] = q1[k]
stats[k]['q3'] = q3[k]
stats[k]['whislo'] = q1[k] - 1.5 * iqr[k]
stats[k]['whishi'] = q3[k] + 1.5 * iqr[k]
stats[k]['label'] = k
# Outliers are observations that fall below Q1 - 1.5(IQR) or
# above Q3 + 1.5(IQR) We check if a voxel is an outlier only if
# we have a mask, else we are biased.
if args.mask is not None:
outliers = (x < stats[k]['whislo']) | (x > stats[k]['whishi'])
percent_outliers[k] = np.sum(outliers)/nb_voxels*100
# What would be our definition of too many outliers?
# Maybe mean(all_means)+-3SD?
# Or we let people choose based on the figure.
# if percent_outliers[k] > ???? :
# logger.warning(' Careful! Diffusion-Weighted Image'
# ' i=%s has %s %% outlier voxels',
# k, percent_outliers[k])
# Saving all statistics as npy values
residual_basename, _ = split_name_with_nii(args.residual)
res_stats_basename = residual_basename + ".npy"
np.save(add_filename_suffix(
res_stats_basename, "_mean_residuals"), R_k)
np.save(add_filename_suffix(res_stats_basename, "_q1_residuals"), q1)
np.save(add_filename_suffix(res_stats_basename, "_q3_residuals"), q3)
np.save(add_filename_suffix(res_stats_basename, "_iqr_residuals"), iqr)
np.save(add_filename_suffix(res_stats_basename, "_std_residuals"), std)
# Showing results in graph
if args.mask is None:
fig, axe = plt.subplots(nrows=1, ncols=1, squeeze=False)
else:
fig, axe = plt.subplots(nrows=1, ncols=2, squeeze=False,
figsize=[10, 4.8])
# Default is [6.4, 4.8]. Increasing width to see better.
medianprops = dict(linestyle='-', linewidth=2.5, color='firebrick')
meanprops = dict(linestyle='-', linewidth=2.5, color='green')
axe[0, 0].bxp(stats, showmeans=True, meanline=True, showfliers=False,
medianprops=medianprops, meanprops=meanprops)
axe[0, 0].set_xlabel('DW image')
axe[0, 0].set_ylabel('Residuals per DWI volume. Red is median,\n'
'green is mean. Whiskers are 1.5*interquartile')
axe[0, 0].set_title('Residuals')
axe[0, 0].set_xticks(range(0, q1.shape[0], 5))
axe[0, 0].set_xticklabels(range(0, q1.shape[0], 5))
if args.mask is not None:
axe[0, 1].plot(range(data.shape[-1]), percent_outliers)
axe[0, 1].set_xticks(range(0, q1.shape[0], 5))
axe[0, 1].set_xticklabels(range(0, q1.shape[0], 5))
axe[0, 1].set_xlabel('DW image')
axe[0, 1].set_ylabel('Percentage of outlier voxels')
axe[0, 1].set_title('Outliers')
plt.savefig(residual_basename + '_residuals_stats.png')
ffa = dname + 'tensor_fa.nii.gz'
fa_img = nib.Nifti1Image(tenfit.fa.astype(np.float32), affine)
nib.save(fa_img, ffa)
# In[10]:
# save first eigen vector
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)
nib.save(evecs_img, dname + 'tensor_evecs.nii.gz')
# In[32]:
# compute and save rgb
from dipy.reconst.dti import color_fa
RGB = color_fa(tenfit.fa, tenfit.evecs)
nib.save(nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine),
dname + 'tensor_rgb.nii.gz')
# In[ ]:
# In[36]:
sh_order = 8 #TODO: check what that does
if data.shape[-1] < 15:
raise ValueError('You need at least 15 unique DWI volumes to '
'compute fiber ODFs. You currently have: {0}'
' DWI volumes.'.format(data.shape[-1]))
elif data.shape[-1] < 30:
sh_order = 6
def test_tensor_slicer(interactive=False):
evals = np.array([1.4, .35, .35]) * 10 ** (-3)
evecs = np.eye(3)
mevals = np.zeros((3, 2, 4, 3))
mevecs = np.zeros((3, 2, 4, 3, 3))
mevals[..., :] = evals
mevecs[..., :, :] = evecs
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
affine = np.eye(4)
renderer = window.Renderer()
tensor_actor = actor.tensor_slicer(mevals, mevecs, affine=affine,
sphere=sphere, scale=.3)
I, J, K = mevals.shape[:3]
renderer.add(tensor_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
tensor_actor.display_extent(0, 1, 0, J, 0, K)
tensor_actor.GetProperty().SetOpacity(1.0)
if interactive:
window.show(renderer, reset_camera=False)
npt.assert_equal(renderer.GetActors().GetNumberOfItems(), 1)
# Test extent
big_extent = renderer.GetActors().GetLastActor().GetBounds()
big_extent_x = abs(big_extent[1] - big_extent[0])
tensor_actor.display(x=2)
if interactive:
window.show(renderer, reset_camera=False)
small_extent = renderer.GetActors().GetLastActor().GetBounds()
small_extent_x = abs(small_extent[1] - small_extent[0])
npt.assert_equal(big_extent_x > small_extent_x, True)
# Test empty mask
empty_actor = actor.tensor_slicer(mevals, mevecs, affine=affine,
mask=np.zeros(mevals.shape[:3]),
sphere=sphere, scale=.3)
npt.assert_equal(empty_actor.GetMapper(), None)
# Test mask
mask = np.ones(mevals.shape[:3])
mask[:2, :3, :3] = 0
cfa = color_fa(fractional_anisotropy(mevals), mevecs)
tensor_actor = actor.tensor_slicer(mevals, mevecs, affine=affine, mask=mask,
scalar_colors=cfa, sphere=sphere, scale=.3)
renderer.clear()
renderer.add(tensor_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
if interactive:
window.show(renderer, reset_camera=False)
mask_extent = renderer.GetActors().GetLastActor().GetBounds()
mask_extent_x = abs(mask_extent[1] - mask_extent[0])
npt.assert_equal(big_extent_x > mask_extent_x, True)
# test display
tensor_actor.display()
current_extent = renderer.GetActors().GetLastActor().GetBounds()
current_extent_x = abs(current_extent[1] - current_extent[0])
npt.assert_equal(big_extent_x > current_extent_x, True)
if interactive:
window.show(renderer, reset_camera=False)
tensor_actor.display(y=1)
current_extent = renderer.GetActors().GetLastActor().GetBounds()
current_extent_y = abs(current_extent[3] - current_extent[2])
big_extent_y = abs(big_extent[3] - big_extent[2])
npt.assert_equal(big_extent_y > current_extent_y, True)
if interactive:
window.show(renderer, reset_camera=False)
tensor_actor.display(z=1)
current_extent = renderer.GetActors().GetLastActor().GetBounds()
current_extent_z = abs(current_extent[5] - current_extent[4])
big_extent_z = abs(big_extent[5] - big_extent[4])
npt.assert_equal(big_extent_z > current_extent_z, True)
if interactive:
window.show(renderer, reset_camera=False)
def run(self,
input_files,
bvalues_files,
bvectors_files,
mask_files,
b0_threshold=50,
bvecs_tol=0.01,
save_metrics=[],
out_dir='',
out_tensor='tensors.nii.gz',
out_fa='fa.nii.gz',
out_ga='ga.nii.gz',
out_rgb='rgb.nii.gz',
out_md='md.nii.gz',
out_ad='ad.nii.gz',
out_rd='rd.nii.gz',
out_mode='mode.nii.gz',
out_evec='evecs.nii.gz',
out_eval='evals.nii.gz',
nifti_tensor=True):
""" Workflow for tensor reconstruction and for computing DTI metrics.
using Weighted Least-Squares.
Performs a tensor reconstruction on the files by 'globing'
``input_files`` and saves the DTI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once.
b0_threshold : float, optional
Threshold used to find b0 volumes.
bvecs_tol : float, optional
Threshold used to check that norm(bvec) = 1 +/- bvecs_tol
b-vectors are unit vectors.
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
out_dir : string, optional
Output directory. (default current directory)
out_tensor : string, optional
Name of the tensors volume to be saved.
Per default, this will be saved following the nifti standard:
with the tensor elements as Dxx, Dxy, Dyy, Dxz, Dyz, Dzz on the
last (5th) dimension of the volume (shape: (i, j, k, 1, 6)). If
`nifti_tensor` is False, this will be saved in an alternate format
that is used by other software (e.g., FSL): a
4-dimensional volume (shape (i, j, k, 6)) with Dxx, Dxy, Dxz, Dyy,
Dyz, Dzz on the last dimension.
out_fa : string, optional
Name of the fractional anisotropy volume to be saved.
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved.
out_rgb : string, optional
Name of the color fa volume to be saved.
out_md : string, optional
Name of the mean diffusivity volume to be saved.
out_ad : string, optional
Name of the axial diffusivity volume to be saved.
out_rd : string, optional
Name of the radial diffusivity volume to be saved.
out_mode : string, optional
Name of the mode volume to be saved.
out_evec : string, optional
Name of the eigenvectors volume to be saved.
out_eval : string, optional
Name of the eigenvalues to be saved.
nifti_tensor : bool, optional
Whether the tensor is saved in the standard Nifti format or in an
alternate format
that is used by other software (e.g., FSL): a
4-dimensional volume (shape (i, j, k, 6)) with
Dxx, Dxy, Dxz, Dyy, Dyz, Dzz on the last dimension.
References
----------
.. [1] Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of
the effective self-diffusion tensor from the NMR spin echo. J Magn
Reson B 103, 247-254.
.. [2] Basser, P., Pierpaoli, C., 1996. Microstructural and
physiological features of tissues elucidated by quantitative
diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219.
.. [3] Lin-Ching C., Jones D.K., Pierpaoli, C. 2005. RESTORE: Robust
estimation of tensors by outlier rejection. MRM 53: 1088-1095
.. [4] hung, SW., Lu, Y., Henry, R.G., 2006. Comparison of bootstrap
approaches for estimation of uncertainties of DTI parameters.
NeuroImage 33, 531-541.
