def test_standalone_functions():
# WLS procedure
params = wls_fit_tensor(gtab_2s, DWI)
assert_array_almost_equal(params[..., 12], GTF)
fa = fractional_anisotropy(params[..., :3])
assert_array_almost_equal(fa, FAref)
# NLS procedure
params = nls_fit_tensor(gtab_2s, DWI)
assert_array_almost_equal(params[..., 12], GTF)
fa = fractional_anisotropy(params[..., :3])
assert_array_almost_equal(fa, FAref)
def test_response_from_mask():
fdata, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
data = nib.load(fdata).get_data()
gtab = gradient_table(bvals, bvecs)
ten = TensorModel(gtab)
tenfit = ten.fit(data)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
radius = 3
for fa_thr in np.arange(0, 1, 0.1):
response_auto, ratio_auto, nvoxels = auto_response(gtab,
data,
roi_center=None,
roi_radius=radius,
fa_thr=fa_thr,
return_number_of_voxels=True)
ci, cj, ck = np.array(data.shape[:3]) / 2
mask = np.zeros(data.shape[:3])
mask[ci - radius: ci + radius,
cj - radius: cj + radius,
ck - radius: ck + radius] = 1
mask[FA <= fa_thr] = 0
response_mask, ratio_mask = response_from_mask(gtab, data, mask)
assert_equal(int(np.sum(mask)), nvoxels)
assert_array_almost_equal(response_mask[0], response_auto[0])
assert_almost_equal(response_mask[1], response_auto[1])
assert_almost_equal(ratio_mask, ratio_auto)
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 DIPY_nii2streamlines(imgfile, maskfile, bvals, bvecs, output_prefix):
import numpy as np
import nibabel as nib
import os
from dipy.reconst.dti import TensorModel
print "nii2streamlines"
img = nib.load(imgfile)
bvals = np.genfromtxt(bvals)
bvecs = np.genfromtxt(bvecs)
if bvecs.shape[1] != 3:
bvecs = bvecs.T
print bvecs.shape
from nipype.utils.filemanip import split_filename
_, prefix, _ = split_filename(imgfile)
from dipy.data import gradient_table
gtab = gradient_table(bvals, bvecs)
data = img.get_data()
affine = img.get_affine()
zooms = img.get_header().get_zooms()[:3]
new_zooms = (2., 2., 2.)
data2, affine2 = data, affine
mask = nib.load(maskfile).get_data().astype(np.bool)
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data2, mask)
from dipy.reconst.dti import fractional_anisotropy
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
fa_img = nib.Nifti1Image(FA, img.get_affine())
nib.save(fa_img, experiment_dir + '/' + ('%s_tensor_fa.nii.gz' % prefix))
evecs = tenfit.evecs
evec_img = nib.Nifti1Image(evecs, img.get_affine())
nib.save(evec_img, experiment_dir + '/' + ('%s_tensor_evec.nii.gz' % prefix))
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
from dipy.reconst.dti import quantize_evecs
peak_indices = quantize_evecs(tenfit.evecs, sphere.vertices)
from dipy.tracking.eudx import EuDX
eu = EuDX(FA, peak_indices, odf_vertices = sphere.vertices, a_low=0.2, seeds=10**6, ang_thr=35)
tensor_streamlines = [streamline for streamline in eu]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = new_zooms
hdr['voxel_order'] = 'LPS'
hdr['dim'] = data2.shape[:3]
import dipy.tracking.metrics as dmetrics
tensor_streamlines = ((sl, None, None) for sl in tensor_streamlines if dmetrics.length(sl) > 15)
ten_sl_fname = experiment_dir + '/' + ('%s_streamline.trk' % prefix)
nib.trackvis.write(ten_sl_fname, tensor_streamlines, hdr, points_space='voxel')
return ten_sl_fname
def compute_dti(fname_in, fname_bvals, fname_bvecs, prefix):
"""
Compute DTI.
:param fname_in: input 4d file.
:param bvals: bvals txt file
:param bvecs: bvecs txt file
:param prefix: output prefix. Example: "dti_"
:return: True/False
"""
# Open file.
from msct_image import Image
nii = Image(fname_in)
data = nii.data
print('data.shape (%d, %d, %d, %d)' % data.shape)
# open bvecs/bvals
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fname_bvals, fname_bvecs)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
