write_raw_bids(
raw,
bids_path,
events_data=events_data,
event_id=event_id,
overwrite=True,
)
# MRI scan
t1_fname = op.join(subjects_dir, f"sub-{subject}", "mri", "T1.mgz")
# transformation matrix
trans = mne.read_trans(op.join(bids_root, f"sub-{subject}-trans.fif"))
t1w_bids_path = BIDSPath(subject=subject, root=bids_root, suffix="T1w")
landmarks = get_anat_landmarks(
t1_fname,
info=raw.info,
trans=trans,
fs_subject=f"sub-{subject}",
fs_subjects_dir=subjects_dir,
)
t1w_bids_path = write_anat(
image=t1_fname,
bids_path=t1w_bids_path,
landmarks=landmarks,
verbose=True,
)
anat_dir = t1w_bids_path.directory
# ERM
erm_fname = op.join(bids_root, "sub-%s_erm_raw.fif" % subject)
erm = mne.io.read_raw_fif(erm_fname, allow_maxshield="yes")
erm.info["line_freq"] = 60
er_date = erm.info["meas_date"].strftime("%Y%m%d")
def test_update_anat_landmarks(tmp_path):
"""Test updating the anatomical landmarks of an MRI scan."""
data_path = Path(testing.data_path())
raw_path = data_path / 'MEG' / 'sample' / 'sample_audvis_trunc_raw.fif'
trans_path = Path(str(raw_path).replace('_raw.fif', '-trans.fif'))
t1_path = data_path / 'subjects' / 'sample' / 'mri' / 'T1.mgz'
fs_subject = 'sample'
fs_subjects_dir = data_path / 'subjects'
bids_root = tmp_path
bids_path_mri = BIDSPath(subject=subject_id,
session=session_id,
acquisition=acq,
root=bids_root,
datatype='anat',
suffix='T1w')
# First, write the MRI scan to BIDS, including the anatomical landmarks
info = mne.io.read_info(raw_path)
trans = mne.read_trans(trans_path)
landmarks = get_anat_landmarks(image=t1_path,
info=info,
trans=trans,
fs_subject=fs_subject,
fs_subjects_dir=fs_subjects_dir)
bids_path_mri = write_anat(image=t1_path,
bids_path=bids_path_mri,
landmarks=landmarks,
deface=False)
bids_path_mri_json = bids_path_mri.copy().update(extension='.json')
# Modify the landmarks
# Move the nasion a bit
landmarks_new = landmarks.copy()
landmarks_new.dig[1]['r'] *= 0.9
update_anat_landmarks(bids_path=bids_path_mri, landmarks=landmarks_new)
with bids_path_mri_json.fpath.open(encoding='utf-8') as f:
mri_json = json.load(f)
assert np.allclose(landmarks_new.dig[1]['r'],
mri_json['AnatomicalLandmarkCoordinates']['NAS'])
# Remove JSON sidecar; updating the anatomical landmarks should re-create
# the file unless `on_missing` is `'raise'`
bids_path_mri_json.fpath.unlink()
with pytest.raises(KeyError,
match='No AnatomicalLandmarkCoordinates section found'):
update_anat_landmarks(bids_path=bids_path_mri, landmarks=landmarks_new)
update_anat_landmarks(bids_path=bids_path_mri,
landmarks=landmarks_new,
on_missing='ignore')
with pytest.raises(KeyError, match='landmark not found'):
update_anat_landmarks(bids_path=bids_path_mri,
landmarks=landmarks_new,
kind='ses-1')
update_anat_landmarks(bids_path=bids_path_mri,
landmarks=landmarks_new,
kind='ses-1',
on_missing='ignore')
mri_json = json.loads(bids_path_mri_json.fpath.read_text(encoding='utf-8'))
assert 'NAS' in mri_json['AnatomicalLandmarkCoordinates']
assert 'NAS_ses-1' in mri_json['AnatomicalLandmarkCoordinates']
assert np.allclose(landmarks_new.dig[1]['r'],
mri_json['AnatomicalLandmarkCoordinates']['NAS'])
# Check without extension provided
bids_path_mri_no_ext = bids_path_mri.copy().update(extension=None)
update_anat_landmarks(bids_path=bids_path_mri_no_ext,
landmarks=landmarks_new)
# Check without datatytpe provided
bids_path_mri_no_datatype = bids_path_mri.copy().update(datatype=None)
update_anat_landmarks(bids_path=bids_path_mri_no_datatype,
landmarks=landmarks)
# Check handling of invalid input
bids_path_invalid = bids_path_mri.copy().update(datatype='meg')
with pytest.raises(ValueError, match='Can only operate on "anat"'):
update_anat_landmarks(bids_path=bids_path_invalid, landmarks=landmarks)
