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[Fix] vol_idx missing in snr_in_cc Tutorial #1914

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merged 2 commits into from Jul 21, 2019
Merged

[Fix] vol_idx missing in snr_in_cc Tutorial #1914

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1ba18c6
add vol_idx to median_otsu
skoudoro Jul 19, 2019
00268cc
fix some pep8
skoudoro Jul 19, 2019
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34 changes: 20 additions & 14 deletions doc/examples/snr_in_cc.py
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Expand Up @@ -42,21 +42,25 @@
affine = img.affine

print('Computing brain mask...')
b0_mask, mask = median_otsu(data)
b0_mask, mask = median_otsu(data, vol_idx=[0])

print('Computing tensors...')
tenmodel = TensorModel(gtab)
tensorfit = tenmodel.fit(data, mask=mask)

"""Next, we set our red-green-blue thresholds to (0.6, 1) in the x axis
and (0, 0.1) in the y and z axes respectively.
These values work well in practice to isolate the very RED voxels of the cfa map.
"""
Next, we set our red-green-blue thresholds to (0.6, 1) in the x axis and
(0, 0.1) in the y and z axes respectively. These values work well in practice
to isolate the very RED voxels of the cfa map.

Then, as assurance, we want just RED voxels in the CC (there could be noisy
red voxels around the brain mask and we don't want those). Unless the brain
acquisition was badly aligned, the CC is always close to the mid-sagittal
slice.

Then, as assurance, we want just RED voxels in the CC (there could be
noisy red voxels around the brain mask and we don't want those). Unless the brain
acquisition was badly aligned, the CC is always close to the mid-sagittal slice.
The following lines perform these two operations and then saves the
computed mask.

The following lines perform these two operations and then saves the computed mask.
"""

print('Computing worst-case/best-case SNR using the corpus callosum...')
Expand All @@ -78,7 +82,7 @@
bounds_min[2]:bounds_max[2]] = 1

mask_cc_part, cfa = segment_from_cfa(tensorfit, CC_box, threshold,
return_cfa=True)
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)
Expand All @@ -105,8 +109,9 @@

"""

"""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.
"""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.

"""

Expand Down Expand Up @@ -143,10 +148,11 @@

for direction in [0, axis_X, axis_Y, axis_Z]:
SNR = mean_signal[direction]/noise_std
if direction == 0 :
if direction == 0:
print("SNR for the b=0 image is :", SNR)
else :
print("SNR for direction", direction, " ", gtab.bvecs[direction], "is :", SNR)
else:
print("SNR for direction", direction, " ",
gtab.bvecs[direction], "is :", SNR)

"""SNR for the b=0 image is : ''42.0695455758''"""
"""SNR for direction 58 [ 0.98875 0.1177 -0.09229] is : ''5.46995373635''"""
Expand Down