# # mask and crop the data. This is a quick way to avoid calculating Tensors on the background of the image.
# from dipy.segment.mask import median_otsu
# maskdata, mask = median_otsu(data, 3, 1, True, vol_idx=range(10, 50), dilate=2)
# print('maskdata.shape (%d, %d, %d, %d)' % maskdata.shape)
# fit tensor model
import dipy.reconst.dti as dti
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(data)
# Compute metrics
printv('Computing metrics...', param.verbose)
# FA
from dipy.reconst.dti import fractional_anisotropy
nii.data = fractional_anisotropy(tenfit.evals)
nii.setFileName(prefix+'FA.nii.gz')
nii.save('float32')
# MD
from dipy.reconst.dti import mean_diffusivity
nii.data = mean_diffusivity(tenfit.evals)
nii.setFileName(prefix+'MD.nii.gz')
nii.save('float32')
# RD
from dipy.reconst.dti import radial_diffusivity
nii.data = radial_diffusivity(tenfit.evals)
nii.setFileName(prefix+'RD.nii.gz')
nii.save('float32')
# AD
from dipy.reconst.dti import axial_diffusivity
nii.data = axial_diffusivity(tenfit.evals)
nii.setFileName(prefix+'AD.nii.gz')
nii.save('float32')
return True
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 estimate_response(gtab, data, affine, mask, fa_thr=0.7):
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
mask[FA <= 0.1] = 0
mask[FA > 1.] = 0
indices = np.where(FA > fa_thr)
lambdas = tenfit.evals[indices][:, :2]
S0s = data[indices][:, 0]
S0 = np.mean(S0s)
l01 = np.mean(lambdas, axis=0)
evals = np.array([l01[0], l01[1], l01[1]])
ratio = evals[1] / evals[0]
print 'Response evals' , evals, ' ratio: ', ratio, '\tMean S0', S0
return (evals, S0), ratio
def prepare(training, category, snr, denoised, odeconv, tv, method):
data, affine, gtab = get_specific_data(training,
category,
snr,
denoised)
prefix = create_file_prefix(training,
category,
snr,
denoised,
odeconv,
tv,
method)
if training:
mask = nib.load('wm_mask_hardi_01.nii.gz').get_data()
else:
#mask = np.ones(data.shape[:-1])
mask = nib.load('test_hardi_30_den=1_fa_0025_dilate2_mask.nii.gz').get_data()
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
mask[FA <= 0.1] = 0
mask[FA > 1.] = 0
indices = np.where(FA > 0.7)
lambdas = tenfit.evals[indices][:, :2]
S0s = data[indices][:, 0]
S0 = np.mean(S0s)
if S0 == 0:
print 'S0 equals to 0 switching to 1'
S0 = 1
l01 = np.mean(lambdas, axis=0)
evals = np.array([l01[0], l01[1], l01[1]])
print evals, S0
return data, affine, gtab, mask, evals, S0, prefix
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 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 tractography_rec(imag, bvals, bvecs, seed, threshold):
''' Script to generate tractography. Uses the EuDX function from dipy. Returns tractography and FA.
Parameters
----------
imag: NiftiImage object
bvals: bvals array
bvecs: bvecs array
seed: int or ndarray (Parameter for the EuDX function)
threshold : float (Parameter for the EuDX function)
'''
print "Retrieving data and affine"
data = img.get_data()
affine = img.get_affine()
#new version of dipy
print "Computing tensor model"
gradients = gradient_table(bvals,bvecs)
tensor_model = dti.TensorModel(gradients)
tensors = tensor_model.fit(data)
print "Computing FA"
FA = dti.fractional_anisotropy(tensors.evals)
FA[np.isnan(FA)] = 0
print "Computing evecs"
evecs_img = nib.Nifti1Image(tensors.evecs.astype(np.float32), affine)
evecs = evecs_img.get_data()
sphere = get_sphere('symmetric724')
peak_indices = dti.quantize_evecs(evecs, sphere.vertices)
print "Computing EuDX reconstruction."
streamlines = EuDX(FA.astype('f8'),
ind=peak_indices,
seeds=seed,
odf_vertices= sphere.vertices,
a_low=threshold)
return streamlines, FA
def single_fiber_response(diffusionData, mask, gtable, fa_thr = 0.7):
from dipy.reconst.dti import TensorModel, fractional_anisotropy
ten = TensorModel(gtable)
tenfit = ten.fit(diffusionData, mask=mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
indices = np.where(FA > fa_thr)
lambdas = tenfit.evals[indices][:, :2]
S0s = diffusionData[indices][:, np.nonzero(gtable.b0s_mask)[0]]
S0 = np.mean(S0s)
l01 = np.mean(lambdas, axis=0)
evals = np.array([l01[0], l01[1], l01[1]])
response = (evals, S0)
ratio = evals[1]/evals[0]
return response, ratio
def tensor_model(
input_filename_data, input_filename_bvecs, input_filename_bvals, output_filename_fa=None, output_filename_evecs=None
):
# print 'Tensor model ...'
# print 'Loading data ...'
img = nib.load(input_filename_data)
data = img.get_data()
affine = img.get_affine()
bvals, bvecs = read_bvals_bvecs(input_filename_bvals, input_filename_bvecs)
gtab = gradient_table(bvals, bvecs)
mask = data[..., 0] > 50
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
if output_filename_fa == None:
filename_save_fa = input_filename_data.split(".")[0] + "_tensor_fa.nii.gz"
else:
filename_save_fa = os.path.abspath(output_filename_fa)
fa_img = nib.Nifti1Image(FA, img.get_affine())
nib.save(fa_img, filename_save_fa)
print "Saving fa to:", filename_save_fa
if output_filename_evecs == None:
filename_save_evecs = input_filename_data.split(".")[0] + "_tensor_evecs.nii.gz"
else:
filename_save_evecs = os.path.abspath(output_filename_evecs)
evecs_img = nib.Nifti1Image(tenfit.evecs, img.get_affine())
nib.save(evecs_img, filename_save_evecs)
print "Saving evecs to:", filename_save_evecs
return filename_save_fa, filename_save_evecs
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 mask_for_response_ssst(gtab,
data,
roi_center=None,
roi_radii=10,
fa_thr=0.7):
""" Computation of mask for single-shell single-tissue (ssst) response
function using FA.
