threshold = (0.6, 1, 0, 0.1, 0, 0.1)
CC_box = np.zeros_like(data[..., 0])
mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 4
bounds_min = mins + diff
bounds_max = maxs - diff
CC_box[bounds_min[0]:bounds_max[0], bounds_min[1]:bounds_max[1],
bounds_min[2]:bounds_max[2]] = 1
mask_cc_part, cfa = segment_from_cfa(tensorfit,
CC_box,
threshold,
return_cfa=True)
cfa_img = nib.Nifti1Image((cfa * 255).astype(np.uint8), affine)
mask_cc_part_img = nib.Nifti1Image(mask_cc_part.astype(np.uint8), affine)
nib.save(mask_cc_part_img, 'cc.nii.gz')
"""Now that we are happy with our crude CC mask that selected voxels in the x-direction,
we can use all the voxels to estimate the mean signal in this region.
"""
mean_signal = np.mean(data[mask_cc_part], axis=0)
"""Now, we need a good background estimation. We will re-use the brain mask
computed before and invert it to catch the outside of the brain. This could
also be determined manually with a ROI in the background.
[Warning: Certain MR manufacturers mask out the outside of the brain with 0's.
def run(self, data_files, bvals_files, bvecs_files, mask_file,
bbox_threshold=[0.6, 1, 0, 0.1, 0, 0.1], out_dir='',
out_file='product.json', out_mask_cc='cc.nii.gz',
out_mask_noise='mask_noise.nii.gz'):
"""Compute the signal-to-noise ratio in the corpus callosum.
Parameters
----------
data_files : string
Path to the dwi.nii.gz file. This path may contain wildcards to
process multiple inputs at once.
bvals_files : string
Path of bvals.
bvecs_files : string
Path of bvecs.
mask_file : string
Path of a brain mask file.
bbox_threshold : variable float, optional
Threshold for bounding box, values separated with commas for ex.
[0.6,1,0,0.1,0,0.1]. (default (0.6, 1, 0, 0.1, 0, 0.1))
out_dir : string, optional
Where the resulting file will be saved. (default '')
out_file : string, optional
Name of the result file to be saved. (default 'product.json')
out_mask_cc : string, optional
Name of the CC mask volume to be saved (default 'cc.nii.gz')
out_mask_noise : string, optional
Name of the mask noise volume to be saved
(default 'mask_noise.nii.gz')
"""
io_it = self.get_io_iterator()
for dwi_path, bvals_path, bvecs_path, mask_path, out_path, \
cc_mask_path, mask_noise_path in io_it:
data, affine = load_nifti(dwi_path)
bvals, bvecs = read_bvals_bvecs(bvals_path, bvecs_path)
gtab = gradient_table(bvals=bvals, bvecs=bvecs)
mask, affine = load_nifti(mask_path)
logging.info('Computing tensors...')
tenmodel = TensorModel(gtab)
tensorfit = tenmodel.fit(data, mask=mask)
logging.info(
'Computing worst-case/best-case SNR using the CC...')
if np.ndim(data) == 4:
CC_box = np.zeros_like(data[..., 0])
elif np.ndim(data) == 3:
CC_box = np.zeros_like(data)
else:
raise IOError('DWI data has invalid dimensions')
mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 4
bounds_min = mins + diff
bounds_max = maxs - diff
CC_box[bounds_min[0]:bounds_max[0],
bounds_min[1]:bounds_max[1],
bounds_min[2]:bounds_max[2]] = 1
if len(bbox_threshold) != 6:
raise IOError('bbox_threshold should have 6 float values')
mask_cc_part, cfa = segment_from_cfa(tensorfit, CC_box,
bbox_threshold,
return_cfa=True)
save_nifti(cc_mask_path, mask_cc_part.astype(np.uint8), affine)
logging.info('CC mask saved as {0}'.format(cc_mask_path))
mean_signal = np.mean(data[mask_cc_part], axis=0)
mask_noise = binary_dilation(mask, iterations=10)
mask_noise[..., :mask_noise.shape[-1]//2] = 1
mask_noise = ~mask_noise
