def main():
# data_dir = os.environ.get('OneDrive') + r'\data\dti_navigation\joonas'
#
# filenames = {'T1': 'sub-S1_ses-S8741_T1w',
# 'STL_INVESALIUS': 'half_head_inv',
# 'STL_WORLD': 'half_head_world'}
subj_id = 7
item = 'brain'
data_dir = os.environ.get(
'OneDrive') + r'\data\nexstim_coord\mri\ppM1_S{}'.format(subj_id)
filenames = {
'T1': 'ppM1_S{}'.format(subj_id),
'STL_INVESALIUS': 'ppM1_S{}_{}_shell_inv'.format(subj_id, item),
'STL_WORLD': 'ppM1_S{}_{}_shell_world'.format(subj_id, item)
}
img_path = os.path.join(data_dir, filenames['T1'] + '.nii')
stl_invesalius_path = os.path.join(data_dir,
filenames['STL_INVESALIUS'] + '.stl')
stl_world_path = os.path.join(data_dir, filenames['STL_WORLD'] + '.stl')
imagedata, affine = imf.load_image(img_path)
# mri2inv_mat = imf.mri2inv(imagedata, affine)
inv2mri_mat = imf.inv2mri(imagedata, affine)
matrix_vtk = vtk.vtkMatrix4x4()
for row in range(0, 4):
for col in range(0, 4):
matrix_vtk.SetElement(row, col, inv2mri_mat[row, col])
reader = vtk.vtkSTLReader()
reader.SetFileName(stl_invesalius_path)
reader.Update()
# poly_normals = vtk.vtkPolyDataNormals()
# poly_normals.SetInputData(reader.GetOutput())
# poly_normals.ConsistencyOn()
# poly_normals.AutoOrientNormalsOn()
# poly_normals.SplittingOff()
# poly_normals.UpdateInformation()
# poly_normals.Update()
transform = vtk.vtkTransform()
transform.SetMatrix(matrix_vtk)
transform_polydata = vtk.vtkTransformPolyDataFilter()
transform_polydata.SetTransform(transform)
transform_polydata.SetInputData(reader.GetOutput())
transform_polydata.Update()
stl_writer = vtk.vtkSTLWriter()
stl_writer.SetFileTypeToBinary()
stl_writer.SetFileName(stl_world_path)
stl_writer.SetInputConnection(transform_polydata.GetOutputPort())
stl_writer.Write()
def main():
SHOW_WINDOW = True
SHOW_AXES = True
TRANFORM_SCANNER = True
SAVE_SCANNER = True
subject_id = 0
subject = ['joonas', 'pantelis', 'baran', 'victor']
marker_file = {
'joonas': '20211123-200911-T1-markers_all_repeated',
'pantelis': '20211126-121334-T1-markers_all_repeated',
'baran': '20211123-205329-T1-markers_all_repeated',
'victor': '20211126-132408-T1-markers_all_repeated'
}
target_names = ['M1', 'V1', 'BROCA', 'DLPFC']
columns_export = ['label', 'x_seed', 'y_seed', 'z_seed', 'is_target']
root_dir = os.environ.get('OneDrive') + r'\data\dti_navigation\normMRI'
data_dir = os.path.join(root_dir, subject[subject_id])
filenames = {
'MKSS': marker_file[subject[subject_id]],
'COIL': 'magstim_fig8_coil',
'T1': 'T1',
'BRAINSIM': 'wm',
'HEADSIM': 'skin'
}
# mkss_path = os.path.join(data_dir, filenames['MKSS'] + '.mkss')
mkss_path = os.path.join(data_dir, filenames['MKSS'] + '.mkss')
head_sim_path = os.path.join(data_dir, filenames['HEADSIM'] + '.stl')
brain_sim_path = os.path.join(data_dir, filenames['BRAINSIM'] + '.stl')
coil_path = os.path.join(root_dir, filenames['COIL'] + '.stl')