Parameters
----------
gtab : GradientTable
data : ndarray
diffusion data (4D)
roi_center : array-like, (3,)
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]`.
roi_radii : int or array-like, (3,)
radii of cuboid ROI
fa_thr : float
FA threshold
Returns
-------
mask : ndarray
Mask of voxels within the ROI and with FA above the FA threshold.
Notes
-----
In CSD there is an important pre-processing step: the estimation of the
fiber response function. In order to do this, we look for voxels with very
anisotropic configurations. This function aims to accomplish that by
returning a mask of voxels within a ROI, that have a FA value above a
given threshold. For example we can use a ROI (20x20x20) at
the center of the volume and store the signal values for the voxels with
FA values higher than 0.7 (see [1]_).
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2004. Direct estimation of the
fiber orientation density function from diffusion-weighted MRI
data using spherical deconvolution
"""
if len(data.shape) < 4:
msg = """Data must be 4D (3D image + directions). To use a 2D image,
please reshape it into a (N, N, 1, ndirs) array."""
raise ValueError(msg)
if isinstance(roi_radii, numbers.Number):
roi_radii = (roi_radii, roi_radii, roi_radii)
if roi_center is None:
roi_center = np.array(data.shape[:3]) // 2
roi_radii = _roi_in_volume(data.shape, np.asarray(roi_center),
np.asarray(roi_radii))
roi_mask = _mask_from_roi(data.shape[:3], roi_center, roi_radii)
ten = TensorModel(gtab)
tenfit = ten.fit(data, mask=roi_mask)
fa = fractional_anisotropy(tenfit.evals)
fa[np.isnan(fa)] = 0
mask = np.zeros(fa.shape, dtype=np.int64)
mask[fa > fa_thr] = 1
if np.sum(mask) == 0:
msg = """No voxel with a FA higher than {} were found.
Try a larger roi or a lower threshold.""".format(str(fa_thr))
warnings.warn(msg, UserWarning)
return mask
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')
def compute_dti(fname_in, fname_bvals, fname_bvecs, prefix, method, file_mask):
"""
Compute DTI.
:param fname_in: input 4d file.
:param bvals: bvals txt file
:param bvecs: bvecs txt file
:param prefix: output prefix. Example: "dti_"
:param method: algo for computing dti
:return: True/False
"""
# Open file.
from msct_image import Image
nii = Image(fname_in)
data = nii.data
print('data.shape (%d, %d, %d, %d)' % data.shape)
# open bvecs/bvals
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fname_bvals, fname_bvecs)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
# mask and crop the data. This is a quick way to avoid calculating Tensors on the background of the image.
if not file_mask == '':
printv('Open mask file...', param.verbose)
# open mask file
nii_mask = Image(file_mask)
mask = nii_mask.data
# fit tensor model
printv('Computing tensor using "'+method+'" method...', param.verbose)
import dipy.reconst.dti as dti
if method == 'standard':
tenmodel = dti.TensorModel(gtab)
if file_mask == '':
tenfit = tenmodel.fit(data)
else:
tenfit = tenmodel.fit(data, mask)
elif method == 'restore':
import dipy.denoise.noise_estimate as ne
sigma = ne.estimate_sigma(data)
dti_restore = dti.TensorModel(gtab, fit_method='RESTORE', sigma=sigma)
if file_mask == '':
tenfit = dti_restore.fit(data)
else:
tenfit = dti_restore.fit(data, mask)
# Compute metrics
printv('Computing metrics...', param.verbose)
# FA
from dipy.reconst.dti import fractional_anisotropy
nii.data = fractional_anisotropy(tenfit.evals)
nii.setFileName(prefix+'FA.nii.gz')
nii.save('float32')
# MD
from dipy.reconst.dti import mean_diffusivity
nii.data = mean_diffusivity(tenfit.evals)
nii.setFileName(prefix+'MD.nii.gz')
nii.save('float32')
# RD
from dipy.reconst.dti import radial_diffusivity
nii.data = radial_diffusivity(tenfit.evals)
nii.setFileName(prefix+'RD.nii.gz')
nii.save('float32')
# AD
from dipy.reconst.dti import axial_diffusivity
nii.data = axial_diffusivity(tenfit.evals)
nii.setFileName(prefix+'AD.nii.gz')
nii.save('float32')
return True
def auto_response(gtab, data, roi_center=None, roi_radius=10, fa_thr=0.7,
fa_callable=fa_superior, return_number_of_voxels=False):
""" Automatic estimation of response function using FA.