save_nifti(mask_noise_path, mask_noise.astype(np.uint8), affine)
logging.info('Mask noise saved as {0}'.format(mask_noise_path))
noise_std = np.std(data[mask_noise, :])
logging.info('Noise standard deviation sigma= ' + str(noise_std))
idx = np.sum(gtab.bvecs, axis=-1) == 0
gtab.bvecs[idx] = np.inf
axis_X = np.argmin(
np.sum((gtab.bvecs-np.array([1, 0, 0])) ** 2, axis=-1))
axis_Y = np.argmin(
np.sum((gtab.bvecs-np.array([0, 1, 0])) ** 2, axis=-1))
axis_Z = np.argmin(
np.sum((gtab.bvecs-np.array([0, 0, 1])) ** 2, axis=-1))
SNR_output = []
SNR_directions = []
for direction in ['b0', axis_X, axis_Y, axis_Z]:
if direction == 'b0':
SNR = mean_signal[0]/noise_std
logging.info("SNR for the b=0 image is :" + str(SNR))
else:
logging.info("SNR for direction " + str(direction) +
" " + str(gtab.bvecs[direction]) + "is :" +
str(SNR))
SNR_directions.append(direction)
SNR = mean_signal[direction]/noise_std
SNR_output.append(SNR)
data = []
data.append({
'data': str(SNR_output[0]) + ' ' + str(SNR_output[1]) +
' ' + str(SNR_output[2]) + ' ' + str(SNR_output[3]),
'directions': 'b0' + ' ' + str(SNR_directions[0]) +
' ' + str(SNR_directions[1]) + ' ' +
str(SNR_directions[2])
})
with open(os.path.join(out_dir, out_path), 'w') as myfile:
json.dump(data, myfile)
from dipy.segment.mask import segment_from_cfa
from dipy.segment.mask import bounding_box
threshold = (0.6, 1, 0, 0.1, 0, 0.1)
CC_box = np.zeros_like(data[..., 0])
mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 4
bounds_min = mins + diff
bounds_max = maxs - diff
CC_box[bounds_min[0] : bounds_max[0], bounds_min[1] : bounds_max[1], bounds_min[2] : bounds_max[2]] = 1
mask_cc_part, cfa = segment_from_cfa(tensorfit, CC_box, threshold, return_cfa=True)
cfa_img = nib.Nifti1Image((cfa * 255).astype(np.uint8), affine)
mask_cc_part_img = nib.Nifti1Image(mask_cc_part.astype(np.uint8), affine)
nib.save(mask_cc_part_img, "mask_CC_part.nii.gz")
import matplotlib.pyplot as plt
region = 40
fig = plt.figure("Corpus callosum segmentation")
plt.subplot(1, 2, 1)
plt.title("Corpus callosum (CC)")
plt.axis("off")
red = cfa[..., 0]
plt.imshow(np.rot90(red[region, ...]))
def run(self, data_files, bvals_files, bvecs_files, mask_files,
bbox_threshold=[0.6, 1, 0, 0.1, 0, 0.1], out_dir='',
out_file='product.json', out_mask_cc='cc.nii.gz',
out_mask_noise='mask_noise.nii.gz'):
"""Compute the signal-to-noise ratio in the corpus callosum.
Parameters
----------
data_files : string
Path to the dwi.nii.gz file. This path may contain wildcards to
process multiple inputs at once.
bvals_files : string
Path of bvals.
bvecs_files : string
Path of bvecs.
mask_files : string
Path of brain mask
bbox_threshold : variable float, optional
Threshold for bounding box, values separated with commas for ex.
[0.6,1,0,0.1,0,0.1]. (default (0.6, 1, 0, 0.1, 0, 0.1))
out_dir : string, optional
Where the resulting file will be saved. (default '')
out_file : string, optional
Name of the result file to be saved. (default 'product.json')
out_mask_cc : string, optional
Name of the CC mask volume to be saved (default 'cc.nii.gz')
out_mask_noise : string, optional
Name of the mask noise volume to be saved
(default 'mask_noise.nii.gz')
"""
io_it = self.get_io_iterator()
for dwi_path, bvals_path, bvecs_path, mask_path, out_path, \
cc_mask_path, mask_noise_path in io_it:
data, affine = load_nifti(dwi_path)
bvals, bvecs = read_bvals_bvecs(bvals_path, bvecs_path)
gtab = gradient_table(bvals=bvals, bvecs=bvecs)
logging.info('Computing brain mask...')