img_path = os.path.join(data_dir, filenames['T1'] + '.nii')
save_path = os.path.join(data_dir, filenames['MKSS'] + '_scanner.csv')
imagedata, affine = imf.load_image(img_path)
inv2mri_mat = imf.inv2mri(imagedata, affine)
data = imf.load_mkss(mkss_path)
coord_list = data.iloc[:, 16:19].values
orient_list = data.iloc[:, 19:].values
colour_list = data.iloc[:, 6:9].values
size_list = data.iloc[:, 9].values
label_list = data.iloc[:, 10].values.tolist()
seed_list = data.iloc[:, 11:14].values
# coord_list, orient_list, colour_list, size_list, id_list, seed_list, tg_list = imf.load_mks(mkss_path)
seed_list_w = np.hstack((seed_list, np.ones([seed_list.shape[0],
1]))).transpose()
seed_list_w = inv2mri_mat @ seed_list_w
seed_list_w = seed_list_w.transpose()[:, :3]
id_fids = ['LEI', 'REI', 'NAI']
index_coord = label_list.index('BROCA')
index_fids = [label_list.index(n) for n in id_fids]
if TRANFORM_SCANNER:
data_out = data[columns_export].copy()
data_out.iloc[:, 1:4] = seed_list_w
data_out['distance'] = -1 * np.ones([seed_list_w.shape[0], 1])
data_out['distance_coil'] = -1 * np.ones([seed_list_w.shape[0], 1])
for n in target_names:
index_coord = label_list.index(n)
distances = np.linalg.norm(seed_list_w - seed_list_w[index_coord],
axis=1)
distances_coil = np.linalg.norm(coord_list -
coord_list[index_coord],
axis=1)
repeats_id = np.where((distances_coil != 0.0)
& (distances_coil <= 5))
target_id = np.where((distances_coil == 0.0))
data_out.loc[repeats_id[0], 'label'] = n
data_out.loc[target_id[0], 'is_target'] = True
data_out.loc[repeats_id[0], 'distance'] = distances[repeats_id[0]]
data_out.loc[repeats_id[0],
'distance_coil'] = distances_coil[repeats_id[0]]
if SAVE_SCANNER:
data_out.to_csv(save_path, sep=';', index=False, float_format="%.3f")
distance = np.linalg.norm(coord_list[index_coord] -
seed_list_w[index_coord])
print("Distance between seed and coil center: {}".format(distance))
if SHOW_WINDOW:
# Create a rendering window and renderer
ren, ren_win, iren = vf.create_window()
# 0: red, 1: green, 2: blue, 3: maroon (dark red),
# 4: purple, 5: teal (petrol blue), 6: yellow, 7: orange
colours = [[1., 0., 0.], [0., 1., 0.], [0., 0., 1.], [1., .0, 1.],
[0.45, 0., 0.5], [0., .5, .5], [1., 1., 0.], [1., .4, .0]]
repos = [0., 0., 0., 0., 0., 0.]
_ = vf.load_stl(head_sim_path,
ren,
opacity=.4,
colour="SkinColor",
replace=repos,
user_matrix=np.identity(4))
_ = vf.load_stl(brain_sim_path,
ren,
opacity=.6,
colour=[1., 1., 1.],
replace=repos,
user_matrix=np.identity(4))
# create fiducial markers
for n in index_fids:
_ = vf.add_marker(coord_list[n], ren, colours[n], radius=2)
# --- fiducial markers
# create coil vectors
coil_pos = np.hstack(
(coord_list[index_coord], orient_list[index_coord]))
m_coil = imf.coil_transform_matrix(coil_pos)
vec_length = 75
repos_coil = [0., 0., 0., 0., 0., 90.]