Parameters
----------
gtab : GradientTable
data : ndarray
diffusion data
roi_center : tuple, (3,)
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]`.
roi_radius : int
radius of cubic ROI
fa_thr : float
FA threshold
fa_callable : callable
A callable that defines an operation that compares FA with the fa_thr. The operator
should have two positional arguments (e.g., `fa_operator(FA, fa_thr)`) and it should
return a bool array.
return_number_of_voxels : bool
If True, returns the number of voxels used for estimating the response
function.
Returns
-------
response : tuple, (2,)
(`evals`, `S0`)
ratio : float
The ratio between smallest versus largest eigenvalue of the response.
number of voxels : int (optional)
The number of voxels used for estimating the response function.
Notes
-----
In CSD there is an important pre-processing step: the estimation of the
fiber response function. In order to do this we look for voxels with very
anisotropic configurations. For example we can use an ROI (20x20x20) at
the center of the volume and store the signal values for the voxels with
FA values higher than 0.7. Of course, if we haven't precalculated FA we
need to fit a Tensor model to the datasets. Which is what we do in this
function.
For the response we also need to find the average S0 in the ROI. This is
possible using `gtab.b0s_mask()` we can find all the S0 volumes (which
correspond to b-values equal 0) in the dataset.
The `response` consists always of a prolate tensor created by averaging
the highest and second highest eigenvalues in the ROI with FA higher than
threshold. We also include the average S0s.
We also return the `ratio` which is used for the SDT models. If requested,
the number of voxels used for estimating the response function is also
returned, which can be used to judge the fidelity of the response function.
As a rule of thumb, at least 300 voxels should be used to estimate a good
response function (see [1]_).
References
----------
.. [1] Tournier, J.D., et al. NeuroImage 2004. Direct estimation of the
fiber orientation density function from diffusion-weighted MRI
data using spherical deconvolution
"""
ten = TensorModel(gtab)
if roi_center is None:
ci, cj, ck = np.array(data.shape[:3]) // 2
else:
ci, cj, ck = roi_center
w = roi_radius
roi = data[int(ci - w): int(ci + w),
int(cj - w): int(cj + w),
int(ck - w): int(ck + w)]
tenfit = ten.fit(roi)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
indices = np.where(fa_callable(FA, fa_thr))
if indices[0].size == 0:
msg = "No voxel with a FA higher than " + str(fa_thr) + " were found."
msg += " Try a larger roi or a lower threshold."
warnings.warn(msg, UserWarning)
lambdas = tenfit.evals[indices][:, :2]
S0s = roi[indices][:, np.nonzero(gtab.b0s_mask)[0]]
response, ratio = _get_response(S0s, lambdas)
if return_number_of_voxels:
return response, ratio, indices[0].size
return response, ratio
def eudx_advanced(self, dti_file, mask_file, gtab,
seed_num=100000, stop_val=0.1):
"""
Tracking with more complex tensors - experimental
Initializes the graph with nodes corresponding to the number of ROIs
**Positional Arguments:**
dti_file:
- File (registered) to use for tensor/fiber tracking
mask_file:
- Brain mask to keep tensors inside the brain
gtab:
- dipy formatted bval/bvec Structure
**Optional Arguments:**
seed_num:
- Number of seeds to use for fiber tracking
stop_val:
- Value to cutoff fiber track
"""
img = nb.load(dti_file)
data = img.get_data()
img = nb.load(mask_file)
mask = img.get_data()
mask = mask > 0 # to ensure binary mask
"""
For the constrained spherical deconvolution we need to estimate the
response function (see :ref:`example_reconst_csd`) and create a model.
"""
response, ratio = auto_response(gtab, data, roi_radius=10,
fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
"""
Next, we use ``peaks_from_model`` to fit the data and calculated
the fiber directions in all voxels.
"""
sphere = get_sphere('symmetric724')
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
mask=mask,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
"""
For the tracking part, we will use ``csd_model`` fiber directions
but stop tracking where fractional anisotropy (FA) is low (< 0.1).
To derive the FA, used as a stopping criterion, we need to fit a
tensor model first. Here, we use weighted least squares (WLS).
"""
print 'tensors...'
tensor_model = TensorModel(gtab, fit_method='WLS')
tensor_fit = tensor_model.fit(data, mask)
FA = fractional_anisotropy(tensor_fit.evals)
"""
In order for the stopping values to be used with our tracking
algorithm we need to have the same dimensions as the
``csd_peaks.peak_values``. For this reason, we can assign the
same FA value to every peak direction in the same voxel in
the following way.