bids_path_invalid = bids_path_mri.copy().update(suffix=None)
with pytest.raises(ValueError, match='lease specify the "suffix"'):
update_anat_landmarks(bids_path=bids_path_invalid, landmarks=landmarks)
bids_path_invalid = bids_path_mri.copy().update(suffix='meg')
with pytest.raises(ValueError,
match='Can only operate on "T1w" and "FLASH"'):
update_anat_landmarks(bids_path=bids_path_invalid, landmarks=landmarks)
bids_path_invalid = bids_path_mri.copy().update(subject='invalid')
with pytest.raises(ValueError, match='Could not find an MRI scan'):
update_anat_landmarks(bids_path=bids_path_invalid, landmarks=landmarks)
# Unsupported coordinate frame
landmarks_invalid = landmarks.copy()
for digpoint in landmarks_invalid.dig:
digpoint['coord_frame'] = FIFF.FIFFV_MNE_COORD_RAS
with pytest.raises(ValueError, match='must be specified in MRI voxel'):
update_anat_landmarks(bids_path=bids_path_mri,
landmarks=landmarks_invalid)
# Missing cardinal point
landmarks_invalid = landmarks.copy()
del landmarks_invalid.dig[0]
with pytest.raises(ValueError,
match='did not contain all required cardinal points'):
update_anat_landmarks(bids_path=bids_path_mri,
landmarks=landmarks_invalid)
# Test with path-like landmarks
fiducials_path = (data_path / 'subjects' / 'sample' / 'bem' /
'sample-fiducials.fif')
update_anat_landmarks(bids_path=bids_path_mri,
landmarks=fiducials_path,
fs_subject='sample',
fs_subjects_dir=data_path / 'subjects')
expected_coords_in_voxels = np.array([
[68.38202, 45.24057, 43.439808], # noqa: E241
[42.27006, 30.758774, 74.09837], # noqa: E202, E241
[17.044853, 46.586075, 42.618504]
])
mri_json = json.loads(bids_path_mri_json.fpath.read_text(encoding='utf-8'))
for landmark, expected_coords in zip(('LPA', 'NAS', 'RPA'),
expected_coords_in_voxels):
assert np.allclose(mri_json['AnatomicalLandmarkCoordinates'][landmark],
expected_coords)
def test_update_anat_landmarks(tmpdir):
"""Test updating the anatomical landmarks of an MRI scan."""
data_path = Path(testing.data_path())
raw_path = data_path / 'MEG' / 'sample' / 'sample_audvis_trunc_raw.fif'
trans_path = Path(str(raw_path).replace('_raw.fif', '-trans.fif'))
t1_path = data_path / 'subjects' / 'sample' / 'mri' / 'T1.mgz'
fs_subject = 'sample'
fs_subjects_dir = data_path / 'subjects'
bids_root = Path(tmpdir)
bids_path_mri = BIDSPath(subject=subject_id, session=session_id,
acquisition=acq, root=bids_root, datatype='anat',
suffix='T1w')
# First, write the MRI scan to BIDS, including the anatomical landmarks
info = mne.io.read_info(raw_path)
trans = mne.read_trans(trans_path)
landmarks = get_anat_landmarks(
image=t1_path, info=info, trans=trans, fs_subject=fs_subject,
fs_subjects_dir=fs_subjects_dir
)
bids_path_mri = write_anat(image=t1_path, bids_path=bids_path_mri,
landmarks=landmarks, deface=False)
bids_path_mri_json = bids_path_mri.copy().update(extension='.json')
# Modify the landmarks
# Move the nasion a bit
landmarks_new = landmarks.copy()
landmarks_new.dig[1]['r'] *= 0.9
update_anat_landmarks(bids_path=bids_path_mri, landmarks=landmarks_new)
with bids_path_mri_json.fpath.open(encoding='utf-8') as f:
mri_json = json.load(f)
assert np.allclose(
landmarks_new.dig[1]['r'],
mri_json['AnatomicalLandmarkCoordinates']['NAS']
)
# Remove JSON sidecar; updating the anatomical landmarks should re-create
# the file
bids_path_mri_json.fpath.unlink()
update_anat_landmarks(bids_path=bids_path_mri, landmarks=landmarks_new)
with bids_path_mri_json.fpath.open(encoding='utf-8') as f:
mri_json = json.load(f)
assert np.allclose(
landmarks_new.dig[1]['r'],
mri_json['AnatomicalLandmarkCoordinates']['NAS']
)
# Check without extension provided
bids_path_mri_no_ext = bids_path_mri.copy().update(extension=None)
update_anat_landmarks(bids_path=bids_path_mri_no_ext,
landmarks=landmarks_new)