"""
io_it = self.get_io_iterator()
for dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad, \
omode, oevecs, oevals in io_it:
logging.info('Computing DTI metrics for {0}'.format(dwi))
data, affine = load_nifti(dwi)
if mask is not None:
mask = load_nifti_data(mask).astype(bool)
tenfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold, bvecs_tol)
if not save_metrics:
save_metrics = [
'fa', 'md', 'rd', 'ad', 'ga', 'rgb', 'mode', 'evec',
'eval', 'tensor'
]
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'tensor' in save_metrics:
tensor_vals = lower_triangular(tenfit.quadratic_form)
if nifti_tensor:
ten_img = nifti1_symmat(tensor_vals, affine=affine)
else:
alt_order = [0, 1, 3, 2, 4, 5]
ten_img = nib.Nifti1Image(
tensor_vals[..., alt_order].astype(np.float32), affine)
nib.save(ten_img, otensor)
if 'fa' in save_metrics:
save_nifti(ofa, FA.astype(np.float32), affine)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(tenfit.evals)
save_nifti(oga, GA.astype(np.float32), affine)
if 'rgb' in save_metrics:
RGB = color_fa(FA, tenfit.evecs)
save_nifti(orgb, np.array(255 * RGB, 'uint8'), affine)
if 'md' in save_metrics:
MD = mean_diffusivity(tenfit.evals)
save_nifti(omd, MD.astype(np.float32), affine)
if 'ad' in save_metrics:
AD = axial_diffusivity(tenfit.evals)
save_nifti(oad, AD.astype(np.float32), affine)
if 'rd' in save_metrics:
RD = radial_diffusivity(tenfit.evals)
save_nifti(orad, RD.astype(np.float32), affine)
if 'mode' in save_metrics:
MODE = get_mode(tenfit.quadratic_form)
save_nifti(omode, MODE.astype(np.float32), affine)
if 'evec' in save_metrics:
save_nifti(oevecs, tenfit.evecs.astype(np.float32), affine)
if 'eval' in save_metrics:
save_nifti(oevals, tenfit.evals.astype(np.float32), affine)
dname_ = os.path.dirname(oevals)
if dname_ == '':
logging.info('DTI metrics saved in current directory')
else:
logging.info('DTI metrics saved in {0}'.format(dname_))
def main():
parser = _build_args_parser()
args = parser.parse_args()
if not args.not_all:
args.fa = args.fa or 'fa.nii.gz'
args.ga = args.ga or 'ga.nii.gz'
args.rgb = args.rgb or 'rgb.nii.gz'
args.md = args.md or 'md.nii.gz'
args.ad = args.ad or 'ad.nii.gz'
args.rd = args.rd or 'rd.nii.gz'
args.mode = args.mode or 'mode.nii.gz'
args.norm = args.norm or 'tensor_norm.nii.gz'
args.tensor = args.tensor or 'tensor.nii.gz'
args.evecs = args.evecs or 'tensor_evecs.nii.gz'
args.evals = args.evals or 'tensor_evals.nii.gz'
args.residual = args.residual or 'dti_residual.nii.gz'
args.p_i_signal =\
args.p_i_signal or 'physically_implausible_signals_mask.nii.gz'
args.pulsation = args.pulsation or 'pulsation_and_misalignment.nii.gz'
outputs = [args.fa, args.ga, args.rgb, args.md, args.ad, args.rd,
args.mode, args.norm, args.tensor, args.evecs, args.evals,
args.residual, args.p_i_signal, args.pulsation]
if args.not_all and not any(outputs):
parser.error('When using --not_all, you need to specify at least ' +
'one metric to output.')
assert_inputs_exist(
parser, [args.input, args.bvals, args.bvecs], [args.mask])
assert_outputs_exists(parser, args, outputs)
img = nib.load(args.input)
data = img.get_data()
affine = img.get_affine()
if args.mask is None:
mask = None
else:
mask = nib.load(args.mask).get_data().astype(np.bool)
# Validate bvals and bvecs
logging.info('Tensor estimation with the %s method...', args.method)
bvals, bvecs = read_bvals_bvecs(args.bvals, args.bvecs)
if not is_normalized_bvecs(bvecs):
logging.warning('Your b-vectors do not seem normalized...')
bvecs = normalize_bvecs(bvecs)
check_b0_threshold(args, bvals.min())
gtab = gradient_table(bvals, bvecs, b0_threshold=bvals.min())
# Get tensors
if args.method == 'restore':
sigma = ne.estimate_sigma(data)
tenmodel = TensorModel(gtab, fit_method=args.method, sigma=sigma,
min_signal=_get_min_nonzero_signal(data))
else:
tenmodel = TensorModel(gtab, fit_method=args.method,
min_signal=_get_min_nonzero_signal(data))
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if args.tensor:
# Get the Tensor values and format them for visualisation
# in the Fibernavigator.
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(
tensor_vals_reordered.astype(np.float32), affine)
nib.save(fiber_tensors, args.tensor)
if args.fa:
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, args.fa)
if args.ga:
GA = geodesic_anisotropy(tenfit.evals)
GA[np.isnan(GA)] = 0
ga_img = nib.Nifti1Image(GA.astype(np.float32), affine)
nib.save(ga_img, args.ga)
if args.rgb:
RGB = color_fa(FA, tenfit.evecs)
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, args.rgb)
if args.md:
MD = mean_diffusivity(tenfit.evals)
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, args.md)
if args.ad:
AD = axial_diffusivity(tenfit.evals)
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, args.ad)
if args.rd:
RD = radial_diffusivity(tenfit.evals)
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, args.rd)
if args.mode:
# Compute tensor mode
inter_mode = dipy_mode(tenfit.quadratic_form)
# Since the mode computation can generate NANs when not masked,
# we need to remove them.
non_nan_indices = np.isfinite(inter_mode)
mode = np.zeros(inter_mode.shape)
mode[non_nan_indices] = inter_mode[non_nan_indices]
mode_img = nib.Nifti1Image(mode.astype(np.float32), affine)
nib.save(mode_img, args.mode)
if args.norm:
NORM = norm(tenfit.quadratic_form)
norm_img = nib.Nifti1Image(NORM.astype(np.float32), affine)
nib.save(norm_img, args.norm)
if args.evecs:
evecs = tenfit.evecs.astype(np.float32)
evecs_img = nib.Nifti1Image(evecs, affine)
nib.save(evecs_img, args.evecs)
# save individual e-vectors also
e1_img = nib.Nifti1Image(evecs[..., 0], affine)
e2_img = nib.Nifti1Image(evecs[..., 1], affine)
e3_img = nib.Nifti1Image(evecs[..., 2], affine)
nib.save(e1_img, add_filename_suffix(args.evecs, '_v1'))
nib.save(e2_img, add_filename_suffix(args.evecs, '_v2'))
nib.save(e3_img, add_filename_suffix(args.evecs, '_v3'))
if args.evals:
evals = tenfit.evals.astype(np.float32)
evals_img = nib.Nifti1Image(evals, affine)
nib.save(evals_img, args.evals)
# save individual e-values also
e1_img = nib.Nifti1Image(evals[..., 0], affine)
e2_img = nib.Nifti1Image(evals[..., 1], affine)
e3_img = nib.Nifti1Image(evals[..., 2], affine)
nib.save(e1_img, add_filename_suffix(args.evals, '_e1'))
nib.save(e2_img, add_filename_suffix(args.evals, '_e2'))
nib.save(e3_img, add_filename_suffix(args.evals, '_e3'))
if args.p_i_signal:
S0 = np.mean(data[..., gtab.b0s_mask], axis=-1, keepdims=True)
DWI = data[..., ~gtab.b0s_mask]
pis_mask = np.max(S0 < DWI, axis=-1)
if args.mask is not None:
pis_mask *= mask
pis_img = nib.Nifti1Image(pis_mask.astype(np.int16), affine)
nib.save(pis_img, args.p_i_signal)
if args.pulsation:
STD = np.std(data[..., ~gtab.b0s_mask], axis=-1)
if args.mask is not None:
STD *= mask
std_img = nib.Nifti1Image(STD.astype(np.float32), affine)
nib.save(std_img, add_filename_suffix(args.pulsation, '_std_dwi'))
if np.sum(gtab.b0s_mask) <= 1:
logger.info('Not enough b=0 images to output standard '
'deviation map')
else:
if len(np.where(gtab.b0s_mask)) == 2:
logger.info('Only two b=0 images. Be careful with the '
'interpretation of this std map')
STD = np.std(data[..., gtab.b0s_mask], axis=-1)
if args.mask is not None:
STD *= mask
std_img = nib.Nifti1Image(STD.astype(np.float32), affine)
nib.save(std_img, add_filename_suffix(args.pulsation, '_std_b0'))
if args.residual:
if args.mask is None:
logger.info("Outlier detection will not be performed, since no "
"mask was provided.")
S0 = np.mean(data[..., gtab.b0s_mask], axis=-1)
data_p = tenfit.predict(gtab, S0)
R = np.mean(np.abs(data_p[..., ~gtab.b0s_mask] -
data[..., ~gtab.b0s_mask]), axis=-1)
if args.mask is not None:
R *= mask
R_img = nib.Nifti1Image(R.astype(np.float32), affine)
nib.save(R_img, args.residual)
R_k = np.zeros(data.shape[-1]) # mean residual per DWI
std = np.zeros(data.shape[-1]) # std residual per DWI
q1 = np.zeros(data.shape[-1]) # first quartile
q3 = np.zeros(data.shape[-1]) # third quartile
iqr = np.zeros(data.shape[-1]) # interquartile
for i in range(data.shape[-1]):
x = np.abs(data_p[..., i] - data[..., i])[mask]
R_k[i] = np.mean(x)
std[i] = np.std(x)
q3[i], q1[i] = np.percentile(x, [75, 25])
iqr[i] = q3[i] - q1[i]
# Outliers are observations that fall below Q1 - 1.5(IQR) or
# above Q3 + 1.5(IQR) We check if a volume is an outlier only if
# we have a mask, else we are biased.
if args.mask is not None and R_k[i] < (q1[i] - 1.5 * iqr[i]) \
or R_k[i] > (q3[i] + 1.5 * iqr[i]):
logger.warning('WARNING: Diffusion-Weighted Image i=%s is an '
'outlier', i)
residual_basename, _ = split_name_with_nii(args.residual)
res_stats_basename = residual_basename + ".npy"
np.save(add_filename_suffix(
res_stats_basename, "_mean_residuals"), R_k)
np.save(add_filename_suffix(res_stats_basename, "_q1_residuals"), q1)
np.save(add_filename_suffix(res_stats_basename, "_q3_residuals"), q3)
np.save(add_filename_suffix(res_stats_basename, "_iqr_residuals"), iqr)
np.save(add_filename_suffix(res_stats_basename, "_std_residuals"), std)
# To do: I would like to have an error bar with q1 and q3.
# Now, q1 acts as a std
dwi = np.arange(R_k[~gtab.b0s_mask].shape[0])
plt.bar(dwi, R_k[~gtab.b0s_mask], 0.75,
color='y', yerr=q1[~gtab.b0s_mask])
plt.xlabel('DW image')
plt.ylabel('Mean residuals +- q1')
plt.title('Residuals')
plt.savefig(residual_basename + '_residuals_stats.png')
"""
The other is to call the ``TensorFit`` class method:
"""
MD2 = tenfit.md
"""
Obviously, the quantities are identical.
We can also compute the colored FA or RGB-map [Pajevic1999]_. First, we make sure
that the FA is scaled between 0 and 1, we compute the RGB map and save it.
"""
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tenfit.evecs)
nib.save(nib.Nifti1Image(np.array(255 * RGB, 'uint8'), img.affine), 'tensor_rgb.nii.gz')
"""
Let's try to visualize the tensor ellipsoids of a small rectangular
area in an axial slice of the splenium of the corpus callosum (CC).