_, calc_mask = median_otsu(data)
mask, affine = load_nifti(mask_path)
mask = np.array(calc_mask == mask.astype(bool)).astype(int)
logging.info('Computing tensors...')
tenmodel = TensorModel(gtab)
tensorfit = tenmodel.fit(data, mask=mask)
logging.info(
'Computing worst-case/best-case SNR using the CC...')
if np.ndim(data) == 4:
CC_box = np.zeros_like(data[..., 0])
elif np.ndim(data) == 3:
CC_box = np.zeros_like(data)
else:
raise IOError('DWI data has invalid dimensions')
mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 4
bounds_min = mins + diff
bounds_max = maxs - diff
CC_box[bounds_min[0]:bounds_max[0],
bounds_min[1]:bounds_max[1],
bounds_min[2]:bounds_max[2]] = 1
if len(bbox_threshold) != 6:
raise IOError('bbox_threshold should have 6 float values')
mask_cc_part, cfa = segment_from_cfa(tensorfit, CC_box,
bbox_threshold,
return_cfa=True)
save_nifti(cc_mask_path, mask_cc_part.astype(np.uint8), affine)
logging.info('CC mask saved as {0}'.format(cc_mask_path))
mean_signal = np.mean(data[mask_cc_part], axis=0)
mask_noise = binary_dilation(mask, iterations=10)
mask_noise[..., :mask_noise.shape[-1]//2] = 1
mask_noise = ~mask_noise
save_nifti(mask_noise_path, mask_noise.astype(np.uint8), affine)
logging.info('Mask noise saved as {0}'.format(mask_noise_path))
noise_std = np.std(data[mask_noise, :])
logging.info('Noise standard deviation sigma= ' + str(noise_std))
idx = np.sum(gtab.bvecs, axis=-1) == 0
gtab.bvecs[idx] = np.inf
axis_X = np.argmin(
np.sum((gtab.bvecs-np.array([1, 0, 0])) ** 2, axis=-1))
axis_Y = np.argmin(
np.sum((gtab.bvecs-np.array([0, 1, 0])) ** 2, axis=-1))
axis_Z = np.argmin(
np.sum((gtab.bvecs-np.array([0, 0, 1])) ** 2, axis=-1))
SNR_output = []
SNR_directions = []
for direction in ['b0', axis_X, axis_Y, axis_Z]:
if direction == 'b0':
SNR = mean_signal[0]/noise_std
logging.info("SNR for the b=0 image is :" + str(SNR))
else:
logging.info("SNR for direction " + str(direction) +
" " + str(gtab.bvecs[direction]) + "is :" +
str(SNR))
SNR_directions.append(direction)
SNR = mean_signal[direction]/noise_std
SNR_output.append(SNR)
data = []
data.append({
'data': str(SNR_output[0]) + ' ' + str(SNR_output[1]) +
' ' + str(SNR_output[2]) + ' ' + str(SNR_output[3]),
'directions': 'b0' + ' ' + str(SNR_directions[0]) +
' ' + str(SNR_directions[1]) + ' ' +
str(SNR_directions[2])
})
with open(os.path.join(out_dir, out_path), 'w') as myfile:
json.dump(data, myfile)
def run(self,
data_file,
data_bvals,
data_bvecs,
mask=None,
bbox_threshold=(0.6, 1, 0, 0.1, 0, 0.1),
out_dir='',
out_file='product.json',
out_mask_cc='cc.nii.gz',
out_mask_noise='mask_noise.nii.gz'):
""" Workflow for computing the signal-to-noise ratio in the
corpus callosum
Parameters
----------
data_file : string
Path to the dwi.nii.gz file. This path may contain wildcards to
process multiple inputs at once.
data_bvals : string
Path of bvals.
data_bvecs : string
Path of bvecs.
mask : string, optional
Path of mask if desired. (default None)
bbox_threshold : string, optional
Threshold for bounding box, values separated with commas for ex.
[0.6,1,0,0.1,0,0.1]. (default (0.6, 1, 0, 0.1, 0, 0.1))
out_dir : string, optional
Where the resulting file will be saved. (default '')
out_file : string, optional
Name of the result file to be saved. (default 'product.json')
out_mask_cc : string, optional
Name of the CC mask volume to be saved (default 'cc.nii.gz')
out_mask_noise : string, optional
Name of the mask noise volume to be saved
(default 'mask_noise.nii.gz')
"""
if not isinstance(bbox_threshold, tuple):
b = bbox_threshold.replace("[", "")
b = b.replace("]", "")
b = b.replace("(", "")
b = b.replace(")", "")
b = b.replace(" ", "")
b = b.split(",")
for i in range(len(b)):
b[i] = float(b[i])
bbox_threshold = tuple(b)
io_it = self.get_io_iterator()
for data_path, data_bvals_path, data_bvecs_path, out_path, \
cc_mask_path, mask_noise_path in io_it:
img = nib.load('{0}'.format(data_path))
bvals, bvecs = read_bvals_bvecs('{0}'.format(data_bvals_path),
'{0}'.format(data_bvecs_path))
gtab = gradient_table(bvals, bvecs)
data = img.get_data()
affine = img.affine
logging.info('Computing brain mask...')