# Check without datatytpe provided
bids_path_mri_no_datatype = bids_path_mri.copy().update(datatype=None)
update_anat_landmarks(bids_path=bids_path_mri_no_datatype,
landmarks=landmarks)
# Check handling of invalid input
bids_path_invalid = bids_path_mri.copy().update(datatype='meg')
with pytest.raises(ValueError, match='Can only operate on "anat"'):
update_anat_landmarks(bids_path=bids_path_invalid, landmarks=landmarks)
bids_path_invalid = bids_path_mri.copy().update(suffix=None)
with pytest.raises(ValueError, match='lease specify the "suffix"'):
update_anat_landmarks(bids_path=bids_path_invalid, landmarks=landmarks)
bids_path_invalid = bids_path_mri.copy().update(suffix='meg')
with pytest.raises(ValueError,
match='Can only operate on "T1w" and "FLASH"'):
update_anat_landmarks(bids_path=bids_path_invalid, landmarks=landmarks)
bids_path_invalid = bids_path_mri.copy().update(subject='invalid')
with pytest.raises(ValueError, match='Could not find an MRI scan'):
update_anat_landmarks(bids_path=bids_path_invalid, landmarks=landmarks)
# Unsupported coordinate frame
landmarks_invalid = landmarks.copy()
for digpoint in landmarks_invalid.dig:
digpoint['coord_frame'] = FIFF.FIFFV_MNE_COORD_RAS
with pytest.raises(ValueError, match='must be specified in MRI voxel'):
update_anat_landmarks(bids_path=bids_path_mri,
landmarks=landmarks_invalid)
# Missing cardinal point
landmarks_invalid = landmarks.copy()
del landmarks_invalid.dig[0]
with pytest.raises(ValueError,
match='did not contain all required cardinal points'):
update_anat_landmarks(bids_path=bids_path_mri,
landmarks=landmarks_invalid)
bids_path = BIDSPath(subject=subject_id, task=task, root=bids_root)
# plot T1 to show that it is ACPC-aligned
# note that the origin is centered on the anterior commissure (AC)
# with the y-axis passing through the posterior commissure (PC)
T1_fname = op.join(subjects_dir, 'sample_seeg', 'mri', 'T1.mgz')
fig = plot_anat(T1_fname, cut_coords=(0, 0, 0))
fig.axes['x'].ax.annotate('AC', (2., -2.), (30., -40.),
color='w',
arrowprops=dict(facecolor='w', alpha=0.5))
fig.axes['x'].ax.annotate('PC', (-31., -2.), (-80., -40.),
color='w',
arrowprops=dict(facecolor='w', alpha=0.5))
# write ACPC-aligned T1
landmarks = get_anat_landmarks(T1_fname, raw.info, trans, 'sample_seeg',
subjects_dir)
T1_bids_path = write_anat(T1_fname,
bids_path,
deface=True,
landmarks=landmarks)
# write `raw` to BIDS and anonymize it (converts to BrainVision format)
#
# we need to pass the `montage` argument for coordinate frames other than
# "head" which is what MNE uses internally in the `raw` object
#
# `acpc_aligned=True` affirms that our MRI is aligned to ACPC
# if this is not true, convert to `fsaverage` (see below)!
write_raw_bids(raw,
bids_path,
anonymize=dict(daysback=40000),
# consist of the coordinates of three anatomical landmarks (LPA, Nasion and
# RPA (=left and right preauricular points) expressed in voxel coordinates
# w.r.t. the T1 image.
# First create the BIDSPath object.
t1w_bids_path = BIDSPath(subject=sub,
session=ses,
root=output_path,
suffix='T1w')
# use ``trans`` to transform landmarks from the ``raw`` file to
# the voxel space of the image
landmarks = get_anat_landmarks(
t1_fname, # path to the MRI scan
info=raw.info, # the MEG data file info from the same subject as the MRI
trans=trans, # our transformation matrix
fs_subject='sample', # FreeSurfer subject
fs_subjects_dir=fs_subjects_dir, # FreeSurfer subjects directory
)
# We use the write_anat function
t1w_bids_path = write_anat(
image=t1_fname, # path to the MRI scan
bids_path=t1w_bids_path,
landmarks=landmarks, # the landmarks in MRI voxel space
verbose=True # this will print out the sidecar file
)
anat_dir = t1w_bids_path.directory
# %%
# Let's have another look at our BIDS directory