"""
stopping_values = np.zeros(csd_peaks.peak_values.shape)
stopping_values[:] = FA[..., None]
streamline_generator = EuDX(stopping_values,
csd_peaks.peak_indices,
seeds=seed_num,
odf_vertices=sphere.vertices,
a_low=stop_val)
streamlines = [streamline for streamline in streamline_generator]
return streamlines
def auto_response(gtab, data, roi_center=None, roi_radius=10, fa_thr=0.7):
""" Automatic estimation of response function using FA
Parameters
----------
gtab : GradientTable
data : ndarray
diffusion data
roi_center : tuple, (3,)
Center of ROI in data. If center is None, it is assumed that it is
the center of the volume with shape `data.shape[:3]`.
roi_radius : int
radius of cubic ROI
fa_thr : float
FA threshold
Returns
-------
response : tuple, (2,)
(`evals`, `S0`)
ratio : float
the ratio between smallest versus largest eigenvalue of the response
Notes
-----
In CSD there is an important pre-processing step: the estimation of the
fiber response function. In order to do this we look for voxels with very
anisotropic configurations. For example we can use an ROI (20x20x20) at
the center of the volume and store the signal values for the voxels with
FA values higher than 0.7. Of course, if we haven't precalculated FA we
need to fit a Tensor model to the datasets. Which is what we do in this
function.
For the response we also need to find the average S0 in the ROI. This is
possible using `gtab.b0s_mask()` we can find all the S0 volumes (which
correspond to b-values equal 0) in the dataset.
The `response` consists always of a prolate tensor created by averaging
the highest and second highest eigenvalues in the ROI with FA higher than
threshold. We also include the average S0s.
Finally, we also return the `ratio` which is used for the SDT models.
"""
ten = TensorModel(gtab)
if roi_center is None:
ci, cj, ck = np.array(data.shape[:3]) / 2
else:
ci, cj, ck = roi_center
w = roi_radius
roi = data[ci - w: ci + w, cj - w: cj + w, ck - w: ck + w]
tenfit = ten.fit(roi)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
indices = np.where(FA > fa_thr)
lambdas = tenfit.evals[indices][:, :2]
S0s = roi[indices][:, np.nonzero(gtab.b0s_mask)[0]]
S0 = np.mean(S0s)
l01 = np.mean(lambdas, axis=0)
evals = np.array([l01[0], l01[1], l01[1]])
response = (evals, S0)
ratio = evals[1]/evals[0]
return response, ratio
def _run_interface(self, runtime):
from dipy.core.gradients import GradientTable
from dipy.reconst.dti import fractional_anisotropy, mean_diffusivity
from dipy.reconst.csdeconv import recursive_response, auto_response
img = nb.load(self.inputs.in_file)
affine = img.get_affine()
if isdefined(self.inputs.in_mask):
msk = nb.load(self.inputs.in_mask).get_data()
msk[msk > 0] = 1
msk[msk < 0] = 0
else:
msk = np.ones(imref.get_shape())
data = img.get_data().astype(np.float32)
gtab = self._get_gradient_table()
evals = np.nan_to_num(nb.load(self.inputs.in_evals).get_data())
FA = np.nan_to_num(fractional_anisotropy(evals)) * msk
indices = np.where(FA > self.inputs.fa_thresh)
S0s = data[indices][:, np.nonzero(gtab.b0s_mask)[0]]
S0 = np.mean(S0s)
if self.inputs.auto:
response, ratio = auto_response(gtab, data,
roi_radius=self.inputs.roi_radius,
fa_thr=self.inputs.fa_thresh)
response = response[0].tolist() + [S0]
elif self.inputs.recursive:
MD = np.nan_to_num(mean_diffusivity(evals)) * msk
indices = np.logical_or(
FA >= 0.4, (np.logical_and(FA >= 0.15, MD >= 0.0011)))
data = nb.load(self.inputs.in_file).get_data()
response = recursive_response(gtab, data, mask=indices, sh_order=8,
peak_thr=0.01, init_fa=0.08,
init_trace=0.0021, iter=8,
convergence=0.001,
parallel=True)
ratio = abs(response[1] / response[0])
else:
lambdas = evals[indices]
l01 = np.sort(np.mean(lambdas, axis=0))
response = np.array([l01[-1], l01[-2], l01[-2], S0])
ratio = abs(response[1] / response[0])
if ratio > 0.25:
IFLOGGER.warn(('Estimated response is not prolate enough. '
'Ratio=%0.3f.') % ratio)
elif ratio < 1.e-5 or np.any(np.isnan(response)):
response = np.array([1.8e-3, 3.6e-4, 3.6e-4, S0])
IFLOGGER.warn(
('Estimated response is not valid, using a default one'))
else:
IFLOGGER.info(('Estimated response: %s') % str(response[:3]))
np.savetxt(op.abspath(self.inputs.response), response)
wm_mask = np.zeros_like(FA)
wm_mask[indices] = 1
nb.Nifti1Image(
wm_mask.astype(np.uint8), affine,
None).to_filename(op.abspath(self.inputs.out_mask))
return runtime
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 test_recursive_response_calibration():
"""
Test the recursive response calibration method.