# coil vectors in invesalius 3D space
p1 = m_coil[:-1, -1]
coil_dir = m_coil[:-1, 0]
coil_face = m_coil[:-1, 1]
p2_face = p1 + vec_length * coil_face
p2_dir = p1 + vec_length * coil_dir
coil_norm = np.cross(coil_dir, coil_face)
p2_norm = p1 - vec_length * coil_norm
# offset = 40
# coil_norm = coil_norm/np.linalg.norm(coil_norm)
# coord_offset = p1 - offset * coil_norm
_ = vf.load_stl(coil_path,
ren,
opacity=.6,
replace=repos_coil,
colour=[1., 1., 1.],
user_matrix=m_coil)
_ = vf.add_line(ren, p1, p2_dir, color=[1.0, .0, .0])
_ = vf.add_line(ren, p1, p2_face, color=[.0, 1.0, .0])
_ = vf.add_line(ren, p1, p2_norm, color=[.0, .0, 1.0])
_ = vf.add_marker(p1, ren, colours[4], radius=2)
# --- coil vectors
# seed markers
_ = vf.add_marker(seed_list_w[index_coord], ren, colours[5], radius=2)
# _ = vf.add_marker(seed_list[index_coord], ren, colours[6], radius=2)
# --- seed markers
# Add axes to scene origin
if SHOW_AXES:
_ = vf.add_line(ren, [0, 0, 0], [150, 0, 0], color=[1.0, 0.0, 0.0])
_ = vf.add_line(ren, [0, 0, 0], [0, 150, 0], color=[0.0, 1.0, 0.0])
_ = vf.add_line(ren, [0, 0, 0], [0, 0, 150], color=[0.0, 0.0, 1.0])
# Initialize window and interactor
iren.Initialize()
ren_win.Render()
iren.Start()
def main():
SHOW_WINDOW = True
SHOW_AXES = True
COMP_ACT_SEED = True
SEED_OFFSET = 25
data_dir = os.environ.get('OneDrive') + r'\data\dti_navigation\joonas'
# markers_20210304_all_before_rep_scan
# markers_20210304_rep_left_m1_dlpfc_broca_V1_final_scan_seed
# markers_20210304_rep_left_m1_dlpfc_broca_V1_final_labeled_scanner
filenames = {
'MKSS':
'markers_20210304_rep_left_m1_dlpfc_broca_V1_final_labeled_scanner',
'COIL': 'magstim_fig8_coil',
'BRAINSIM': 'wm',
'HEADSIM': 'skin',
'ACT': 'trekkerACTlabels',
'T1': 'sub-S1_ses-S8741_T1w'
}
mkss_path = os.path.join(data_dir, filenames['MKSS'] + '.mkss')
head_sim_path = os.path.join(data_dir, filenames['HEADSIM'] + '.stl')
brain_sim_path = os.path.join(data_dir, filenames['BRAINSIM'] + '.stl')
coil_path = os.path.join(data_dir, filenames['COIL'] + '.stl')
act_path = os.path.join(data_dir, filenames['ACT'] + '.nii')
# img_path = os.path.join(data_dir, filenames['T1'] + '.nii')
coord_list, orient_list, colour_list, size_list, id_list, seed_list, tg_list = imf.load_mks(
mkss_path)
id_fids = ['LEI', 'REI', 'NAI']
index_coord = id_list.index('m1-left')
index_fids = [id_list.index(n) for n in id_fids]
if COMP_ACT_SEED:
imagedata, affine = imf.load_image(act_path)
# imagedata2, affine2 = imf.load_image(img_path)
img_shape = imagedata.header.get_data_shape()
act_data = imagedata.get_fdata()
mri2inv_mat = imf.mri2inv(imagedata, affine)
if SHOW_WINDOW:
# Create a rendering window and renderer
ren, ren_win, iren = vf.create_window()
# 0: red, 1: green, 2: blue, 3: maroon (dark red),
# 4: purple, 5: teal (petrol blue), 6: yellow, 7: orange
colours = [[1., 0., 0.], [0., 1., 0.], [0., 0., 1.], [1., .0, 1.],
[0.45, 0., 0.5], [0., .5, .5], [1., 1., 0.], [1., .4, .0]]
repos = [0., 0., 0., 0., 0., 0.]
_ = vf.load_stl(head_sim_path,
ren,
opacity=.4,
colour="SkinColor",
replace=repos,
user_matrix=np.identity(4))
_ = vf.load_stl(brain_sim_path,
ren,
opacity=.6,
colour=[1., 1., 1.],
replace=repos,
user_matrix=np.identity(4))
# if COMP_ACT_SEED:
# _ = vf.load_stl(brain_sim_path, ren, opacity=.6, colour=[1., 1., 1.], replace=repos,
# user_matrix=np.linalg.inv(affine))
# _ = vf.load_stl(brain_sim_path, ren, opacity=.6, colour=[1., 1., 0.], replace=repos,
# user_matrix=mri2inv_mat)
# create fiducial markers
for n in index_fids:
_ = vf.add_marker(coord_list[n], ren, colours[n], radius=2)
# --- fiducial markers
# create coil vectors
coil_pos = np.hstack(
(coord_list[index_coord], orient_list[index_coord]))
m_coil = imf.coil_transform_matrix(coil_pos)
vec_length = 75
repos_coil = [0., 0., 0., 0., 0., 90.]
