def run(self,
input_files,
cluster=False,
cluster_thr=15.,
random_colors=False,
length_gt=0,
length_lt=1000,
clusters_gt=0,
clusters_lt=10**8,
native_coords=False,
stealth=False,
emergency_header='icbm_2009a',
bg_color=(0, 0, 0),
disable_order_transparency=False,
buan=False,
buan_thr=0.5,
buan_highlight=(1, 0, 0),
out_dir='',
out_stealth_png='tmp.png'):
""" Interactive medical visualization - Invert the Horizon!
Interact with any number of .trk, .tck or .dpy tractograms and anatomy
files .nii or .nii.gz. Cluster streamlines on loading.
Parameters
----------
input_files : variable string
cluster : bool, optional
Enable QuickBundlesX clustering.
cluster_thr : float, optional
Distance threshold used for clustering. Default value 15.0 for
small animal brains you may need to use something smaller such
as 2.0. The distance is in mm. For this parameter to be active
``cluster`` should be enabled.
random_colors : bool, optional
Given multiple tractograms have been included then each tractogram
will be shown with different color.
length_gt : float, optional
Clusters with average length greater than ``length_gt`` amount
in mm will be shown.
length_lt : float, optional
Clusters with average length less than ``length_lt`` amount in
mm will be shown.
clusters_gt : int, optional
Clusters with size greater than ``clusters_gt`` will be shown.
clusters_lt : int, optional
Clusters with size less than ``clusters_gt`` will be shown.
native_coords : bool, optional
Show results in native coordinates.
stealth : bool, optional
Do not use interactive mode just save figure.
emergency_header : str, optional
If no anatomy reference is provided an emergency header is
provided. Current options 'icbm_2009a' and 'icbm_2009c'.
bg_color : variable float, optional
Define the background color of the scene. Colors can be defined
with 1 or 3 values and should be between [0-1].
disable_order_transparency : bool, optional
Use depth peeling to sort transparent objects.
If True also enables anti-aliasing.
buan : bool, optional
Enables BUAN framework visualization.
buan_thr : float, optional
Uses the threshold value to highlight segments on the
bundle which have pvalues less than this threshold.
buan_highlight : variable float, optional
Define the bundle highlight area color. Colors can be defined
with 1 or 3 values and should be between [0-1].
For example, a value of (1, 0, 0) would mean the red color.
out_dir : str, optional
Output directory. (default current directory)
out_stealth_png : str, optional
Filename of saved picture.
References
----------
.. [Horizon_ISMRM19] Garyfallidis E., M-A. Cote, B.Q. Chandio,
S. Fadnavis, J. Guaje, R. Aggarwal, E. St-Onge, K.S. Juneja,
S. Koudoro, D. Reagan, DIPY Horizon: fast, modular, unified and
adaptive visualization, Proceedings of: International Society of
Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 2019.
"""
verbose = True
tractograms = []
images = []
pams = []
numpy_files = []
interactive = not stealth
world_coords = not native_coords
bundle_colors = None
mni_2009a = {}
mni_2009a['affine'] = np.array([[1., 0., 0.,
-98.], [0., 1., 0., -134.],
[0., 0., 1., -72.], [0., 0., 0., 1.]])
mni_2009a['dims'] = (197, 233, 189)
mni_2009a['vox_size'] = (1., 1., 1.)
mni_2009a['vox_space'] = 'RAS'
mni_2009c = {}
mni_2009c['affine'] = np.array([[1., 0., 0.,
-96.], [0., 1., 0., -132.],
[0., 0., 1., -78.], [0., 0., 0., 1.]])
mni_2009c['dims'] = (193, 229, 193)
mni_2009c['vox_size'] = (1., 1., 1.)
mni_2009c['vox_space'] = 'RAS'
if emergency_header == 'icbm_2009a':
hdr = mni_2009c
else:
hdr = mni_2009c
emergency_ref = create_nifti_header(hdr['affine'], hdr['dims'],
hdr['vox_size'])
io_it = self.get_io_iterator()
for input_output in io_it:
fname = input_output[0]
if verbose:
print('Loading file ...')