"""
print('Computing tensor ellipsoids in a part of the splenium of the CC')
from dipy.data import get_sphere
sphere = get_sphere('repulsion724')
from dipy.viz import window, actor
# Enables/disables interactive visualization
def nonlinfit_fn(dwi, bvecs, bvals, base_name):
import nibabel as nb
import numpy as np
import os.path as op
import dipy.reconst.dti as dti
from dipy.core.gradients import GradientTable
dwi_img = nb.load(dwi)
dwi_data = dwi_img.get_data()
dwi_affine = dwi_img.get_affine()
from dipy.segment.mask import median_otsu
b0_mask, mask = median_otsu(dwi_data, 2, 4)
# Mask the data so that tensors are not fit for
# unnecessary voxels
mask_img = nb.Nifti1Image(mask.astype(np.float32), dwi_affine)
b0_imgs = nb.Nifti1Image(b0_mask.astype(np.float32), dwi_affine)
b0_img = nb.four_to_three(b0_imgs)[0]
out_mask_name = op.abspath(base_name + '_binary_mask.nii.gz')
out_b0_name = op.abspath(base_name + '_b0_mask.nii.gz')
nb.save(mask_img, out_mask_name)
nb.save(b0_img, out_b0_name)
# Load the gradient strengths and directions
bvals = np.loadtxt(bvals)
gradients = np.loadtxt(bvecs)
# Dipy wants Nx3 arrays
if gradients.shape[0] == 3:
gradients = gradients.T
assert(gradients.shape[1] == 3)
# Place in Dipy's preferred format
gtab = GradientTable(gradients)
gtab.bvals = bvals
# Fit the tensors to the data
tenmodel = dti.TensorModel(gtab, fit_method="NLLS")
tenfit = tenmodel.fit(dwi_data, mask)
# Calculate the fit, fa, and md of each voxel's tensor
tensor_data = tenfit.lower_triangular()
print('Computing anisotropy measures (FA, MD, RGB)')
from dipy.reconst.dti import fractional_anisotropy, color_fa
evals = tenfit.evals.astype(np.float32)
FA = fractional_anisotropy(np.abs(evals))
FA = np.clip(FA, 0, 1)
MD = dti.mean_diffusivity(np.abs(evals))
norm = dti.norm(tenfit.quadratic_form)
RGB = color_fa(FA, tenfit.evecs)
evecs = tenfit.evecs.astype(np.float32)
mode = tenfit.mode.astype(np.float32)
mode = np.nan_to_num(mode)
# Write tensor as a 4D Nifti image with the original affine
tensor_fit_img = nb.Nifti1Image(tensor_data.astype(np.float32), dwi_affine)
mode_img = nb.Nifti1Image(mode.astype(np.float32), dwi_affine)
norm_img = nb.Nifti1Image(norm.astype(np.float32), dwi_affine)
FA_img = nb.Nifti1Image(FA.astype(np.float32), dwi_affine)
evecs_img = nb.Nifti1Image(evecs, dwi_affine)
evals_img = nb.Nifti1Image(evals, dwi_affine)
rgb_img = nb.Nifti1Image(np.array(255 * RGB, 'uint8'), dwi_affine)
MD_img = nb.Nifti1Image(MD.astype(np.float32), dwi_affine)
out_tensor_file = op.abspath(base_name + "_tensor.nii.gz")
out_mode_file = op.abspath(base_name + "_mode.nii.gz")
out_fa_file = op.abspath(base_name + "_fa.nii.gz")
out_norm_file = op.abspath(base_name + "_norm.nii.gz")
out_evals_file = op.abspath(base_name + "_evals.nii.gz")
out_evecs_file = op.abspath(base_name + "_evecs.nii.gz")
out_rgb_fa_file = op.abspath(base_name + "_rgb_fa.nii.gz")
out_md_file = op.abspath(base_name + "_md.nii.gz")
nb.save(rgb_img, out_rgb_fa_file)
nb.save(norm_img, out_norm_file)
nb.save(mode_img, out_mode_file)
nb.save(tensor_fit_img, out_tensor_file)
nb.save(evecs_img, out_evecs_file)
nb.save(evals_img, out_evals_file)
nb.save(FA_img, out_fa_file)
nb.save(MD_img, out_md_file)
print('Tensor fit image saved as {i}'.format(i=out_tensor_file))
print('FA image saved as {i}'.format(i=out_fa_file))
print('MD image saved as {i}'.format(i=out_md_file))
return out_tensor_file, out_fa_file, out_md_file, \
out_evecs_file, out_evals_file, out_rgb_fa_file, out_norm_file, \
out_mode_file, out_mask_name, out_b0_name
img = nib.load(fimg)
data = img.get_data()
affine = img.get_affine()
header = img.get_header()
voxel_size = header.get_zooms()[:3]
mask, S0_mask = median_otsu(data[:, :, :, 0])
fbval = "original.bval"
fbvec = "original.bvec"
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
ten_model = TensorModel(gtab)
ten_fit = ten_model.fit(data, mask)
fa = fractional_anisotropy(ten_fit.evals)
cfa = color_fa(fa, ten_fit.evecs)
csamodel = CsaOdfModel(gtab, 6)
sphere = get_sphere('symmetric724')
pmd = peaks_from_model(model=csamodel,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_odf=False)
#Deterministic tractography
eu = EuDX(a=fa,
ind=pmd.peak_indices[..., 0],
seeds=2000000,
odf_vertices=sphere.vertices,
def _tensor_slicer_mapper(evals,
evecs,
affine=None,
mask=None,
sphere=None,
scale=2.2,
norm=True,
opacity=1.,
scalar_colors=None):
""" Helper function for slicing tensor fields
Parameters
----------
evals : (3,) or (X, 3) or (X, Y, 3) or (X, Y, Z, 3) ndarray
eigenvalues
evecs : (3, 3) or (X, 3, 3) or (X, Y, 3, 3) or (X, Y, Z, 3, 3) ndarray
eigenvectors
affine : array
4x4 transformation array from native coordinates to world coordinates
mask : ndarray
3D mask
sphere : Sphere
a sphere
scale : float
Distance between spheres.
norm : bool
Normalize `sphere_values`.
opacity : float
Takes values from 0 (fully transparent) to 1 (opaque)
scalar_colors : (3,) or (X, 3) or (X, Y, 3) or (X, Y, Z, 3) ndarray
RGB colors used to show the tensors
Default None, color the ellipsoids using ``color_fa``
Returns
---------
mapper : vtkPolyDataMapper
Ellipsoid mapper
"""
if mask is None:
mask = np.ones(evals.shape[:3])
ijk = np.ascontiguousarray(np.array(np.nonzero(mask)).T)
if len(ijk) == 0:
return None
if affine is not None:
ijk = np.ascontiguousarray(apply_affine(affine, ijk))
faces = np.asarray(sphere.faces, dtype=int)
vertices = sphere.vertices
if scalar_colors is None:
from dipy.reconst.dti import color_fa, fractional_anisotropy
cfa = color_fa(fractional_anisotropy(evals), evecs)
else:
cfa = _makeNd(scalar_colors, 4)
cols = np.zeros((ijk.shape[0], ) + sphere.vertices.shape, dtype='f4')
all_xyz = []
all_faces = []
for (k, center) in enumerate(ijk):
ea = evals[tuple(center.astype(np.int))]
if norm:
ea /= ea.max()
ea = np.diag(ea.copy())
ev = evecs[tuple(center.astype(np.int))].copy()
xyz = np.dot(ev, np.dot(ea, vertices.T))
xyz = xyz.T
all_xyz.append(scale * xyz + center)
all_faces.append(faces + k * xyz.shape[0])
cols[k, ...] = np.interp(cfa[tuple(center.astype(np.int))], [0, 1],
[0, 255]).astype('ubyte')
all_xyz = np.ascontiguousarray(np.concatenate(all_xyz))
all_xyz_vtk = numpy_support.numpy_to_vtk(all_xyz, deep=True)
all_faces = np.concatenate(all_faces)
all_faces = np.hstack((3 * np.ones((len(all_faces), 1)), all_faces))
ncells = len(all_faces)
all_faces = np.ascontiguousarray(all_faces.ravel(), dtype='i8')
all_faces_vtk = numpy_support.numpy_to_vtkIdTypeArray(all_faces, deep=True)
points = vtk.vtkPoints()
points.SetData(all_xyz_vtk)
cells = vtk.vtkCellArray()
cells.SetCells(ncells, all_faces_vtk)
cols = np.ascontiguousarray(np.reshape(
cols, (cols.shape[0] * cols.shape[1], cols.shape[2])),
dtype='f4')
vtk_colors = numpy_support.numpy_to_vtk(cols,
deep=True,
array_type=vtk.VTK_UNSIGNED_CHAR)
vtk_colors.SetName("Colors")
polydata = vtk.vtkPolyData()
polydata.SetPoints(points)
polydata.SetPolys(cells)
polydata.GetPointData().SetScalars(vtk_colors)
mapper = vtk.vtkPolyDataMapper()
if major_version <= 5:
mapper.SetInput(polydata)
else:
mapper.SetInputData(polydata)
return mapper
def tensor(evals, evecs, scalar_colors=None,
sphere=None, scale=2.2, norm=True):
"""Plot many tensors as ellipsoids simultaneously.
Parameters
----------
evals : (3,) or (X, 3) or (X, Y, 3) or (X, Y, Z, 3) ndarray
eigenvalues
evecs : (3, 3) or (X, 3, 3) or (X, Y, 3, 3) or (X, Y, Z, 3, 3) ndarray
eigenvectors
scalar_colors : (3,) or (X, 3) or (X, Y, 3) or (X, Y, Z, 3) ndarray
RGB colors used to show the tensors
Default None, color the ellipsoids using ``color_fa``
sphere : Sphere,
this sphere will be transformed to the tensor ellipsoid
Default is None which uses a symmetric sphere with 724 points.
scale : float,
distance between ellipsoids.
norm : boolean,
Normalize `evals`.
Returns
-------
actor : vtkActor
Ellipsoids
Examples
--------
>>> from dipy.viz import fvtk
>>> r = fvtk.ren()
>>> evals = np.array([1.4, .35, .35]) * 10 ** (-3)
>>> evecs = np.eye(3)
>>> from dipy.data import get_sphere
>>> sphere = get_sphere('symmetric724')
>>> fvtk.add(r, fvtk.tensor(evals, evecs, sphere=sphere))
>>> #fvtk.show(r)
"""
evals = np.asarray(evals)
if evals.ndim > 4:
raise ValueError("Wrong shape")
evals = _makeNd(evals, 4)
evecs = _makeNd(evecs, 5)
grid_shape = np.array(evals.shape[:3])
if sphere is None:
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
faces = np.asarray(sphere.faces, dtype=int)
vertices = sphere.vertices
colors = vtk.vtkUnsignedCharArray()
colors.SetNumberOfComponents(3)
colors.SetName("Colors")
if scalar_colors is None:
from dipy.reconst.dti import color_fa, fractional_anisotropy
cfa = color_fa(fractional_anisotropy(evals), evecs)
else:
cfa = _makeNd(scalar_colors, 4)
list_sq = []
list_cols = []
for ijk in ndindex(grid_shape):
ea = evals[ijk]
if norm:
ea /= ea.max()
ea = np.diag(ea.copy())
ev = evecs[ijk].copy()
xyz = np.dot(ev, np.dot(ea, vertices.T))
xyz += scale * (ijk - grid_shape / 2.)[:, None]
xyz = xyz.T
list_sq.append(xyz)
acolor = np.zeros(xyz.shape)
acolor[:, :] = np.interp(cfa[ijk], [0, 1], [0, 255])
list_cols.append(acolor.astype('ubyte'))
points = vtk.vtkPoints()
triangles = vtk.vtkCellArray()
for k in xrange(len(list_sq)):
xyz = list_sq[k]
cols = list_cols[k]
for i in xrange(xyz.shape[0]):
points.InsertNextPoint(*xyz[i])
colors.InsertNextTuple3(*cols[i])
for j in xrange(faces.shape[0]):
triangle = vtk.vtkTriangle()
triangle.GetPointIds().SetId(0, faces[j, 0] + k * xyz.shape[0])
triangle.GetPointIds().SetId(1, faces[j, 1] + k * xyz.shape[0])
triangle.GetPointIds().SetId(2, faces[j, 2] + k * xyz.shape[0])
triangles.InsertNextCell(triangle)
del triangle
polydata = vtk.vtkPolyData()
polydata.SetPoints(points)
polydata.SetPolys(triangles)
polydata.GetPointData().SetScalars(colors)
polydata.Modified()
mapper = vtk.vtkPolyDataMapper()
if major_version <= 5:
mapper.SetInput(polydata)
else:
mapper.SetInputData(polydata)
actor = vtk.vtkActor()
actor.SetMapper(mapper)
return actor
from dipy.io import read_bvals_bvecs
bvals,bvecs = read_bvals_bvecs(fbval,fbvec)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals,bvecs)
from dipy.reconst.dti import TensorModel
ten = TensorModel(gtab)
tenfit = ten.fit(data)
from dipy.reconst.dti import fractional_anisotropy
fa = fractional_anisotropy(tenfit.evals)
from dipy.reconst.dti import color_fa
cfa = color_fa(fa,tenfit.evecs)
#save FA to image
nib.save(nib.Nifti1Image(np.array(255*cfa,'uint8'),img.get_affine()),'tensor_rgb.nii.gz')
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
from dipy.viz import fvtk
ren = fvtk.ren()
evals=tenfit.evals[20:50,55:85,38:39]
evecs=tenfit.evecs[20:50,55:85,38:39]
print "Hello World!"
def run(self,
input_files,
bvalues_files,
bvectors_files,
mask_files,
b0_threshold=0.0,
bvecs_tol=0.01,
save_metrics=[],
out_dir='',
out_tensor='tensors.nii.gz',
out_fa='fa.nii.gz',
out_ga='ga.nii.gz',
out_rgb='rgb.nii.gz',
out_md='md.nii.gz',
out_ad='ad.nii.gz',
out_rd='rd.nii.gz',
out_mode='mode.nii.gz',
out_evec='evecs.nii.gz',
out_eval='evals.nii.gz'):
""" Workflow for tensor reconstruction and for computing DTI metrics.
using Weighted Least-Squares.