# coil vectors in invesalius 3D space
p1 = m_coil[:-1, -1]
coil_dir = m_coil[:-1, 0]
coil_face = m_coil[:-1, 1]
p2_face = p1 + vec_length * coil_face
p2_dir = p1 + vec_length * coil_dir
coil_norm = np.cross(coil_dir, coil_face)
p2_norm = p1 - vec_length * coil_norm
if COMP_ACT_SEED:
coord_list_w = imf.create_grid((-2, 2), (0, 20), SEED_OFFSET - 5,
1)
coord_list_w_tr = m_coil @ coord_list_w
coord_offset = imf.grid_offset(act_data,
coord_list_w_tr,
affine=affine)
# coord_list_w_tr_inv = mri2inv_mat @ m_coil @ coord_list_w
# coord_list_inv = imf.grid_offset_inv(act_data, coord_list_w_tr_inv, img_shape[1])
# coord_list_mri = np.squeeze(coord_list_inv + np.array([[0, img_shape[1], 0]]))
# offset = 40
# coil_norm = coil_norm/np.linalg.norm(coil_norm)
# coord_offset = p1 - offset * coil_norm
# _ = vf.load_stl(coil_path, ren, opacity=.6, replace=repos_coil, colour=[1., 1., 1.], user_matrix=m_coil)
_ = vf.add_line(ren, p1, p2_dir, color=[1.0, .0, .0])
_ = vf.add_line(ren, p1, p2_face, color=[.0, 1.0, .0])
_ = vf.add_line(ren, p1, p2_norm, color=[.0, .0, 1.0])
_ = vf.add_marker(p1, ren, colours[4], radius=2)
# --- coil vectors
# seed markers
_ = vf.add_marker(seed_list[index_coord], ren, colours[5], radius=.5)
_ = vf.add_marker(coord_offset, ren, colours[6], radius=.5)
# _ = vf.add_marker(coord_list_inv, ren, colours[0], radius=.5)
# _ = vf.add_marker(coord_list_mri, ren, colours[0], radius=.5)
# for n in coord_list_w_tr.T:
# _ = vf.add_marker(n[:3], ren, colours[7], radius=.5, opacity=.2)
# --- seed markers
# Add axes to scene origin
if SHOW_AXES:
_ = vf.add_line(ren, [0, 0, 0], [150, 0, 0], color=[1.0, 0.0, 0.0])
_ = vf.add_line(ren, [0, 0, 0], [0, 150, 0], color=[0.0, 1.0, 0.0])
_ = vf.add_line(ren, [0, 0, 0], [0, 0, 150], color=[0.0, 0.0, 1.0])
# Initialize window and interactor
iren.Initialize()
ren_win.Render()
iren.Start()
def main():
FIX_MKS = False
SAVE_FIX = False
SAVE_SCAN = True
RECOMPUTE_SEED = True
SEED_OFFSET = 25
data_dir = os.environ.get('OneDrive') + r'\data\dti_navigation\joonas'
filenames = {
'T1': 'sub-S1_ses-S8741_T1w',
'MKS': 'markers_20210304_rep_left_m1_dlpfc_broca_V1_final_labeled',
'MKS_CORR':
'markers_20210304_rep_left_m1_dlpfc_broca_V1_final_labeled',
'ACT': 'trekkerACTlabels'
}
img_path = os.path.join(data_dir, filenames['T1'] + '.nii')
act_path = os.path.join(data_dir, filenames['ACT'] + '.nii')
mkss_path = os.path.join(data_dir, filenames['MKS'] + '.mkss')
mkss_save_path = os.path.join(data_dir,
filenames['MKS'] + '_scanner_fixedgrid.mkss')
if FIX_MKS:
mks_corrupt_path = os.path.join(data_dir,
filenames['MKS_CORR'] + '.mks')
content = imf.load_mks_corrupt(mks_corrupt_path)
content_fixed = imf.fix_mks_corrupt(
