def test_warp_montage_volume():
"""Test warping an montage based on intracranial electrode positions."""
import nibabel as nib
subject_T1 = nib.load(op.join(subjects_dir, 'sample', 'mri', 'T1.mgz'))
subject_brain = nib.load(
op.join(subjects_dir, 'sample', 'mri', 'brain.mgz'))
template_brain = nib.load(
op.join(subjects_dir, 'fsaverage', 'mri', 'brain.mgz'))
reg_affine, sdr_morph = compute_volume_registration(
subject_brain,
template_brain,
zooms=5,
niter=dict(translation=[5, 5, 5], rigid=[5, 5, 5], sdr=[3, 3, 3]),
pipeline=('translation', 'rigid', 'sdr'))
# make fake image with three coordinates
CT_data = np.zeros(subject_brain.shape)
# make electrode contact hyperintensities
CT_data[45:47, 39:41, 49:50] = 500 # surround high intensity
CT_data[46, 40, 49] = 1000 # center even higher intensity
CT_data[47:49, 39:40, 49:50] = 500
CT_data[48, 39, 50] = 1000
CT_data[50:52, 38:40, 50:51] = 500
CT_data[50, 39, 50] = 1000
CT = nib.Nifti1Image(CT_data, subject_T1.affine)
ch_coords = np.array([[-8.7040273, 17.99938754, 10.29604017],
[-14.03007764, 19.69978401, 12.07236939],
[-21.1130506, 21.98310911, 13.25658887]])
ch_pos = dict(zip(['1', '2', '3'], ch_coords / 1000)) # mm -> m
montage = make_dig_montage(ch_pos, coord_frame='mri')
montage_warped, image_from, image_to = warp_montage_volume(
montage,
CT,
reg_affine,
sdr_morph,
'sample',
subjects_dir=subjects_dir,
thresh=0.99)
# checked with nilearn plot from `tut-ieeg-localize`
# check montage in surface RAS
ground_truth_warped = np.array([[-0.27778788, 0.24251515, -0.35693939],
[-0.30033333, 0.24785714, -0.35014286],
[-0.32261947, 0.25295575, -0.34614159]])
for i in range(len(montage.ch_names)):
assert np.linalg.norm( # off by less than 1.5 cm
montage_warped.dig[i]['r'] - ground_truth_warped[i]) < 0.015
# check image_from
assert_array_equal(np.array(np.where(_get_img_fdata(image_from) == 1)),
np.array([[45, 46, 46], [40, 39, 40], [49, 49, 49]]))
assert_array_equal(np.array(np.where(_get_img_fdata(image_from) == 2)),
np.array([[48, 48], [39, 39], [49, 50]]))
assert_array_equal(np.array(np.where(_get_img_fdata(image_from) == 3)),
np.array([[50, 50, 51], [38, 39, 39], [50, 50, 50]]))
# check image_to, too many, just check center
ground_truth_warped_voxels = np.array(
[[135.5959596, 161.97979798, 123.83838384],
[143.11111111, 159.71428571, 125.61904762],
[150.53982301, 158.38053097, 127.31858407]])
for i in range(len(montage.ch_names)):
assert np.linalg.norm(
np.array(np.where(_get_img_fdata(image_to) == i + 1)).mean(
axis=1) - ground_truth_warped_voxels[i]) < 5
# test inputs
with pytest.raises(ValueError, match='`thresh` must be between 0 and 1'):
warp_montage_volume(montage,
CT,
reg_affine,
sdr_morph,
'sample',
thresh=11.)
with pytest.raises(ValueError, match='subject folder is incorrect'):
warp_montage_volume(montage,
CT,
reg_affine,
sdr_morph,
subject_from='foo')
CT_unaligned = nib.Nifti1Image(CT_data, subject_brain.affine)
with pytest.raises(RuntimeError, match='not aligned to Freesurfer'):
warp_montage_volume(montage,
CT_unaligned,
reg_affine,
sdr_morph,
'sample',
subjects_dir=subjects_dir)
bad_montage = make_dig_montage(ch_pos, coord_frame='mri')
bad_montage.dig[0]['coord_frame'] = 99
with pytest.raises(RuntimeError,
match='Only single coordinate frame in '
'dig is supported'):
warp_montage_volume(bad_montage,
CT,
reg_affine,
sdr_morph,
'sample',
subjects_dir=subjects_dir)
wrong_montage = make_dig_montage(ch_pos, coord_frame='head')
with pytest.raises(RuntimeError, match='Coordinate frame not supported'):
warp_montage_volume(wrong_montage,
CT,
reg_affine,
sdr_morph,
'sample',
subjects_dir=subjects_dir)
# check channel not warped
ch_pos_doubled = ch_pos.copy()
ch_pos_doubled.update(zip(['4', '5', '6'], ch_coords / 1000))
doubled_montage = make_dig_montage(ch_pos_doubled, coord_frame='mri')
with pytest.warns(RuntimeWarning, match='not assigned'):
warp_montage_volume(doubled_montage,
CT,
reg_affine,
sdr_morph,
'sample',
subjects_dir=subjects_dir)
def test_warp_montage_volume():
"""Test warping an montage based on intracranial electrode positions."""