Performs a tensor reconstruction on the files by 'globing'
``input_files`` and saves the DTI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions (default 0.0)
bvecs_tol : float, optional
Threshold used to check that norm(bvec) = 1 +/- bvecs_tol
b-vectors are unit vectors (default 0.01)
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
(default [] (all))
out_dir : string, optional
Output directory (default input file directory)
out_tensor : string, optional
Name of the tensors volume to be saved (default 'tensors.nii.gz')
out_fa : string, optional
Name of the fractional anisotropy volume to be saved
(default 'fa.nii.gz')
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved
(default 'ga.nii.gz')
out_rgb : string, optional
Name of the color fa volume to be saved (default 'rgb.nii.gz')
out_md : string, optional
Name of the mean diffusivity volume to be saved
(default 'md.nii.gz')
out_ad : string, optional
Name of the axial diffusivity volume to be saved
(default 'ad.nii.gz')
out_rd : string, optional
Name of the radial diffusivity volume to be saved
(default 'rd.nii.gz')
out_mode : string, optional
Name of the mode volume to be saved (default 'mode.nii.gz')
out_evec : string, optional
Name of the eigenvectors volume to be saved
(default 'evecs.nii.gz')
out_eval : string, optional
Name of the eigenvalues to be saved (default 'evals.nii.gz')
References
----------
.. [1] Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of
the effective self-diffusion tensor from the NMR spin echo. J Magn
Reson B 103, 247-254.
.. [2] Basser, P., Pierpaoli, C., 1996. Microstructural and
physiological features of tissues elucidated by quantitative
diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219.
.. [3] Lin-Ching C., Jones D.K., Pierpaoli, C. 2005. RESTORE: Robust
estimation of tensors by outlier rejection. MRM 53: 1088-1095
.. [4] hung, SW., Lu, Y., Henry, R.G., 2006. Comparison of bootstrap
approaches for estimation of uncertainties of DTI parameters.
NeuroImage 33, 531-541.
"""
io_it = self.get_io_iterator()
for dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad, \
omode, oevecs, oevals in io_it:
logging.info('Computing DTI metrics for {0}'.format(dwi))
img = nib.load(dwi)
data = img.get_data()
affine = img.affine
if mask is not None:
mask = nib.load(mask).get_data().astype(np.bool)
tenfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold, bvecs_tol)
if not save_metrics:
save_metrics = [
'fa', 'md', 'rd', 'ad', 'ga', 'rgb', 'mode', 'evec',
'eval', 'tensor'
]
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'tensor' in save_metrics:
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(
tensor_vals_reordered.astype(np.float32), affine)
nib.save(fiber_tensors, otensor)
if 'fa' in save_metrics:
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, ofa)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(tenfit.evals)
ga_img = nib.Nifti1Image(GA.astype(np.float32), affine)
nib.save(ga_img, oga)
if 'rgb' in save_metrics:
RGB = color_fa(FA, tenfit.evecs)
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, orgb)
if 'md' in save_metrics:
MD = mean_diffusivity(tenfit.evals)
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, omd)
if 'ad' in save_metrics:
AD = axial_diffusivity(tenfit.evals)
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, oad)
if 'rd' in save_metrics:
RD = radial_diffusivity(tenfit.evals)
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, orad)
if 'mode' in save_metrics:
MODE = get_mode(tenfit.quadratic_form)
mode_img = nib.Nifti1Image(MODE.astype(np.float32), affine)
nib.save(mode_img, omode)
if 'evec' in save_metrics:
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32),
affine)
nib.save(evecs_img, oevecs)
if 'eval' in save_metrics:
evals_img = nib.Nifti1Image(tenfit.evals.astype(np.float32),
affine)
nib.save(evals_img, oevals)
dname_ = os.path.dirname(oevals)
if dname_ == '':
logging.info('DTI metrics saved in current directory')
else:
logging.info('DTI metrics saved in {0}'.format(dname_))
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
from dipy.reconst.dti import TensorModel
ten = TensorModel(gtab)
tenfit = ten.fit(data, mask)
from dipy.reconst.dti import fractional_anisotropy
fa = fractional_anisotropy(tenfit.evals)
fa[np.isnan(fa)] = 0
from dipy.reconst.dti import color_fa
Rgbv = color_fa(fa, tenfit.evecs)
fa = np.clip(fa, 0, 1)
#save FA to image
nib.save(nib.Nifti1Image(np.array(255 * Rgbv, 'uint8'), img.get_affine()),
'tensor_rgb.nii.gz')
import matplotlib.pyplot as plt
axial_middle = data.shape[2] / 2
plt.figure('Showing the datasets')
plt.subplot(1, 2, 1).set_axis_off()
plt.imshow(data[:, :, axial_middle, 0].T, cmap='gray', origin='lower')
plt.subplot(1, 2, 2).set_axis_off()
plt.imshow(data[:, :, axial_middle, 10].T, cmap='gray', origin='lower')
shutil.move('output_fa.nii', args.output_nifti1_fa)
move_directory_files(input_dir, args.output_nifti1_fa_files_path, copy=True)
evecs_img = nibabel.Nifti1Image(tenfit.evecs.astype(numpy.float32), img.affine)
nibabel.save(evecs_img, 'output_evecs.nii')
shutil.move('output_evecs.nii', args.output_nifti1_evecs)
move_directory_files(input_dir, args.output_nifti1_evecs_files_path, copy=True)
md1 = dti.mean_diffusivity(tenfit.evals)
nibabel.save(nibabel.Nifti1Image(md1.astype(numpy.float32), img.affine),
'output_md.nii')
shutil.move('output_md.nii', args.output_nifti1_md)
move_directory_files(input_dir, args.output_nifti1_md_files_path, copy=True)
fa = numpy.clip(fa, 0, 1)
rgb = color_fa(fa, tenfit.evecs)
nibabel.save(nibabel.Nifti1Image(numpy.array(255 * rgb, 'uint8'), img.affine),
'output_rgb.nii')
shutil.move('output_rgb.nii', args.output_nifti1_rgb)
move_directory_files(input_dir, args.output_nifti1_rgb_files_path, copy=True)
sphere = get_sphere('symmetric724')
ren = fvtk.ren()
evals = tenfit.evals[13:43, 44:74, 28:29]
evecs = tenfit.evecs[13:43, 44:74, 28:29]
cfa = rgb[13:43, 44:74, 28:29]
cfa /= cfa.max()
fvtk.add(ren, fvtk.tensor(evals, evecs, cfa, sphere))
fvtk.record(ren, n_frames=1, out_path='tensor_ellipsoids.png', size=(600, 600))
shutil.move('tensor_ellipsoids.png', args.output_png_ellipsoids)
def test_tensor_slicer(interactive=False):
evals = np.array([1.4, .35, .35]) * 10 ** (-3)
evecs = np.eye(3)
mevals = np.zeros((3, 2, 4, 3))
mevecs = np.zeros((3, 2, 4, 3, 3))
mevals[..., :] = evals
mevecs[..., :, :] = evecs
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
affine = np.eye(4)
renderer = window.Renderer()
tensor_actor = actor.tensor_slicer(mevals, mevecs, affine=affine,
sphere=sphere, scale=.3)
I, J, K = mevals.shape[:3]
renderer.add(tensor_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
tensor_actor.display_extent(0, 1, 0, J, 0, K)
tensor_actor.GetProperty().SetOpacity(1.0)
if interactive:
window.show(renderer, reset_camera=False)
npt.assert_equal(renderer.GetActors().GetNumberOfItems(), 1)
# Test extent
big_extent = renderer.GetActors().GetLastActor().GetBounds()
big_extent_x = abs(big_extent[1] - big_extent[0])
tensor_actor.display(x=2)
if interactive:
window.show(renderer, reset_camera=False)
small_extent = renderer.GetActors().GetLastActor().GetBounds()
small_extent_x = abs(small_extent[1] - small_extent[0])
npt.assert_equal(big_extent_x > small_extent_x, True)
# Test empty mask
empty_actor = actor.tensor_slicer(mevals, mevecs, affine=affine,
mask=np.zeros(mevals.shape[:3]),
sphere=sphere, scale=.3)
npt.assert_equal(empty_actor.GetMapper(), None)
# Test mask
mask = np.ones(mevals.shape[:3])
mask[:2, :3, :3] = 0
cfa = color_fa(fractional_anisotropy(mevals), mevecs)
tensor_actor = actor.tensor_slicer(mevals, mevecs, affine=affine,
mask=mask, scalar_colors=cfa,
sphere=sphere, scale=.3)
renderer.clear()
renderer.add(tensor_actor)
renderer.reset_camera()
renderer.reset_clipping_range()
if interactive:
window.show(renderer, reset_camera=False)
mask_extent = renderer.GetActors().GetLastActor().GetBounds()
mask_extent_x = abs(mask_extent[1] - mask_extent[0])
npt.assert_equal(big_extent_x > mask_extent_x, True)
# test display
tensor_actor.display()
current_extent = renderer.GetActors().GetLastActor().GetBounds()
current_extent_x = abs(current_extent[1] - current_extent[0])
npt.assert_equal(big_extent_x > current_extent_x, True)
if interactive:
window.show(renderer, reset_camera=False)
tensor_actor.display(y=1)
current_extent = renderer.GetActors().GetLastActor().GetBounds()
current_extent_y = abs(current_extent[3] - current_extent[2])
big_extent_y = abs(big_extent[3] - big_extent[2])
npt.assert_equal(big_extent_y > current_extent_y, True)
if interactive:
window.show(renderer, reset_camera=False)
tensor_actor.display(z=1)
current_extent = renderer.GetActors().GetLastActor().GetBounds()
current_extent_z = abs(current_extent[5] - current_extent[4])
big_extent_z = abs(big_extent[5] - big_extent[4])
npt.assert_equal(big_extent_z > current_extent_z, True)
if interactive:
window.show(renderer, reset_camera=False)