content, problems=['0.5019607843137255', '1.0'])
if SAVE_FIX:
mkss_fix_path = os.path.join(data_dir,
filenames['MKS_CORR'] + '_fix.mkss')
imf.save_mks(mkss_fix_path, content_fixed)
else:
imagedata, affine = imf.load_image(img_path)
# mri2inv_mat = imf.mri2inv(imagedata, affine)
inv2mri_mat = imf.inv2mri(imagedata, affine)
recomp_seed = {
'recompute': False,
'recompute': False,
'seed_offset': 25,
'act_data': None,
'affine': None
}
if RECOMPUTE_SEED:
imagedata_act, _ = imf.load_image(act_path)
act_data = imagedata_act.get_fdata()
recomp_seed = {
'recompute': RECOMPUTE_SEED,
'seed_offset': SEED_OFFSET,
'act_data': act_data,
'affine': affine
}
coord_list, orient_list, colour_list, size_list, id_list, seed_list, tg_list = imf.load_mks(
mkss_path)
coil_pos_scan, seed_scan = imf.mks2mkss(coord_list,
orient_list,
seed_list,
user_matrix=inv2mri_mat,
recompute_seed=recomp_seed)
if SAVE_SCAN:
# content_scan = coil_pos_scan.tolist(), colour_list, size_list, id_list, seed_scan.tolist(), tg_list
content_scan = coil_pos_scan, colour_list, size_list, id_list, seed_scan, tg_list
imf.save_mkss_scan(filename=mkss_save_path, content=content_scan)
def main():
SHOW_WINDOW = True
SHOW_AXES = True
# data_dir = os.environ.get('OneDrive') + r'\data\dti_navigation\joonas'
# filenames = {'T1': 'sub-S1_ses-S8741_T1w', 'MKSS': 'markers_20210304_m1',
# 'ACT': 'trekkerACTlabels', 'COIL': 'magstim_fig8_coil',
# 'HEAD': 'half_head_world', 'BRAIN': 'brain_inv', 'BRAINSIM': 'wm',
# 'HEADSIM': 'skin', 'MKS': 'ana_markers3'}
subj_id = 7
data_dir = os.environ.get(
'OneDrive') + r'\data\nexstim_coord\mri\ppM1_S{}'.format(subj_id)
filenames = {
'T1': 'ppM1_S{}'.format(subj_id),
'HEAD': 'ppM1_S{}_scalp_shell_world'.format(subj_id),
'HEADSIM': 'ppM1_S{}_brain_shell_world'.format(subj_id),
'BRAIN': 'brain_inv',
'BRAINSIM': 'wm'
}
img_path = os.path.join(data_dir, filenames['T1'] + '.nii')
head_inv_path = os.path.join(data_dir, filenames['HEAD'] + '.stl')
# brain_inv_path = os.path.join(data_dir, filenames['BRAIN'] + '.stl')
head_sim_path = os.path.join(data_dir, filenames['HEADSIM'] + '.stl')
# brain_sim_path = os.path.join(data_dir, filenames['BRAINSIM'] + '.stl')
imagedata, affine = imf.load_image(img_path)
mri2inv_mat = imf.mri2inv(imagedata, affine)
inv2mri_mat = imf.inv2mri(imagedata, affine)
if SHOW_WINDOW:
# Create a rendering window and renderer
ren, ren_win, iren = vf.create_window()
# 0: red, 1: green, 2: blue, 3: maroon (dark red),
# 4: purple, 5: teal (petrol blue), 6: yellow, 7: orange
colours = [[1., 0., 0.], [0., 1., 0.], [0., 0., 1.], [1., .0, 1.],
[0.45, 0., 0.5], [0., .5, .5], [1., 1., 0.], [1., .4, .0]]
repos = [0., 0., 0., 0., 0., 0.]