"""
SNR = 100
S0 = 1
sh_order = 8
_, fbvals, fbvecs = get_data('small_64D')
bvals = np.load(fbvals)
bvecs = np.load(fbvecs)
sphere = get_sphere('symmetric724')
gtab = gradient_table(bvals, bvecs)
evals = np.array([0.0015, 0.0003, 0.0003])
evecs = np.array([[0, 1, 0], [0, 0, 1], [1, 0, 0]]).T
mevals = np.array(([0.0015, 0.0003, 0.0003],
[0.0015, 0.0003, 0.0003]))
angles = [(0, 0), (90, 0)]
where_dwi = lazy_index(~gtab.b0s_mask)
S_cross, sticks_cross = multi_tensor(gtab, mevals, S0, angles=angles,
fractions=[50, 50], snr=SNR)
S_single = single_tensor(gtab, S0, evals, evecs, snr=SNR)
data = np.concatenate((np.tile(S_cross, (8, 1)),
np.tile(S_single, (2, 1))),
axis=0)
odf_gt_cross = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50])
odf_gt_single = single_tensor_odf(sphere.vertices, evals, evecs)
response = recursive_response(gtab, data, mask=None, sh_order=8,
peak_thr=0.01, init_fa=0.05,
init_trace=0.0021, iter=8, convergence=0.001,
parallel=False)
csd = ConstrainedSphericalDeconvModel(gtab, response)
csd_fit = csd.fit(data)
assert_equal(np.all(csd_fit.shm_coeff[:, 0] >= 0), True)
fodf = csd_fit.odf(sphere)
directions_gt_single, _, _ = peak_directions(odf_gt_single, sphere)
directions_gt_cross, _, _ = peak_directions(odf_gt_cross, sphere)
directions_single, _, _ = peak_directions(fodf[8, :], sphere)
directions_cross, _, _ = peak_directions(fodf[0, :], sphere)
ang_sim = angular_similarity(directions_cross, directions_gt_cross)
assert_equal(ang_sim > 1.9, True)
assert_equal(directions_cross.shape[0], 2)
assert_equal(directions_gt_cross.shape[0], 2)
ang_sim = angular_similarity(directions_single, directions_gt_single)
assert_equal(ang_sim > 0.9, True)
assert_equal(directions_single.shape[0], 1)
assert_equal(directions_gt_single.shape[0], 1)
sphere = Sphere(xyz=gtab.gradients[where_dwi])
sf = response.on_sphere(sphere)
S = np.concatenate(([response.S0], sf))
tenmodel = dti.TensorModel(gtab, min_signal=0.001)
tenfit = tenmodel.fit(S)
FA = fractional_anisotropy(tenfit.evals)
FA_gt = fractional_anisotropy(evals)
assert_almost_equal(FA, FA_gt, 1)
def dodata(f_name,data_path):
dipy_home = pjoin(os.path.expanduser('~'), 'dipy_data')
folder = pjoin(dipy_home, data_path)
fraw = pjoin(folder, f_name+'.nii.gz')
fbval = pjoin(folder, f_name+'.bval')
fbvec = pjoin(folder, f_name+'.bvec')
flabels = pjoin(folder, f_name+'.nii-label.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
img = nib.load(fraw)
data = img.get_data()
affine = img.get_affine()
label_img = nib.load(flabels)
labels=label_img.get_data()
lap=through_label_sl.label_position(labels, labelValue=1)
dataslice = data[40:80, 20:80, lap[2][2] / 2]
#print lap[2][2]/2
#get_csd_gfa(f_name,data,gtab,dataslice)
maskdata, mask = median_otsu(data, 2, 1, False, vol_idx=range(10, 50), dilate=2) #不去背景
""" get fa and tensor evecs and ODF"""
from dipy.reconst.dti import TensorModel,mean_diffusivity
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
sphere = get_sphere('symmetric724')
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
np.save(os.getcwd()+'\zhibiao'+f_name+'_FA.npy',FA)
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img,os.getcwd()+'\zhibiao'+f_name+'_FA.nii.gz')
print('Saving "DTI_tensor_fa.nii.gz" sucessful.')
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)
nib.save(evecs_img, os.getcwd()+'\zhibiao'+f_name+'_DTI_tensor_evecs.nii.gz')
print('Saving "DTI_tensor_evecs.nii.gz" sucessful.')