# Test error handling of the method when
# incompatible dimension of mevals and evecs are passed.
mevals = np.zeros((3, 2, 3))
mevecs = np.zeros((3, 2, 4, 3, 3))
with npt.assert_raises(RuntimeError):
tensor_actor = actor.tensor_slicer(mevals, mevecs, affine=affine,
mask=mask, scalar_colors=cfa,
sphere=sphere, scale=.3)
def run_to_estimate_dti_maps(path_input,
path_output,
file_tensor_fitevals="",
file_tensor_fitevecs="",
fbval="",
fbvec=""):
folder = os.path.dirname(path_input)
if fbval == "" or fbvec == "":
folder_sujeto = path_output
for l in os.listdir(folder_sujeto):
if "TENSOR" in l and "bval" in l:
fbval = os.path.join(folder_sujeto, l)
if "TENSOR" in l and "bvec" in l:
fbvec = os.path.join(folder_sujeto, l)
if file_tensor_fitevals == "" or file_tensor_fitevecs == "":
for i in os.listdir(folder):
if "DTIEvals" in i:
file_tensor_fitevals = os.path.join(folder, i)
for i in os.listdir(folder):
if "DTIEvecs" in i:
file_tensor_fitevecs = os.path.join(folder, i)
if not os.path.exists(os.path.join(folder, "list_maps.txt")):
# def to_estimate_dti_maps(path_dwi_input, path_output, file_tensor_fitevecs, file_tensor_fitevals):
ref_name_only = utils.to_extract_filename(file_tensor_fitevecs)
ref_name_only = ref_name_only[:-9]
list_maps = []
img_tensorFitevecs = nib.load(file_tensor_fitevecs)
img_tensorFitevals = nib.load(file_tensor_fitevals)
evecs = img_tensorFitevecs.get_data()
evals = img_tensorFitevals.get_data()
affine = img_tensorFitevecs.affine
print(d.separador + d.separador + 'computing of FA map')
FA = fractional_anisotropy(evals)
FA[np.isnan(FA)] = 0
nib.save(
nib.Nifti1Image(FA.astype(np.float32), affine),
os.path.join(path_output, ref_name_only + '_FA' + d.extension))
list_maps.append(
os.path.join(path_output, ref_name_only + '_FA' + d.extension))
print(d.separador + d.separador + 'computing of Color FA map')
FA2 = np.clip(FA, 0, 1)
RGB = color_fa(FA2, evecs)
nib.save(
nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine),
os.path.join(path_output, ref_name_only + '_FA_RGB' + d.extension))
print(d.separador + d.separador + 'computing of MD map')
MD = dti.mean_diffusivity(evals)
nib.save(
nib.Nifti1Image(MD.astype(np.float32), affine),
os.path.join(path_output, ref_name_only + '_MD' + d.extension))
list_maps.append(
os.path.join(path_output, ref_name_only + '_MD' + d.extension))
print(d.separador + d.separador + 'computing of AD map')
AD = dti.axial_diffusivity(evals)
nib.save(
nib.Nifti1Image(AD.astype(np.float32), affine),
os.path.join(path_output, ref_name_only + '_AD' + d.extension))
list_maps.append(
os.path.join(path_output, ref_name_only + '_AD' + d.extension))
print(d.separador + d.separador + 'computing of RD map')
RD = dti.radial_diffusivity(evals)
nib.save(
nib.Nifti1Image(RD.astype(np.float32), affine),
os.path.join(path_output, ref_name_only + '_RD' + d.extension))
list_maps.append(
os.path.join(path_output, ref_name_only + '_RD' + d.extension))
sphere = get_sphere('symmetric724')
peak_indices = quantize_evecs(evecs, sphere.vertices)
eu = EuDX(FA.astype('f8'),
peak_indices,
seeds=300000,
odf_vertices=sphere.vertices,
a_low=0.15)
tensor_streamlines = [streamline for streamline in eu]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = nib.load(path_input).header.get_zooms()[:3]
hdr['voxel_order'] = 'LAS'
hdr['dim'] = FA.shape
tensor_streamlines_trk = ((sl, None, None)
for sl in tensor_streamlines)
nib.trackvis.write(os.path.join(
path_output, ref_name_only + '_tractography_EuDx.trk'),
tensor_streamlines_trk,
hdr,
points_space='voxel')
print(list_maps)
with open(os.path.join(path_output, "list_maps.txt"), "w") as f:
for s in list_maps:
f.write(str(s) + "\n")
return path_input
def _tensor_slicer_mapper(evals, evecs, affine=None, mask=None, sphere=None, scale=2.2,
norm=True, opacity=1., scalar_colors=None):
""" Helper function for slicing tensor fields
Parameters
----------
evals : (3,) or (X, 3) or (X, Y, 3) or (X, Y, Z, 3) ndarray
eigenvalues
evecs : (3, 3) or (X, 3, 3) or (X, Y, 3, 3) or (X, Y, Z, 3, 3) ndarray
eigenvectors
affine : array
4x4 transformation array from native coordinates to world coordinates
mask : ndarray
3D mask
sphere : Sphere
a sphere
scale : float
Distance between spheres.
norm : bool
Normalize `sphere_values`.
opacity : float
Takes values from 0 (fully transparent) to 1 (opaque)
scalar_colors : (3,) or (X, 3) or (X, Y, 3) or (X, Y, Z, 3) ndarray
RGB colors used to show the tensors
Default None, color the ellipsoids using ``color_fa``
Returns
---------
mapper : vtkPolyDataMapper
Ellipsoid mapper
"""
if mask is None:
mask = np.ones(evals.shape[:3])
ijk = np.ascontiguousarray(np.array(np.nonzero(mask)).T)
if len(ijk) == 0:
return None
if affine is not None:
ijk = np.ascontiguousarray(apply_affine(affine, ijk))
faces = np.asarray(sphere.faces, dtype=int)
vertices = sphere.vertices
if scalar_colors is None:
from dipy.reconst.dti import color_fa, fractional_anisotropy
cfa = color_fa(fractional_anisotropy(evals), evecs)
else:
cfa = _makeNd(scalar_colors, 4)
cols = np.zeros((ijk.shape[0],) + sphere.vertices.shape,
dtype='f4')
all_xyz = []
all_faces = []
for (k, center) in enumerate(ijk):
ea = evals[tuple(center.astype(np.int))]
if norm:
ea /= ea.max()
ea = np.diag(ea.copy())
ev = evecs[tuple(center.astype(np.int))].copy()
xyz = np.dot(ev, np.dot(ea, vertices.T))
xyz = xyz.T
all_xyz.append(scale * xyz + center)
all_faces.append(faces + k * xyz.shape[0])
cols[k, ...] = np.interp(cfa[tuple(center.astype(np.int))], [0, 1], [0, 255]).astype('ubyte')
all_xyz = np.ascontiguousarray(np.concatenate(all_xyz))
all_xyz_vtk = numpy_support.numpy_to_vtk(all_xyz, deep=True)
all_faces = np.concatenate(all_faces)
all_faces = np.hstack((3 * np.ones((len(all_faces), 1)),
all_faces))
ncells = len(all_faces)
all_faces = np.ascontiguousarray(all_faces.ravel(), dtype='i8')
all_faces_vtk = numpy_support.numpy_to_vtkIdTypeArray(all_faces,
deep=True)
points = vtk.vtkPoints()
points.SetData(all_xyz_vtk)
cells = vtk.vtkCellArray()
cells.SetCells(ncells, all_faces_vtk)
cols = np.ascontiguousarray(
np.reshape(cols, (cols.shape[0] * cols.shape[1],
cols.shape[2])), dtype='f4')
vtk_colors = numpy_support.numpy_to_vtk(
cols,
deep=True,
array_type=vtk.VTK_UNSIGNED_CHAR)
vtk_colors.SetName("Colors")
polydata = vtk.vtkPolyData()
polydata.SetPoints(points)
polydata.SetPolys(cells)
polydata.GetPointData().SetScalars(vtk_colors)
mapper = vtk.vtkPolyDataMapper()
if major_version <= 5:
mapper.SetInput(polydata)
else:
mapper.SetInputData(polydata)
return mapper
def tensor(evals, evecs, scalar_colors=None,
sphere=None, scale=2.2, norm=True):
"""Plot many tensors as ellipsoids simultaneously.
Parameters
----------
evals : (3,) or (X, 3) or (X, Y, 3) or (X, Y, Z, 3) ndarray
eigenvalues
evecs : (3, 3) or (X, 3, 3) or (X, Y, 3, 3) or (X, Y, Z, 3, 3) ndarray
eigenvectors
scalar_colors : (3,) or (X, 3) or (X, Y, 3) or (X, Y, Z, 3) ndarray
RGB colors used to show the tensors
Default None, color the ellipsoids using ``color_fa``
sphere : Sphere,
this sphere will be transformed to the tensor ellipsoid
Default is None which uses a symmetric sphere with 724 points.
scale : float,
distance between ellipsoids.
norm : boolean,
Normalize `evals`.
Returns
-------
actor : vtkActor
Ellipsoids
Examples
--------
>>> from dipy.viz import fvtk
>>> r = fvtk.ren()
>>> evals = np.array([1.4, .35, .35]) * 10 ** (-3)
>>> evecs = np.eye(3)
>>> from dipy.data import get_sphere
>>> sphere = get_sphere('symmetric724')
>>> fvtk.add(r, fvtk.tensor(evals, evecs, sphere=sphere))
>>> #fvtk.show(r)
"""
evals = np.asarray(evals)
if evals.ndim > 4:
raise ValueError("Wrong shape")
evals = _makeNd(evals, 4)
evecs = _makeNd(evecs, 5)
grid_shape = np.array(evals.shape[:3])
if sphere is None:
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
faces = np.asarray(sphere.faces, dtype=int)
vertices = sphere.vertices
colors = vtk.vtkUnsignedCharArray()
colors.SetNumberOfComponents(3)
colors.SetName("Colors")
if scalar_colors is None:
from dipy.reconst.dti import color_fa, fractional_anisotropy
cfa = color_fa(fractional_anisotropy(evals), evecs)
else:
cfa = _makeNd(scalar_colors, 4)
list_sq = []
list_cols = []
for ijk in ndindex(grid_shape):
ea = evals[ijk]
if norm:
ea /= ea.max()
ea = np.diag(ea.copy())
ev = evecs[ijk].copy()
xyz = np.dot(ev, np.dot(ea, vertices.T))
xyz += scale * (ijk - grid_shape / 2.)[:, None]
xyz = xyz.T
list_sq.append(xyz)
acolor = np.zeros(xyz.shape)
acolor[:, :] = np.interp(cfa[ijk], [0, 1], [0, 255])
list_cols.append(acolor.astype('ubyte'))
points = vtk.vtkPoints()
triangles = vtk.vtkCellArray()
for k in xrange(len(list_sq)):
xyz = list_sq[k]
cols = list_cols[k]
for i in xrange(xyz.shape[0]):
points.InsertNextPoint(*xyz[i])
colors.InsertNextTuple3(*cols[i])
for j in xrange(faces.shape[0]):
triangle = vtk.vtkTriangle()
triangle.GetPointIds().SetId(0, faces[j, 0] + k * xyz.shape[0])
triangle.GetPointIds().SetId(1, faces[j, 1] + k * xyz.shape[0])
triangle.GetPointIds().SetId(2, faces[j, 2] + k * xyz.shape[0])
triangles.InsertNextCell(triangle)
del triangle
polydata = vtk.vtkPolyData()
polydata.SetPoints(points)
polydata.SetPolys(triangles)
polydata.GetPointData().SetScalars(colors)
polydata.Modified()
mapper = vtk.vtkPolyDataMapper()
if major_version <= 5:
mapper.SetInput(polydata)
else:
mapper.SetInputData(polydata)
actor = vtk.vtkActor()
actor.SetMapper(mapper)
return actor
def processDiffusion(file, ds=False, ec=False, bvs=None):
'''
Process a diffusion-weighted dataset.
file=filename of Analyze or Nifti file (without extension)
bvs = list of b-values (optional)
ec = Whether or not eddy current correction should be applied (takes a while,
and does not work inside Spyder IDE but only from command line)
ds = Whether or not the image should be downsampled to an isotropic voxel size
2D images should be formatted as (x,y,t,z) and 3D images as (x,y,z,t)
which is standard for Bruker files.