_ = vf.load_stl(head_inv_path,
ren,
opacity=.4,
colour=[0.482, 0.627, 0.698],
replace=repos,
user_matrix=np.identity(4))
_ = vf.load_stl(head_sim_path,
ren,
opacity=0.4,
colour="SkinColor",
replace=repos,
user_matrix=np.identity(4))
# _ = vf.load_stl(brain_sim_path, ren, opacity=.6, colour=[1., 1., 1.], replace=repos, user_matrix=mri2inv_mat)
# _ = vf.load_stl(brain_sim_path, ren, opacity=.6, colour=[1., 1., 1.], replace=repos,
# user_matrix=np.identity(4))
pix_dim = 0.48828125
img_shape = 512
offset = pix_dim * (img_shape - 1)
# _ = vf.add_marker([31.25201835082943, 69, -25.883510303223446], ren, colours[0], radius=2)
_ = vf.add_marker(
[-2.8687858387879146, 102.87724194445669, -62.21162745582136],
ren,
colours[1],
radius=1)
# Add axes to scene origin
if SHOW_AXES:
_ = vf.add_line(ren, [0, 0, 0], [150, 0, 0], color=[1.0, 0.0, 0.0])
_ = vf.add_line(ren, [0, 0, 0], [0, 150, 0], color=[0.0, 1.0, 0.0])
_ = vf.add_line(ren, [0, 0, 0], [0, 0, 150], color=[0.0, 0.0, 1.0])
# Initialize window and interactor
iren.Initialize()
ren_win.Render()
iren.Start()
def main():
SAVE_SCAN = True
intensity = [110, 120]
muscles = ['FCP', 'FRC', 'ADM']
# subject = [4, 5, 7, 9]
subject = [11]
data_dir = os.environ.get(
'OneDrive') + r'\data\dti_navigation\motor_mapping'
for subj_ in subject:
for int_ in intensity:
for musc_ in muscles:
# filenames = {'T1': 'subject04_victor', 'XLS': '04_110_MRI_ADM_processed',
# 'XLS_CORR': '04_110_MRI_ADM_processed_scanner'}
filenames = {
'T1': 'T1_sub-{:02d}'.format(subj_),
'XLS':
'{:02d}_{}_MRI_{}_processed'.format(subj_, int_, musc_)
}
img_path = os.path.join(data_dir, filenames['T1'] + '.nii')
xls_path = os.path.join(data_dir, filenames['XLS'] + '.xlsx')
csv_save_path = os.path.join(data_dir,
filenames['XLS'] + '_scanner.csv')
imagedata, affine = imf.load_image(img_path)
# mri2inv_mat = imf.mri2inv(imagedata, affine)
inv2mri_mat = imf.inv2mri(imagedata, affine)
header = [
'x', 'y', 'z', 'ch1_amp', 'ch1_lat', 'ch2_amp', 'ch2_lat',
'ch3_amp', 'ch3_lat'
]
data = pd.read_excel(xls_path,
header=None,
names=header,
usecols='D:F,I:N')
data.drop(labels=range(6), axis=0, inplace=True)
data_arr = data.to_numpy()
data_arr = data_arr.astype(float)
coord_list = data_arr[:, :3].copy()
orient_list = np.zeros([data_arr.shape[0], 3])
coil_pos_scan = imf.array2scanner(coord_list,
orient_list,
user_matrix=inv2mri_mat)
data_scanner = data_arr.copy()
data_scanner[:, :3] = coil_pos_scan[:, :3]
if SAVE_SCAN:
with open(csv_save_path, 'w', newline='') as f:
writer = csv.writer(f, delimiter=';')
# write the header
writer.writerow(header)
# write multiple rows
writer.writerows(data_scanner)
def main():
SHOW_WINDOW = True
SHOW_AXES = True
SHOW_EEG = True
SAVE_EEG = False
reorder = [0, 2, 1]
flipx = [True, False, False]
data_dir = r'P:\tms_eeg\mTMS\projects\2019 EEG-based target automatization\Analysis\EEG electrode transformation\Locations of interest in Nexstim coords'
filenames = {
'T1': 'EEGTA04',
'EEG': 'EEGTA04_electrode_locations_Nexstim',
'TARGET': 'EEGTA04_preSMA_target',
'SAVE_EEG': 'EEGTA04_electrode_locations_MRI'
}
img_path = os.path.join(data_dir, filenames['T1'] + '.nii')
eeg_path = os.path.join(data_dir, filenames['EEG'] + '.mat')