import nibabel as nib
subject_brain = nib.load(
op.join(subjects_dir, 'sample', 'mri', 'brain.mgz'))
template_brain = nib.load(
op.join(subjects_dir, 'fsaverage', 'mri', 'brain.mgz'))
zooms = dict(translation=10, rigid=10, sdr=10)
reg_affine, sdr_morph = compute_volume_registration(
subject_brain, template_brain, zooms=zooms,
niter=[3, 3, 3],
pipeline=('translation', 'rigid', 'sdr'))
# make an info object with three channels with positions
ch_coords = np.array([[-8.7040273, 17.99938754, 10.29604017],
[-14.03007764, 19.69978401, 12.07236939],
[-21.1130506, 21.98310911, 13.25658887]])
ch_pos = dict(zip(['1', '2', '3'], ch_coords / 1000)) # mm -> m
lpa, nasion, rpa = get_mni_fiducials('sample', subjects_dir)
montage = make_dig_montage(ch_pos, lpa=lpa['r'], nasion=nasion['r'],
rpa=rpa['r'], coord_frame='mri')
# make fake image based on the info
CT_data = np.zeros(subject_brain.shape)
# convert to voxels
ch_coords_vox = apply_trans(
np.linalg.inv(subject_brain.header.get_vox2ras_tkr()), ch_coords)
for (x, y, z) in ch_coords_vox.round().astype(int):
# make electrode contact hyperintensities
# first, make the surrounding voxels high intensity
CT_data[x - 1:x + 2, y - 1:y + 2, z - 1:z + 2] = 500
# then, make the center even higher intensity
CT_data[x, y, z] = 1000
CT = nib.Nifti1Image(CT_data, subject_brain.affine)
ch_coords = np.array([[-8.7040273, 17.99938754, 10.29604017],
[-14.03007764, 19.69978401, 12.07236939],
[-21.1130506, 21.98310911, 13.25658887]])
ch_pos = dict(zip(['1', '2', '3'], ch_coords / 1000)) # mm -> m
lpa, nasion, rpa = get_mni_fiducials('sample', subjects_dir)
montage = make_dig_montage(ch_pos, lpa=lpa['r'], nasion=nasion['r'],
rpa=rpa['r'], coord_frame='mri')
montage_warped, image_from, image_to = warp_montage_volume(
montage, CT, reg_affine, sdr_morph, 'sample',
subjects_dir_from=subjects_dir, thresh=0.99)
# checked with nilearn plot from `tut-ieeg-localize`
# check montage in surface RAS
ground_truth_warped = np.array([[-0.009, -0.00133333, -0.033],
[-0.01445455, 0.00127273, -0.03163636],
[-0.022, 0.00285714, -0.031]])
for i, d in enumerate(montage_warped.dig):
assert np.linalg.norm( # off by less than 1.5 cm
d['r'] - ground_truth_warped[i]) < 0.015
# check image_from
for idx, contact in enumerate(range(1, len(ch_pos) + 1)):
voxels = np.array(np.where(np.array(image_from.dataobj) == contact)).T
assert ch_coords_vox.round()[idx] in voxels
assert ch_coords_vox.round()[idx] + 5 not in voxels
# check image_to, too many, just check center
ground_truth_warped_voxels = np.array(
[[135.5959596, 161.97979798, 123.83838384],
[143.11111111, 159.71428571, 125.61904762],
[150.53982301, 158.38053097, 127.31858407]])
for i in range(len(montage.ch_names)):
assert np.linalg.norm(
np.array(np.where(np.array(image_to.dataobj) == i + 1)
).mean(axis=1) - ground_truth_warped_voxels[i]) < 8
# test inputs
with pytest.raises(ValueError, match='`thresh` must be between 0 and 1'):
warp_montage_volume(
montage, CT, reg_affine, sdr_morph, 'sample', thresh=11.)
with pytest.raises(ValueError, match='subject folder is incorrect'):
warp_montage_volume(
montage, CT, reg_affine, sdr_morph, subject_from='foo')
CT_unaligned = nib.Nifti1Image(CT_data, template_brain.affine)
with pytest.raises(RuntimeError, match='not aligned to Freesurfer'):
warp_montage_volume(montage, CT_unaligned, reg_affine,
sdr_morph, 'sample',
subjects_dir_from=subjects_dir)
bad_montage = montage.copy()
for d in bad_montage.dig:
d['coord_frame'] = 99
with pytest.raises(RuntimeError, match='Coordinate frame not supported'):
warp_montage_volume(bad_montage, CT, reg_affine,
sdr_morph, 'sample',
subjects_dir_from=subjects_dir)
# check channel not warped
ch_pos_doubled = ch_pos.copy()
ch_pos_doubled.update(zip(['4', '5', '6'], ch_coords / 1000))
doubled_montage = make_dig_montage(
ch_pos_doubled, lpa=lpa['r'], nasion=nasion['r'],
rpa=rpa['r'], coord_frame='mri')
with pytest.warns(RuntimeWarning, match='not assigned'):
warp_montage_volume(doubled_montage, CT, reg_affine,
None, 'sample', subjects_dir_from=subjects_dir)