This protocol automatically determines how many diffusion
dirs there are and how many b-values according to what is saved in the
accompanying text file. You can provide a list of exact b-values, but if
you do not the program will calculate mean b-values for each cluster of
diffusion directions based on the text file.
'''
dims = list_values(read_line('VisuCoreSize=', file))
ext = checkFileType(file)
img = nib.load(file + ext)
data = img.get_data()
affine = img.get_affine()
bvals, avbvals, dwgrad, dwdir, nA0, nbvals, ndirs = getDiffusionPars(file)
if len(dims) == 2:
#2D arrays are arranged differently. scipy.swapaxes is not sufficient for
#Paravision's Fortran-style column-major ordering as the t-axis is ordered differently.
newshape = (data.shape[0], data.shape[1], data.shape[3], data.shape[2])
print '2D array with shape %r. Reshaping to %r in Fortran-style column major.' % (
data.shape, newshape)
data = np.reshape(data, newshape, order='F')
rescaleImage(file, data, nbvals, dims)
img = nib.Nifti1Image(data, affine)
if ds:
print "Voxel size nonisotropic. Downsampling..."
data, affine = downsampleImage(img)
img = nib.Nifti1Image(data, affine)
else:
affine = img.get_affine()
data = img.get_data()
thresh = np.mean(data[:5, :5, :, 0])
mask = largest_component(brain_mask(data))
for i in range(data.shape[3]):
data[:, :, :, i] *= mask
if bvs == None:
bvalmat = np.array(avbvals)
bvalmat[bvalmat < 10] = 0
else:
bvalmat = np.zeros([nA0 + (ndirs * len(bvs))]) #entered pars
for i, b in enumerate(
bvs
): #unfortunately the ideal b-vals(not effective b-vals) are not in the text file. Have to enter manually and convert to appropriate matrix
bvalmat[nA0 + ndirs * i:] = b
bvecmat = np.zeros([nA0 + ndirs * nbvals, 3])
for i in range(nbvals):
bvecmat[nA0 + ndirs * i:nA0 + ndirs * (
i + 1
), :] = dwdir #fills b-vector matrix with the different diffusion dirs
if len(bvecmat) != len(bvals):
print "Error. Cannot process this image."
# if self.nrep>1:
# print "Image has %r repetitions. Correcting appropriately."
# avbv=np.array(bvalmat)
# tbvec=np.array(bvecmat)
# for c in range(self.nrep-1):
# np.concatenate(bvalmat,avbv)
# np.concatenate(bvecmat,tbv)
print bvalmat.shape
print dwgrad.shape
gtab = gradient_table(
bvalmat, bvecmat
) #creates a gradient table with b-vals and diffusion dirs for processing
from dipy.reconst.dti import TensorModel
starttime = datetime.now()
print "Fitting tensor model."
ten = TensorModel(gtab)
tenfit = ten.fit(data, mask)
time = datetime.now() - starttime
print "Tensor fit completed in %r seconds." % time.seconds
from dipy.reconst.dti import fractional_anisotropy
evecs = tenfit.evecs #eigenvectors
fa = fractional_anisotropy(tenfit.evals)
fa = np.clip(
fa, 0, 1) #removes voxels where fit failed by thresholding at 0 and 1
md = tenfit.md
md[np.isnan(md)] = 0 #removes voxels where fit failed
print "Calculated eigenvectors, MD and FA."
from dipy.reconst.dti import color_fa
cfa = color_fa(fa, tenfit.evecs)
return tenfit, cfa, bvalmat, dwgrad, bvecmat
def run(rawargs):
arguments = docopt(doc, argv=rawargs, version='Orientation Check v{0}'.format(Version))
inputs = [{"Value":"image file", "Flag": "--image"}, {"Value":"bvec file", "Flag": "--bvecs"}, {"Value":"bvec file", "Flag": "--bvecs"}]
for inputinfo in inputs:
if not exists(arguments[inputinfo["Flag"]]):
print("The {0} specified does not exist!".format(inputinfo["Value"]))
sys.exit(1)
rawimage = nib.load(arguments["--image"])
bvals, bvecs = read_bvals_bvecs(arguments['--bvals'], arguments['--bvecs'])
print("Generating gradient table.")
gtab = gradient_table(bvals, bvecs)
#Define the tensor model
print("Generating the tensor model.")
dti_wls = dti.TensorModel(gtab, fit_method="NLLS")
image_data = rawimage.get_data()
print("Masking the brain.")
image_masked, mask = median_otsu(image_data, 3, 1, autocrop=True, dilate=2)
#print(image_masked)
#image_masked_data = nib.nifti1.Nifti1Image(image_masked.astype(np.float32), image_data.get_affine())
#print("Saving masked brain image")
#nib.nifti1.save(image_masked_data, "./imagemasked.nii.gz")
print("Resampling the brain to a standard resolution.")
image, affine1 = reslice(image_masked, rawimage.get_affine(), rawimage.get_header().get_zooms()[:3], (3.0,3.0,3.0))
mask, maskaffine1 = reslice(mask.astype(numpy.int), rawimage.get_affine(), rawimage.get_header().get_zooms()[:3], (3.0,3.0,3.0))
#print(len([type(mask) for i in range(0,image.shape[3])]))
#mask = numpy.expand_dims(mask,3)
#print(mask)
#print(mask.shape)
#image=image*mask
print(image[0][0][0])
print("Checking the image dimensions")
Xsize, Ysize, Zsize, directions = image.shape
print("X: {0}\nY: {1}\nZ: {2}".format(Xsize, Ysize, Zsize))
#Define Image Scopes
print("Defining the image scopes.")
imagedict = {"axial": {"dropdim": [0,1], "scope": (slice(0,Xsize), slice(0,Ysize), slice(math.floor(Zsize/2),math.floor(Zsize/2)+1))},
"coronal": {"dropdim": [0,2], "scope": (slice(0,Xsize), slice(math.floor(Ysize/2),math.floor(Ysize/2)+1), slice(0, Zsize))},
"sagittal": {"dropdim": [1,2], "scope": (slice(math.floor(Xsize/2),math.floor(Xsize/2)+1), slice(0,Ysize), slice(0, Zsize))}}
#roi_idx = (slice(0,image.shape[0]), slice(0,image.shape[1]), slice(middleslice,middleslice+1))#(slice(0,image.shape[0]), slice(0,image.shape[1]), slice(int(image.shape[2]/2),int(image.shape[2]/2)+1))
print("Defining sphere.")
sphere = get_sphere('symmetric724')
#sphere = dpd.get_sphere('symmetric362')
#Slice the whole dataset by the scope
print("Slicing the dataset with the scopes.")
for view in ["sagittal", "coronal", "axial"]:
imagedict[view]["image"] = image[imagedict[view]["scope"]]
imagedict[view]["mask"] = mask[imagedict[view]["scope"]]
print("Fitting {0} data.".format(view))
fit_wls = dti_wls.fit(imagedict[view]["image"])
print("Extracting {0} FA.".format(view))
fa1 = fit_wls.fa * imagedict[view]["mask"]
print("Extracting {0} EVALS.".format(view))
evals1 = fit_wls.evals
print("Extracting {0} EVECS.".format(view))
evecs1 = fit_wls.evecs
print("Extracting {0} Color FA.".format(view))
cfa1 = dti.color_fa(fa1, evecs1)
cfa1 = cfa1/cfa1.max()
print("Defining {0} renderer.".format(view))
render = fvtk.ren()
print("Generating {0} image.".format(view))
x =cfa1.shape[imagedict[view]["dropdim"][0]]
y =cfa1.shape[imagedict[view]["dropdim"][1]]
#print(x, y, 1, 3)
cfa2 = cfa1.reshape(x, y, 1, 3)
evals2 = evals1.reshape(x, y, 1, 3)*1.25
evecs2 = evecs1.reshape(x, y, 1, 3, 3)*1.25
print("Adding render.")
fvtk.add(render, fvtk.tensor(evals2, evecs2, cfa2, sphere))
print("Recording render.")
with Xvfb() as xvfb:
fvtk.record(render, out_path=arguments["--out"+view], size=(800,800), magnification=2)
print("Image Saved")
sys.exit(0)
def tensorFit(self, m='dti', bv=None, removea0=0, mask=True):
if bv != None:
self.backups = [self.pdata, self.avbvals, self.nbvals]
self.pdata, self.avbvals = selectBvals(self, bv)
self.avbvals = np.array(self.avbvals)
self.nbvals = len(bv)
if removea0 != 0:
print "Removing %s A0 images." % removea0
self.pdata = self.pdata[:, :, :, removea0:]
self.nA0 -= removea0
self.avbvals = self.avbvals[removea0:]
self.bvals = self.bvals[removea0:]
# thresh=np.mean(self.pdata[:5,:5,:,0])
if mask:
print "Generating brain mask: Erosion>Dilation>MedianOtsu>LargestComponent"
self.mask = ndi.binary_dilation(
ndi.binary_erosion(ndi.binary_dilation(brain_mask(self.pdata))))
self.mask = largest_component(self.mask)
else:
print "Not using brain mask."
self.mask = np.zeros(self.shape[:-1]) + 1
bvalmat = np.array(self.avbvals)
self.bvecmat = construct_bvecs(self)
if len(self.bvecmat) != len(self.avbvals):
print "Error. Cannot process this image."
print bvalmat.shape
self.gtab = gradient_table(
bvalmat, self.bvecmat
) #creates a gradient table with b-vals and diffusion dirs for processing
if m == 'dti':
from dipy.reconst.dti import TensorModel
starttime = datetime.now()
print "Fitting tensor model."
ten = TensorModel(self.gtab)
self.tenfit = ten.fit(self.pdata, self.mask)
time = datetime.now() - starttime
tenfile = self.name + "_tenfit.pickle"
print "Tensor fit completed in %r seconds. Saving file %s." % (
time.seconds, tenfile)
# with open(tenfile, 'w') as f: TOO INEFFICIENT
# pickle.dump([self, self.tenfit], f)
from dipy.reconst.dti import color_fa
self.cfa = color_fa(self.tenfit.fa, self.tenfit.evecs)
if m == 'RESTORE':
from dipy.reconst.dti import TensorModel
mean_std = 2.5 * np.mean(
np.std(self.pdata[..., self.gtab.b0s_mask],
-1)) #conservative thresh
starttime = datetime.now()
print "Fitting tensor model using the RESTORE method. Sigma=", mean_std
ten = TensorModel(self.gtab, fit_method="RESTORE", sigma=mean_std)
self.tenfit = ten.fit(self.pdata, self.mask)
time = datetime.now() - starttime
tenfile = self.name + "_tenfit.pickle"
print "Tensor fit completed in %r seconds. Saving file %s." % (
time.seconds, tenfile)
# with open(tenfile, 'w') as f: TOO INEFFICIENT
# pickle.dump([self, self.tenfit], f)
from dipy.reconst.dti import color_fa
self.cfa = color_fa(self.tenfit.fa, self.tenfit.evecs)
elif m == 'dki':
from dipy.reconst.dki import DiffusionKurtosisModel
starttime = datetime.now()
print "Fitting kurtosis tensor."
kurt = DiffusionKurtosisModel(self.gtab)
self.tenfit = kurt.fit(self.pdata)
time = datetime.now() - starttime
print "Kurtosis fit completed in %r seconds." % time.seconds
if bv != None:
self.pdata = self.backups[0]
self.avbvals = self.backups[1]
self.nbvals = self.backups[2]
def run(self,
input_files,
bvalues_files,
bvectors_files,
mask_files,
b0_threshold=50.0,
save_metrics=[],
out_dir='',
out_dt_tensor='dti_tensors.nii.gz',
out_fa='fa.nii.gz',
out_ga='ga.nii.gz',
out_rgb='rgb.nii.gz',
out_md='md.nii.gz',
out_ad='ad.nii.gz',
out_rd='rd.nii.gz',
out_mode='mode.nii.gz',
out_evec='evecs.nii.gz',
out_eval='evals.nii.gz',
out_dk_tensor="dki_tensors.nii.gz",
out_mk="mk.nii.gz",
out_ak="ak.nii.gz",
out_rk="rk.nii.gz"):
""" Workflow for Diffusion Kurtosis reconstruction and for computing
DKI metrics. Performs a DKI reconstruction on the files by 'globing'
``input_files`` and saves the DKI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b0 volumes.