target_path = os.path.join(data_dir, filenames['TARGET'] + '.mat')
save_eeg_path = os.path.join(data_dir, filenames['SAVE_EEG'] + '.csv')
# brain_inv_path = os.path.join(data_dir, filenames['BRAIN'] + '.stl')
# head_sim_path = os.path.join(data_dir, filenames['HEADSIM'] + '.stl')
# brain_sim_path = os.path.join(data_dir, filenames['BRAINSIM'] + '.stl')
# coil_path = os.path.join(data_dir, filenames['COIL'] + '.stl')
coord_list = imf.load_eeg_mat(eeg_path)
n_points = coord_list.shape[0]
imagedata, affine = imf.load_image(img_path, closest=True)
img_shape = imagedata.header.get_data_shape()
# mri2inv_mat = imf.mri2inv(imagedata, affine)
# inv2mri_mat = imf.inv2mri(imagedata, affine)
coords_np = np.zeros([n_points, 3])
for n, coord in enumerate(coord_list):
# the flipx and reorder are needed to transform from nexstim to mri space
coords_np[n, :] = n2m.coord_change(coord, img_shape, affine, flipx,
reorder)
# export the coordinates changed only by the affine
# coords_np[n, :] = n2m.coord_change(coord, img_shape, affine)
if SAVE_EEG:
np.savetxt(save_eeg_path, coords_np, delimiter=";")
if SHOW_WINDOW:
# Create a rendering window and renderer
ren, ren_win, iren = vf.create_window()
if SHOW_EEG:
for coord in coords_np:
_ = vf.add_marker(coord, ren, [1., 0., 0.], radius=2)
# Add axes to scene origin
if SHOW_AXES:
_ = vf.add_line(ren, [0, 0, 0], [150, 0, 0], color=[1.0, 0.0, 0.0])
_ = vf.add_line(ren, [0, 0, 0], [0, 150, 0], color=[0.0, 1.0, 0.0])
_ = vf.add_line(ren, [0, 0, 0], [0, 0, 150], color=[0.0, 0.0, 1.0])
# Initialize window and interactor
iren.Initialize()
ren_win.Render()
iren.Start()
def main():
SHOW_WINDOW = True
SHOW_AXES = True
data_dir = os.environ.get('OneDrive') + r'\data\dti_navigation\joonas'
filenames = {
'T1': 'sub-S1_ses-S8741_T1w',
'MKSS': 'markers_20210304_m1',
'ACT': 'trekkerACTlabels',
'COIL': 'magstim_fig8_coil',
'HEAD': 'head_inv',
'BRAIN': 'brain_inv',
'BRAINSIM': 'wm',
'HEADSIM': 'skin',
'MKS': 'ana_markers3'
}
img_path = os.path.join(data_dir, filenames['T1'] + '.nii')
mkss_path = os.path.join(data_dir, filenames['MKS'] + '.mks')
# mkss_path = os.path.join(data_dir, filenames['MKSS'] + '.mkss')
# head_inv_path = os.path.join(data_dir, filenames['HEAD'] + '.stl')
# brain_inv_path = os.path.join(data_dir, filenames['BRAIN'] + '.stl')
head_sim_path = os.path.join(data_dir, filenames['HEADSIM'] + '.stl')
brain_sim_path = os.path.join(data_dir, filenames['BRAINSIM'] + '.stl')
coil_path = os.path.join(data_dir, filenames['COIL'] + '.stl')
coord_list, orient_list, colour_list, size_list, id_list, seed_list, tg_list = imf.load_mks(
mkss_path)
n_points = coord_list.shape[0]
coord_w = np.hstack((coord_list, np.ones((n_points, 1))))
seed_w = np.hstack((seed_list, np.ones((n_points, 1))))
imagedata, affine = imf.load_image(img_path)
mri2inv_mat = imf.mri2inv(imagedata, affine)
inv2mri_mat = imf.inv2mri(imagedata, affine)
if SHOW_WINDOW:
# Create a rendering window and renderer
ren, ren_win, iren = vf.create_window()
# 0: red, 1: green, 2: blue, 3: maroon (dark red),
# 4: purple, 5: teal (petrol blue), 6: yellow, 7: orange
colours = [[1., 0., 0.], [0., 1., 0.], [0., 0., 1.], [1., .0, 1.],
[0.45, 0., 0.5], [0., .5, .5], [1., 1., 0.], [1., .4, .0]]
repos = [0., 0., 0., 0., 0., 0.]