MD1 = mean_diffusivity(tenfit.evals)
nib.save(nib.Nifti1Image(MD1.astype(np.float32), img.get_affine()), os.getcwd()+'\zhibiao'+f_name+'_MD.nii.gz')
#tensor_odfs = tenmodel.fit(data[20:50, 55:85, 38:39]).odf(sphere)
#from dipy.reconst.odf import gfa
#dti_gfa=gfa(tensor_odfs)
wm_mask = (np.logical_or(FA >= 0.4, (np.logical_and(FA >= 0.15, MD >= 0.0011))))
response = recursive_response(gtab, data, mask=wm_mask, sh_order=8,
peak_thr=0.01, init_fa=0.08,
init_trace=0.0021, iter=8, convergence=0.001,
parallel=False)
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
#csd_fit = csd_model.fit(data)
from dipy.direction import peaks_from_model
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=False)
GFA = csd_peaks.gfa
nib.save(GFA, os.getcwd()+'\zhibiao'+f_name+'_MSD.nii.gz')
print('Saving "GFA.nii.gz" sucessful.')
from dipy.reconst.shore import ShoreModel
asm = ShoreModel(gtab)
print('Calculating...SHORE msd')
asmfit = asm.fit(data,mask)
msd = asmfit.msd()
msd[np.isnan(msd)] = 0
#print GFA[:,:,slice].T
print('Saving msd_img.png')
nib.save(msd, os.getcwd()+'\zhibiao'+f_name+'_GFA.nii.gz')
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
# ---------------------------------------------------------------
print('Fitting the free water DTI model...')
# ---------------------------------------------------------------
t0 = time.time()
fw_params = nls_fit_tensor(gtab, data, mask)
dt = time.time() - t0
print("This step took %f seconds to run" % dt)
# ----------------------------------------------------------------
print('Compute tensor statistic from the fitted parameters...')
# ----------------------------------------------------------------
evals = fw_params[..., :3]
FA = dti.fractional_anisotropy(evals)
MD = dti.mean_diffusivity(evals)
F = fw_params[..., 12]
# ----------------------------------------------------------------
print('Compute standard DTI for comparison...')
# ----------------------------------------------------------------
dtimodel = dti.TensorModel(gtab)
dtifit = dtimodel.fit(data, mask=mask)
dti_FA = dtifit.fa
dti_MD = dtifit.md
# ----------------------------------------------------------------
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)))
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 tens_mod_fa_est(gtab_file, dwi_file, B0_mask):
"""
Estimate a tensor FA image to use for registrations.
Parameters
----------
gtab_file : str
File path to pickled DiPy gradient table object.
dwi_file : str
File path to diffusion weighted image.
B0_mask : str
File path to B0 brain mask.
Returns
-------
fa_path : str
File path to FA Nifti1Image.
B0_mask : str
File path to B0 brain mask Nifti1Image.
gtab_file : str
File path to pickled DiPy gradient table object.
dwi_file : str
File path to diffusion weighted Nifti1Image.
fa_md_path : str
File path to FA/MD mask Nifti1Image.
"""
import os
from dipy.io import load_pickle
from dipy.reconst.dti import TensorModel
from dipy.reconst.dti import fractional_anisotropy, mean_diffusivity
gtab = load_pickle(gtab_file)
data = nib.load(dwi_file, mmap=False).get_fdata(dtype=np.float32)
print("Generating tensor FA image to use for registrations...")
nodif_B0_img = nib.load(B0_mask, mmap=False)
nodif_B0_mask_data = nodif_B0_img.get_fdata().astype("bool")
model = TensorModel(gtab)
mod = model.fit(data, nodif_B0_mask_data)
FA = fractional_anisotropy(mod.evals)
# MD = mean_diffusivity(mod.evals)
# FA_MD = np.logical_or(
# FA >= 0.2, (np.logical_and(
# FA >= 0.08, MD >= 0.0011)))
# FA_MD[np.isnan(FA_MD)] = 0
FA = np.nan_to_num(np.asarray(FA.astype('float32')))
fa_path = f"{os.path.dirname(B0_mask)}{'/tensor_fa.nii.gz'}"
nib.save(nib.Nifti1Image(FA, nodif_B0_img.affine), fa_path)
# md_path = f"{os.path.dirname(B0_mask)}{'/tensor_md.nii.gz'}"
# nib.save(
# nib.Nifti1Image(
# MD.astype(
# np.float32),
# nodif_B0_img.affine),
# md_path)
nodif_B0_img.uncache()
del FA
return fa_path, B0_mask, gtab_file, dwi_file
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:
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')
def prepare_data_for_actors(dwi_filename,
bvals_filename,
bvecs_filename,
target_template_filename,
slices_choice,
shells=None):
# Load and prepare the data
dwi_img = nib.load(dwi_filename)
dwi_data = dwi_img.get_data()
dwi_affine = dwi_img.get_affine()
bvals, bvecs = read_bvals_bvecs(bvals_filename, bvecs_filename)
target_template_img = nib.load(target_template_filename)
target_template_data = target_template_img.get_data()
target_template_affine = target_template_img.affine
mask_data = np.zeros(target_template_data.shape)
mask_data[target_template_data > 0] = 1
# Prepare mask for tensors fit
x_slice, y_slice, z_slice = slices_choice
mask_data = prepare_slices_mask(mask_data, x_slice, y_slice, z_slice)
# Extract B0
gtab = gradient_table(bvals, normalize_bvecs(bvecs), b0_threshold=10)
b0_idx = np.where(gtab.b0s_mask)[0]
mean_b0 = np.mean(dwi_data[..., b0_idx], axis=3, dtype=dwi_data.dtype)
if shells:
indices = [get_shell_indices(bvals, shell) for shell in shells]
indices = np.sort(np.hstack(indices))
if len(indices) < 1:
raise ValueError(
'There are no volumes that have the supplied b-values.')