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
out_dir : string, optional
Output directory. (default current directory)
out_dt_tensor : string, optional
Name of the tensors volume to be saved.
out_dk_tensor : string, optional
Name of the tensors volume to be saved.
out_fa : string, optional
Name of the fractional anisotropy volume to be saved.
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved.
out_rgb : string, optional
Name of the color fa volume to be saved.
out_md : string, optional
Name of the mean diffusivity volume to be saved.
out_ad : string, optional
Name of the axial diffusivity volume to be saved.
out_rd : string, optional
Name of the radial diffusivity volume to be saved.
out_mode : string, optional
Name of the mode volume to be saved.
out_evec : string, optional
Name of the eigenvectors volume to be saved.
out_eval : string, optional
Name of the eigenvalues to be saved.
out_mk : string, optional
Name of the mean kurtosis to be saved.
out_ak : string, optional
Name of the axial kurtosis to be saved.
out_rk : string, optional
Name of the radial kurtosis to be saved.
References
----------
.. [1] Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional
kurtosis imaging. Magn Reson Med. 65(3), 823-836
.. [2] Jensen, Jens H., Joseph A. Helpern, Anita Ramani, Hanzhang Lu,
and Kyle Kaczynski. 2005. Diffusional Kurtosis Imaging: The
Quantification of Non-Gaussian Water Diffusion by Means of Magnetic
Resonance Imaging. MRM 53 (6):1432-40.
"""
io_it = self.get_io_iterator()
for (dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad,
omode, oevecs, oevals, odk_tensor, omk, oak, ork) in io_it:
logging.info('Computing DKI metrics for {0}'.format(dwi))
data, affine = load_nifti(dwi)
if mask is not None:
mask = load_nifti_data(mask).astype(bool)
dkfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold)
if not save_metrics:
save_metrics = [
'mk', 'rk', 'ak', 'fa', 'md', 'rd', 'ad', 'ga', 'rgb',
'mode', 'evec', 'eval', 'dt_tensor', 'dk_tensor'
]
evals, evecs, kt = split_dki_param(dkfit.model_params)
FA = fractional_anisotropy(evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'dt_tensor' in save_metrics:
tensor_vals = lower_triangular(dkfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
save_nifti(otensor, tensor_vals_reordered.astype(np.float32),
affine)
if 'dk_tensor' in save_metrics:
save_nifti(odk_tensor, dkfit.kt.astype(np.float32), affine)
if 'fa' in save_metrics:
save_nifti(ofa, FA.astype(np.float32), affine)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(dkfit.evals)
save_nifti(oga, GA.astype(np.float32), affine)
if 'rgb' in save_metrics:
RGB = color_fa(FA, dkfit.evecs)
save_nifti(orgb, np.array(255 * RGB, 'uint8'), affine)
if 'md' in save_metrics:
MD = mean_diffusivity(dkfit.evals)
save_nifti(omd, MD.astype(np.float32), affine)
if 'ad' in save_metrics:
AD = axial_diffusivity(dkfit.evals)
save_nifti(oad, AD.astype(np.float32), affine)
if 'rd' in save_metrics:
RD = radial_diffusivity(dkfit.evals)
save_nifti(orad, RD.astype(np.float32), affine)
if 'mode' in save_metrics:
MODE = get_mode(dkfit.quadratic_form)
save_nifti(omode, MODE.astype(np.float32), affine)
if 'evec' in save_metrics:
save_nifti(oevecs, dkfit.evecs.astype(np.float32), affine)
if 'eval' in save_metrics:
save_nifti(oevals, dkfit.evals.astype(np.float32), affine)
if 'mk' in save_metrics:
save_nifti(omk, dkfit.mk().astype(np.float32), affine)
if 'ak' in save_metrics:
save_nifti(oak, dkfit.ak().astype(np.float32), affine)
if 'rk' in save_metrics:
save_nifti(ork, dkfit.rk().astype(np.float32), affine)
logging.info('DKI metrics saved in {0}'.format(
os.path.dirname(oevals)))
"""
data = img.get_data()[roi_idx]
"""
This dataset is not very noisy, so we will artificially corrupt it to simulate
the effects of "physiological" noise, such as subject motion. But first, let's
establish a baseline, using the data as it is:
"""
fit_wls = dti_wls.fit(data)
fa1 = fit_wls.fa
evals1 = fit_wls.evals
evecs1 = fit_wls.evecs
cfa1 = dti.color_fa(fa1, evecs1)
sphere = dpd.get_sphere('symmetric724')
"""
We visualize the ODFs in the ROI using fvtk:
"""
ren = fvtk.ren()
fvtk.add(ren, fvtk.tensor(evals1, evecs1, cfa1, sphere))
print('Saving illustration as tensor_ellipsoids_wls.png')
fvtk.record(ren, n_frames=1, out_path='tensor_ellipsoids_wls.png',
size=(600, 600))
"""
.. figure:: tensor_ellipsoids_wls.png
:align: center
def run(self, input_files, bvalues_files, bvectors_files, mask_files,
b0_threshold=50, bvecs_tol=0.01, save_metrics=[],
out_dir='', out_tensor='tensors.nii.gz', out_fa='fa.nii.gz',
out_ga='ga.nii.gz', out_rgb='rgb.nii.gz', out_md='md.nii.gz',
out_ad='ad.nii.gz', out_rd='rd.nii.gz', out_mode='mode.nii.gz',
out_evec='evecs.nii.gz', out_eval='evals.nii.gz'):
""" Workflow for tensor reconstruction and for computing DTI metrics.
using Weighted Least-Squares.
Performs a tensor reconstruction on the files by 'globing'
``input_files`` and saves the DTI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvectors files. This path may contain wildcards to use
multiple bvectors files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions (default 0.0)
bvecs_tol : float, optional
Threshold used to check that norm(bvec) = 1 +/- bvecs_tol
b-vectors are unit vectors (default 0.01)
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
(default [] (all))
out_dir : string, optional
Output directory (default input file directory)
out_tensor : string, optional
Name of the tensors volume to be saved (default 'tensors.nii.gz')
out_fa : string, optional
Name of the fractional anisotropy volume to be saved
(default 'fa.nii.gz')
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved
(default 'ga.nii.gz')
out_rgb : string, optional
Name of the color fa volume to be saved (default 'rgb.nii.gz')
out_md : string, optional
Name of the mean diffusivity volume to be saved
(default 'md.nii.gz')
out_ad : string, optional
Name of the axial diffusivity volume to be saved
(default 'ad.nii.gz')
out_rd : string, optional
Name of the radial diffusivity volume to be saved
(default 'rd.nii.gz')
out_mode : string, optional
Name of the mode volume to be saved (default 'mode.nii.gz')
out_evec : string, optional
Name of the eigenvectors volume to be saved
(default 'evecs.nii.gz')
out_eval : string, optional
Name of the eigenvalues to be saved (default 'evals.nii.gz')
References
----------
.. [1] Basser, P.J., Mattiello, J., LeBihan, D., 1994. Estimation of
the effective self-diffusion tensor from the NMR spin echo. J Magn
Reson B 103, 247-254.
.. [2] Basser, P., Pierpaoli, C., 1996. Microstructural and
physiological features of tissues elucidated by quantitative
diffusion-tensor MRI. Journal of Magnetic Resonance 111, 209-219.
.. [3] Lin-Ching C., Jones D.K., Pierpaoli, C. 2005. RESTORE: Robust
estimation of tensors by outlier rejection. MRM 53: 1088-1095
.. [4] hung, SW., Lu, Y., Henry, R.G., 2006. Comparison of bootstrap
approaches for estimation of uncertainties of DTI parameters.
NeuroImage 33, 531-541.
"""
io_it = self.get_io_iterator()
for dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad, \
omode, oevecs, oevals in io_it:
logging.info('Computing DTI metrics for {0}'.format(dwi))
data, affine = load_nifti(dwi)
if mask is not None:
mask = nib.load(mask).get_data().astype(np.bool)
tenfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold, bvecs_tol)
if not save_metrics:
save_metrics = ['fa', 'md', 'rd', 'ad', 'ga', 'rgb', 'mode',
'evec', 'eval', 'tensor']
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'tensor' in save_metrics:
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
save_nifti(otensor, tensor_vals_reordered.astype(np.float32),
affine)
if 'fa' in save_metrics:
save_nifti(ofa, FA.astype(np.float32), affine)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(tenfit.evals)
save_nifti(oga, GA.astype(np.float32), affine)
if 'rgb' in save_metrics:
RGB = color_fa(FA, tenfit.evecs)
save_nifti(orgb, np.array(255 * RGB, 'uint8'), affine)
if 'md' in save_metrics:
MD = mean_diffusivity(tenfit.evals)
save_nifti(omd, MD.astype(np.float32), affine)
if 'ad' in save_metrics:
AD = axial_diffusivity(tenfit.evals)
save_nifti(oad, AD.astype(np.float32), affine)
if 'rd' in save_metrics:
RD = radial_diffusivity(tenfit.evals)
save_nifti(orad, RD.astype(np.float32), affine)
if 'mode' in save_metrics:
MODE = get_mode(tenfit.quadratic_form)
save_nifti(omode, MODE.astype(np.float32), affine)
if 'evec' in save_metrics:
save_nifti(oevecs, tenfit.evecs.astype(np.float32), affine)
if 'eval' in save_metrics:
save_nifti(oevals, tenfit.evals.astype(np.float32), affine)
dname_ = os.path.dirname(oevals)
if dname_ == '':
logging.info('DTI metrics saved in current directory')
else:
logging.info(
'DTI metrics saved in {0}'.format(dname_))
def main():
parser = buildArgsParser()
args = parser.parse_args()
# Load data
img = nib.load(args.input)
data = img.get_data()
affine = img.get_affine()
# Setting suffix savename
if args.savename is None:
filename = ""
else:
filename = args.savename + "_"
if os.path.exists(filename + 'fa.nii.gz'):
if not args.overwrite:
raise ValueError("File " + filename + "fa.nii.gz"
+ " already exists. Use -f option to overwrite.")
print (filename + "fa.nii.gz", " already exists and will be overwritten.")
if args.mask is not None:
mask = nib.load(args.mask).get_data()
else:
print("No mask specified. Computing mask with median_otsu.")
data, mask = median_otsu(data)
mask_img = nib.Nifti1Image(mask.astype(np.float32), affine)
nib.save(mask_img, filename + 'mask.nii.gz')
# Get tensors
print('Tensor estimation...')
b_vals, b_vecs = read_bvals_bvecs(args.bvals, args.bvecs)
gtab = gradient_table_from_bvals_bvecs(b_vals, b_vecs)
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
# FA
print('Computing FA...')