b0_mask, calc_mask = median_otsu(data)
if mask is None:
mask = calc_mask
else:
mask = nib.load(mask).get_data().astype(bool)
mask = np.array(calc_mask == mask).astype(int)
logging.info('Computing tensors...')
tenmodel = TensorModel(gtab)
tensorfit = tenmodel.fit(data, mask=mask)
logging.info('Computing worst-case/best-case SNR using the CC...')
threshold = bbox_threshold
if np.ndim(data) == 4:
CC_box = np.zeros_like(data[..., 0])
elif np.ndim(data) == 3:
CC_box = np.zeros_like(data)
else:
raise IOError('DWI data has invalid dimensions')
mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 4
bounds_min = mins + diff
bounds_max = maxs - diff
CC_box[bounds_min[0]:bounds_max[0], bounds_min[1]:bounds_max[1],
bounds_min[2]:bounds_max[2]] = 1
mask_cc_part, cfa = segment_from_cfa(tensorfit,
CC_box,
threshold,
return_cfa=True)
cfa_img = nib.Nifti1Image((cfa * 255).astype(np.uint8), affine)
mask_cc_part_img = nib.Nifti1Image(mask_cc_part.astype(np.uint8),
affine)
nib.save(mask_cc_part_img, cc_mask_path)
logging.info('CC mask saved as {0}'.format(cc_mask_path))
mean_signal = np.mean(data[mask_cc_part], axis=0)
mask_noise = binary_dilation(mask, iterations=10)
mask_noise[..., :mask_noise.shape[-1] // 2] = 1
mask_noise = ~mask_noise
mask_noise_img = nib.Nifti1Image(mask_noise.astype(np.uint8),
affine)
nib.save(mask_noise_img, mask_noise_path)
logging.info('Mask noise saved as {0}'.format(mask_noise_path))
noise_std = np.std(data[mask_noise, :])
logging.info('Noise standard deviation sigma= ' + str(noise_std))
idx = np.sum(gtab.bvecs, axis=-1) == 0
gtab.bvecs[idx] = np.inf
axis_X = np.argmin(
np.sum((gtab.bvecs - np.array([1, 0, 0]))**2, axis=-1))
axis_Y = np.argmin(
np.sum((gtab.bvecs - np.array([0, 1, 0]))**2, axis=-1))
axis_Z = np.argmin(
np.sum((gtab.bvecs - np.array([0, 0, 1]))**2, axis=-1))
SNR_output = []
SNR_directions = []
for direction in ['b0', axis_X, axis_Y, axis_Z]:
if direction == 'b0':
SNR = mean_signal[0] / noise_std
logging.info("SNR for the b=0 image is :" + str(SNR))
else:
logging.info("SNR for direction " + str(direction) + " " +
str(gtab.bvecs[direction]) + "is :" +
str(SNR))
SNR_directions.append(direction)
SNR = mean_signal[direction] / noise_std
SNR_output.append(SNR)
data = []
data.append({
'data':
str(SNR_output[0]) + ' ' + str(SNR_output[1]) + ' ' +
str(SNR_output[2]) + ' ' + str(SNR_output[3]),
'directions':
'b0' + ' ' + str(SNR_directions[0]) + ' ' +
str(SNR_directions[1]) + ' ' + str(SNR_directions[2])
})
with open(os.path.join(out_dir, out_file), 'w') as myfile:
json.dump(data, myfile)
threshold = (0.7, 1, 0, 0.1, 0, 0.1)
CC_box = np.zeros_like(data[..., 0])
mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 4
bounds_min = mins + diff
bounds_max = maxs - diff
CC_box[bounds_min[0]:bounds_max[0],
bounds_min[1]:bounds_max[1],
bounds_min[2]:bounds_max[2]] = 1
mask_corpus_callosum, cfa = segment_from_cfa(tensorfit, CC_box,
threshold, return_cfa=True)
print("Size of the mask :", np.count_nonzero(mask_corpus_callosum), \
"voxels out of", np.size(CC_box))
"""We can save the produced dataset with nibabel to visualize them later on.
Note that we save the cfa with values between 0 and 255 for visualization
purpose. Remember that the function works with values between
0 and 1, but it will warn you if the supplied values do not fall in this range.
"""
cfa_img = nib.Nifti1Image((cfa*255).astype(np.uint8), affine)
mask_corpus_callosum_img = nib.Nifti1Image(mask_corpus_callosum.astype(np.uint8), affine)