shell_data = np.zeros((dwi_data.shape[:-1] + (len(indices), )),
dtype=dwi_data.dtype)
shell_bvecs = np.zeros((len(indices), 3))
shell_bvals = np.zeros((len(indices), ))
for i, indice in enumerate(indices):
shell_data[..., i] = dwi_data[..., indice]
shell_bvals[i] = bvals[indice]
shell_bvecs[i, :] = bvecs[indice, :]
else:
shell_data = dwi_data
shell_bvals = bvals
shell_bvecs = bvecs
# Register the DWI data to the template
transformed_dwi, transformation = register_image(
target_template_data,
target_template_affine,
mean_b0,
dwi_affine,
transformation_type='rigid',
dwi=shell_data)
# Rotate gradients
rotated_bvecs = np.dot(shell_bvecs, transformation[0:3, 0:3])
rotated_bvecs = normalize_bvecs(rotated_bvecs)
rotated_gtab = gradient_table(shell_bvals, rotated_bvecs, b0_threshold=10)
# Get tensors
tensor_model = TensorModel(rotated_gtab, fit_method='LS')
tensor_fit = tensor_model.fit(transformed_dwi, mask_data)
# Get FA
fa_map = np.clip(fractional_anisotropy(tensor_fit.evals), 0, 1)
# Get eigen vals/vecs
evals = np.zeros(target_template_data.shape + (1, ))
evals[..., 0] = tensor_fit.evals[..., 0] / np.max(tensor_fit.evals[..., 0])
evecs = np.zeros(target_template_data.shape + (1, 3))
evecs[:, :, :, 0, :] = tensor_fit.evecs[..., 0]
return fa_map, evals, evecs
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).T
# 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)
# 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
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 test_fa_of_zero():
evals = np.zeros((4, 3))
fa = fractional_anisotropy(evals)
assert_array_equal(fa, 0)
def compute_tensors(self, dti_vol, atlas_file, gtab):
# WGR:TODO figure out how to organize tensor options and formats
# WGR:TODO figure out how to deal with files on disk vs. in workspace
"""
Takes registered DTI image and produces tensors
**Positional Arguments:**
dti_vol:
- Registered DTI volume, from workspace.
atlas_file:
- File containing an atlas (or brain mask).
gtab:
- Structure containing dipy formatted bval/bvec information
"""
labeldata = nib.load(atlas_file)
label = labeldata.get_data()
"""
Create a brain mask. Here we just threshold labels.
"""
mask = (label > 0)
gtab.info
print data.shape
"""
For the constrained spherical deconvolution we need to estimate the
response function (see :ref:`example_reconst_csd`) and create a model.
"""
response, ratio = auto_response(gtab, dti_vol, roi_radius=10,
fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
"""
Next, we use ``peaks_from_model`` to fit the data and calculated
the fiber directions in all voxels.
"""
sphere = get_sphere('symmetric724')
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
mask=mask,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=True)
"""
For the tracking part, we will use ``csd_model`` fiber directions
but stop tracking where fractional anisotropy (FA) is low (< 0.1).
To derive the FA, used as a stopping criterion, we need to fit a
tensor model first. Here, we use weighted least squares (WLS).
"""
print 'tensors...'
tensor_model = TensorModel(gtab, fit_method='WLS')
tensor_fit = tensor_model.fit(data, mask)
FA = fractional_anisotropy(tensor_fit.evals)
"""
In order for the stopping values to be used with our tracking
algorithm we need to have the same dimensions as the
``csd_peaks.peak_values``. For this reason, we can assign the
same FA value to every peak direction in the same voxel in
the following way.
"""
stopping_values = np.zeros(csd_peaks.peak_values.shape)
stopping_values[:] = FA[..., None]
print datetime.now() - startTime
pass
from dipy.io.pickles import save_pickle, load_pickle
from time import time
threshold = 0.75
from dipy.data import get_sphere
sphere = get_sphere('symmetric724')
dname = 'SNR20/'
if __name__ == '__main__':
data, affine, gtab = get_test_hardi(snr=20, denoised=0)
mask = get_test_mask()
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
nib.save(nib.Nifti1Image(FA.astype('float32'), affine),
'FA.nii.gz')
for i in range(27) :
print 'White matter bundle: ', i
wm_mask = get_test_wm_mask(i)
print(FA[wm_mask].max())
indicesAniso = np.where(np.logical_and(FA > threshold, wm_mask))
print ' Response function'
S0s = data[indicesAniso][:, np.nonzero(gtab.b0s_mask)[0]]
S0 = np.mean(S0s)
if S0 == 0 :
S0 = 1
def getFAfromSix(sixArr, axis=-1):
mat = six2mat(sixArr)
eig = np.linalg.eig(mat)[0]
fa = fractional_anisotropy(eig)
return fa