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
# RGB
print('Computing RGB...')
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tenfit.evecs)
if args.all :
print('Computing Diffusivities...')
# diffusivities
MD = mean_diffusivity(tenfit.evals)
AD = axial_diffusivity(tenfit.evals)
RD = radial_diffusivity(tenfit.evals)
print('Computing Mode...')
MODE = mode(tenfit.quadratic_form)
print('Saving tensor coefficients and metrics...')
# Get the Tensor values and format them for visualisation in the Fibernavigator.
tensor_vals = lower_triangular(tenfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
fiber_tensors = nib.Nifti1Image(tensor_vals_reordered.astype(np.float32), affine)
nib.save(fiber_tensors, filename + 'tensors.nii.gz')
# Save - for some reason this is not read properly by the FiberNav
md_img = nib.Nifti1Image(MD.astype(np.float32), affine)
nib.save(md_img, filename + 'md.nii.gz')
ad_img = nib.Nifti1Image(AD.astype(np.float32), affine)
nib.save(ad_img, filename + 'ad.nii.gz')
rd_img = nib.Nifti1Image(RD.astype(np.float32), affine)
nib.save(rd_img, filename + 'rd.nii.gz')
mode_img = nib.Nifti1Image(MODE.astype(np.float32), affine)
nib.save(mode_img, filename + 'mode.nii.gz')
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img, filename + 'fa.nii.gz')
rgb_img = nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine)
nib.save(rgb_img, filename + 'rgb.nii.gz')
And use them to index into the data:
"""
data = img.get_data()[roi_idx]
"""
This dataset is not very noisy, so we will artificially corrupt it to simulate
the effects of "physiological" noise, such as subject motion. But first, let's
establish a baseline, using the data as it is:
"""
fit_wls = dti_wls.fit(data)
fa1 = fit_wls.fa
evals1 = fit_wls.evals
evecs1 = fit_wls.evecs
cfa1 = dti.color_fa(fa1, evecs1)
sphere = dpd.get_sphere('symmetric724')
"""
We visualize the ODFs in the ROI using fvtk:
"""
ren = fvtk.ren()
fvtk.add(ren, fvtk.tensor(evals1, evecs1, cfa1, sphere))
print('Saving illustration as tensor_ellipsoids_wls.png')
fvtk.record(ren,
n_frames=1,
out_path='tensor_ellipsoids_wls.png',
size=(600, 600))
"""
.. figure:: tensor_ellipsoids_wls.png
:align: center
def run(self, input_files, bvalues_files, bvectors_files, mask_files,
b0_threshold=50.0, save_metrics=[],
out_dir='', out_dt_tensor='dti_tensors.nii.gz', out_fa='fa.nii.gz',
out_ga='ga.nii.gz', out_rgb='rgb.nii.gz', out_md='md.nii.gz',
out_ad='ad.nii.gz', out_rd='rd.nii.gz', out_mode='mode.nii.gz',
out_evec='evecs.nii.gz', out_eval='evals.nii.gz',
out_dk_tensor="dki_tensors.nii.gz",
out_mk="mk.nii.gz", out_ak="ak.nii.gz", out_rk="rk.nii.gz"):
""" Workflow for Diffusion Kurtosis reconstruction and for computing
DKI metrics. Performs a DKI reconstruction on the files by 'globing'
``input_files`` and saves the DKI metrics in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
bvalues_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
bvectors_files : string
Path to the bvalues files. This path may contain wildcards to use
multiple bvalues files at once.
mask_files : string
Path to the input masks. This path may contain wildcards to use
multiple masks at once. (default: No mask used)
b0_threshold : float, optional
Threshold used to find b=0 directions (default 0.0)
save_metrics : variable string, optional
List of metrics to save.
Possible values: fa, ga, rgb, md, ad, rd, mode, tensor, evec, eval
(default [] (all))
out_dir : string, optional
Output directory (default input file directory)
out_dt_tensor : string, optional
Name of the tensors volume to be saved
(default: 'dti_tensors.nii.gz')
out_dk_tensor : string, optional
Name of the tensors volume to be saved
(default 'dki_tensors.nii.gz')
out_fa : string, optional
Name of the fractional anisotropy volume to be saved
(default 'fa.nii.gz')
out_ga : string, optional
Name of the geodesic anisotropy volume to be saved
(default 'ga.nii.gz')
out_rgb : string, optional
Name of the color fa volume to be saved (default 'rgb.nii.gz')
out_md : string, optional
Name of the mean diffusivity volume to be saved
(default 'md.nii.gz')
out_ad : string, optional
Name of the axial diffusivity volume to be saved
(default 'ad.nii.gz')
out_rd : string, optional
Name of the radial diffusivity volume to be saved
(default 'rd.nii.gz')
out_mode : string, optional
Name of the mode volume to be saved (default 'mode.nii.gz')
out_evec : string, optional
Name of the eigenvectors volume to be saved
(default 'evecs.nii.gz')
out_eval : string, optional
Name of the eigenvalues to be saved (default 'evals.nii.gz')
out_mk : string, optional
Name of the mean kurtosis to be saved (default: 'mk.nii.gz')
out_ak : string, optional
Name of the axial kurtosis to be saved (default: 'ak.nii.gz')
out_rk : string, optional
Name of the radial kurtosis to be saved (default: 'rk.nii.gz')
References
----------
.. [1] Tabesh, A., Jensen, J.H., Ardekani, B.A., Helpern, J.A., 2011.
Estimation of tensors and tensor-derived measures in diffusional
kurtosis imaging. Magn Reson Med. 65(3), 823-836
.. [2] Jensen, Jens H., Joseph A. Helpern, Anita Ramani, Hanzhang Lu,
and Kyle Kaczynski. 2005. Diffusional Kurtosis Imaging: The
Quantification of Non-Gaussian Water Diffusion by Means of Magnetic
Resonance Imaging. MRM 53 (6):1432-40.
"""
io_it = self.get_io_iterator()
for (dwi, bval, bvec, mask, otensor, ofa, oga, orgb, omd, oad, orad,
omode, oevecs, oevals, odk_tensor, omk, oak, ork) in io_it:
logging.info('Computing DKI metrics for {0}'.format(dwi))
data, affine = load_nifti(dwi)
if mask is not None:
mask = nib.load(mask).get_data().astype(np.bool)
dkfit, _ = self.get_fitted_tensor(data, mask, bval, bvec,
b0_threshold)
if not save_metrics:
save_metrics = ['mk', 'rk', 'ak', 'fa', 'md', 'rd', 'ad', 'ga',
'rgb', 'mode', 'evec', 'eval', 'dt_tensor',
'dk_tensor']
evals, evecs, kt = split_dki_param(dkfit.model_params)
FA = fractional_anisotropy(evals)
FA[np.isnan(FA)] = 0
FA = np.clip(FA, 0, 1)
if 'dt_tensor' in save_metrics:
tensor_vals = lower_triangular(dkfit.quadratic_form)
correct_order = [0, 1, 3, 2, 4, 5]
tensor_vals_reordered = tensor_vals[..., correct_order]
save_nifti(otensor, tensor_vals_reordered.astype(np.float32),
affine)
if 'dk_tensor' in save_metrics:
save_nifti(odk_tensor, dkfit.kt.astype(np.float32), affine)
if 'fa' in save_metrics:
save_nifti(ofa, FA.astype(np.float32), affine)
if 'ga' in save_metrics:
GA = geodesic_anisotropy(dkfit.evals)
save_nifti(oga, GA.astype(np.float32), affine)
if 'rgb' in save_metrics:
RGB = color_fa(FA, dkfit.evecs)
save_nifti(orgb, np.array(255 * RGB, 'uint8'), affine)
if 'md' in save_metrics:
MD = mean_diffusivity(dkfit.evals)
save_nifti(omd, MD.astype(np.float32), affine)
if 'ad' in save_metrics:
AD = axial_diffusivity(dkfit.evals)
save_nifti(oad, AD.astype(np.float32), affine)
if 'rd' in save_metrics:
RD = radial_diffusivity(dkfit.evals)
save_nifti(orad, RD.astype(np.float32), affine)
if 'mode' in save_metrics:
MODE = get_mode(dkfit.quadratic_form)
save_nifti(omode, MODE.astype(np.float32), affine)
if 'evec' in save_metrics:
save_nifti(oevecs, dkfit.evecs.astype(np.float32), affine)
if 'eval' in save_metrics:
save_nifti(oevals, dkfit.evals.astype(np.float32), affine)
if 'mk' in save_metrics:
save_nifti(omk, dkfit.mk().astype(np.float32), affine)
if 'ak' in save_metrics:
save_nifti(oak, dkfit.ak().astype(np.float32), affine)
if 'rk' in save_metrics:
save_nifti(ork, dkfit.rk().astype(np.float32), affine)
logging.info('DKI metrics saved in {0}'.
format(os.path.dirname(oevals)))
print('data.shape (%d, %d, %d, %d)' % data.shape)
# Creating the mask
maskdata, mask = median_otsu(data, 3, 1, False, vol_idx=range(10, 50), dilate=2)
print('maskdata.shape (%d, %d, %d, %d)' % maskdata.shape)
# Creating tensor model
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(maskdata)
# Computing anisotropy measures
print('Computing anisotropy measures (FA, RGB)')
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
fa_img = nib.Nifti1Image(FA.astype(np.float32), img.get_affine())
nib.save(fa_img, 'tp3_data\\_tensor_fa.nii.gz')
# Computing RGB tensor
FA = np.clip(FA, 0, 1)
RGB = color_fa(FA, tenfit.evecs)
nib.save(nib.Nifti1Image(np.array(255 * RGB, 'uint8'), img.get_affine()), 'tp3_data\\_tensor_rgb.nii.gz')
# Print coronal view of each result
img_rgb = utils.load_nifti("_tensor_rgb.nii.gz").get_data()
img_fa = utils.load_nifti("_tensor_fa.nii.gz").get_data()
#print_FA_RGB(img_fa, img_rgb, img_md, img_evect, "_Question2b")
fa_img = nib.Nifti1Image(tenfit.fa.astype(np.float32), affine)
nib.save(fa_img, ffa)
# In[10]:
# save first eigen vector
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)
nib.save(evecs_img, dname+'tensor_evecs.nii.gz')
# In[32]:
# compute and save rgb
from dipy.reconst.dti import color_fa
RGB = color_fa(tenfit.fa, tenfit.evecs)
nib.save(nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine), dname+'tensor_rgb.nii.gz')
# In[ ]:
# In[36]:
sh_order = 8 #TODO: check what that does
if data.shape[-1] < 15:
raise ValueError('You need at least 15 unique DWI volumes to '
'compute fiber ODFs. You currently have: {0}'
' DWI volumes.'.format(data.shape[-1]))