_ = vf.load_stl(head_sim_path,
ren,
opacity=.4,
colour=[0.482, 0.627, 0.698],
replace=repos,
user_matrix=mri2inv_mat)
_ = vf.load_stl(head_sim_path,
ren,
opacity=.4,
colour="SkinColor",
replace=repos,
user_matrix=np.identity(4))
_ = vf.load_stl(brain_sim_path,
ren,
opacity=.6,
colour=[1., 1., 1.],
replace=repos,
user_matrix=mri2inv_mat)
_ = vf.load_stl(brain_sim_path,
ren,
opacity=.6,
colour=[1., 1., 1.],
replace=repos,
user_matrix=np.identity(4))
# create fiducial markers
fids_inv_vtk = coord_w[:3, :].copy()
# from the invesalius exported fiducial markers you have to multiply the Y coordinate by -1 to
# transform to the regular 3D invesalius space where coil location is saved
fids_inv_vtk[:, 1] *= -1
fids_vis = fids_inv_vtk[:, :3]
fids_scan = inv2mri_mat @ fids_inv_vtk.T
fids_scan_vis = fids_scan.T[:3, :3]
for n in range(3):
_ = vf.add_marker(fids_scan_vis[n, :], ren, colours[n], radius=2)
for n in range(3):
_ = vf.add_marker(fids_vis[n, :], ren, colours[n], radius=2)
# --- fiducial markers
# create coil vectors
coil_pos = np.hstack((coord_w[-1, :3], orient_list[-1, :]))
coil_pos[1] *= -1
m_coil = imf.coil_transform_matrix(coil_pos)
vec_length = 75
repos_coil = [0., 0., 0., 0., 0., 90.]
# coil vectors in invesalius 3D space
p1 = m_coil[:-1, -1]
coil_dir = m_coil[:-1, 0]
coil_face = m_coil[:-1, 1]
p2_face = p1 + vec_length * coil_face
p2_dir = p1 + vec_length * coil_dir
coil_norm = np.cross(coil_dir, coil_face)
p2_norm = p1 - vec_length * coil_norm
# offset = 40
# coil_norm = coil_norm/np.linalg.norm(coil_norm)
# coord_offset_nav = p1 - offset * coil_norm
_ = vf.load_stl(coil_path,
ren,
opacity=.6,
replace=repos_coil,
colour=[1., 1., 1.],
user_matrix=m_coil)
_ = vf.add_line(ren, p1, p2_dir, color=[1.0, .0, .0])
_ = vf.add_line(ren, p1, p2_face, color=[.0, 1.0, .0])
_ = vf.add_line(ren, p1, p2_norm, color=[.0, .0, 1.0])
_ = vf.add_marker(p1, ren, colours[4], radius=2)
# coil vectors in MRI space
m_coil_scan = inv2mri_mat @ m_coil
p1_scan = m_coil_scan[:-1, -1]
coil_dir_scan = m_coil_scan[:-1, 0]
coil_face_scan = m_coil_scan[:-1, 1]
p2_face_scan = p1_scan + vec_length * coil_face_scan
p2_dir_scan = p1_scan + vec_length * coil_dir_scan
coil_norm_scan = np.cross(coil_dir_scan, coil_face_scan)
p2_norm_scan = p1_scan - vec_length * coil_norm_scan
_ = vf.load_stl(coil_path,
ren,
opacity=.6,
replace=repos_coil,
colour=[1., 1., 1.],
user_matrix=m_coil_scan)
_ = vf.add_line(ren, p1_scan, p2_dir_scan, color=[1.0, .0, .0])
_ = vf.add_line(ren, p1_scan, p2_face_scan, color=[.0, 1.0, .0])
_ = vf.add_line(ren, p1_scan, p2_norm_scan, color=[.0, .0, 1.0])
_ = vf.add_marker(p1_scan, ren, colours[4], radius=2)
# --- coil vectors
# seed markers
seed_3dinv = seed_w[np.newaxis, -1, :].copy()
seed_vis = seed_3dinv[0, :3]
seed_scan = inv2mri_mat @ seed_3dinv.T
seed_scan_vis = seed_scan.T[0, :3]
_ = vf.add_marker(seed_vis, ren, colours[5], radius=2)
_ = vf.add_marker(seed_scan_vis, ren, colours[6], radius=2)
# --- seed markers
# Add axes to scene origin
if SHOW_AXES:
_ = vf.add_line(ren, [0, 0, 0], [150, 0, 0], color=[1.0, 0.0, 0.0])
_ = vf.add_line(ren, [0, 0, 0], [0, 150, 0], color=[0.0, 1.0, 0.0])
_ = vf.add_line(ren, [0, 0, 0], [0, 0, 150], color=[0.0, 0.0, 1.0])
# Initialize window and interactor
iren.Initialize()
ren_win.Render()
iren.Start()