print(fname)
print('\n')
fl = fname.lower()
ends = fl.endswith
if ends('.trk'):
sft = load_tractogram(fname, 'same', bbox_valid_check=False)
tractograms.append(sft)
if ends('.dpy') or ends('.tck'):
sft = load_tractogram(fname, emergency_ref)
tractograms.append(sft)
if ends('.nii.gz') or ends('.nii'):
data, affine = load_nifti(fname)
images.append((data, affine))
if verbose:
print('Affine to RAS')
np.set_printoptions(3, suppress=True)
print(affine)
np.set_printoptions()
if ends(".pam5"):
pam = load_peaks(fname)
pams.append(pam)
if verbose:
print('Peak_dirs shape')
print(pam.peak_dirs.shape)
if ends(".npy"):
data = np.load(fname)
numpy_files.append(data)
if verbose:
print('numpy array length')
print(len(data))
if buan:
bundle_colors = []
for i in range(len(numpy_files)):
n = len(numpy_files[i])
pvalues = numpy_files[i]
bundle = tractograms[i].streamlines
indx = assignment_map(bundle, bundle, n)
ind = np.array(indx)
nb_lines = len(bundle)
lines_range = range(nb_lines)
points_per_line = [len(bundle[i]) for i in lines_range]
points_per_line = np.array(points_per_line, np.intp)
cols_arr = line_colors(bundle)
colors_mapper = np.repeat(lines_range, points_per_line, axis=0)
vtk_colors = numpy_to_vtk_colors(255 * cols_arr[colors_mapper])
colors = numpy_support.vtk_to_numpy(vtk_colors)
colors = (colors - np.min(colors)) / np.ptp(colors)
for i in range(n):
if pvalues[i] < buan_thr:
colors[ind == i] = buan_highlight
bundle_colors.append(colors)
if len(bg_color) == 1:
bg_color *= 3
elif len(bg_color) != 3:
raise ValueError('You need 3 values to set up backgound color. '
'e.g --bg_color 0.5 0.5 0.5')
order_transparent = not disable_order_transparency
horizon(tractograms=tractograms,
images=images,
pams=pams,
cluster=cluster,
cluster_thr=cluster_thr,
random_colors=random_colors,
bg_color=bg_color,
order_transparent=order_transparent,
length_gt=length_gt,
length_lt=length_lt,
clusters_gt=clusters_gt,
clusters_lt=clusters_lt,
world_coords=world_coords,
interactive=interactive,
buan=buan,
buan_colors=bundle_colors,
out_png=pjoin(out_dir, out_stealth_png))
def buan_bundle_profiles(model_bundle_folder, bundle_folder,
orig_bundle_folder, metric_folder, group_id, subject,
no_disks=100, out_dir=''):
"""
Applies statistical analysis on bundles and saves the results
in a directory specified by ``out_dir``.
Parameters
----------
model_bundle_folder : string
Path to the input model bundle files. This path may contain
wildcards to process multiple inputs at once.
bundle_folder : string
Path to the input bundle files in common space. This path may
contain wildcards to process multiple inputs at once.
orig_folder : string
Path to the input bundle files in native space. This path may
contain wildcards to process multiple inputs at once.
metric_folder : string
Path to the input dti metric or/and peak files. It will be used as
metric for statistical analysis of bundles.
group_id : integer
what group subject belongs to either 0 for control or 1 for patient
subject : string
subject id e.g. 10001
no_disks : integer, optional
Number of disks used for dividing bundle into disks. (Default 100)
out_dir : string, optional
Output directory (default input file directory)
References
----------
.. [Chandio19] Chandio, B.Q., S. Koudoro, D. Reagan, J. Harezlak,
E. Garyfallidis, Bundle Analytics: a computational and statistical
analyses framework for tractometric studies, Proceedings of:
International Society of Magnetic Resonance in Medicine (ISMRM),
Montreal, Canada, 2019.
"""
t = time()
dt = dict()
mb = glob(os.path.join(model_bundle_folder, "*.trk"))
print(mb)
mb.sort()
bd = glob(os.path.join(bundle_folder, "*.trk"))
bd.sort()
print(bd)
org_bd = glob(os.path.join(orig_bundle_folder, "*.trk"))
org_bd.sort()
print(org_bd)
n = len(org_bd)
n = len(mb)
for io in range(n):
mbundles = load_tractogram(mb[io], reference='same',
bbox_valid_check=False).streamlines
bundles = load_tractogram(bd[io], reference='same',
bbox_valid_check=False).streamlines
orig_bundles = load_tractogram(org_bd[io], reference='same',
bbox_valid_check=False).streamlines
if len(orig_bundles) > 5:
indx = assignment_map(bundles, mbundles, no_disks)
ind = np.array(indx)
metric_files_names_dti = glob(os.path.join(metric_folder,
"*.nii.gz"))
metric_files_names_csa = glob(os.path.join(metric_folder,
"*.pam5"))
_, affine = load_nifti(metric_files_names_dti[0])
affine_r = np.linalg.inv(affine)
transformed_orig_bundles = transform_streamlines(orig_bundles,
affine_r)
for mn in range(len(metric_files_names_dti)):
ab = os.path.split(metric_files_names_dti[mn])
metric_name = ab[1]
fm = metric_name[:-7]
bm = os.path.split(mb[io])[1][:-4]
logging.info("bm = " + bm)
dt = dict()
logging.info("metric = " + metric_files_names_dti[mn])
metric, _ = load_nifti(metric_files_names_dti[mn])
anatomical_measures(transformed_orig_bundles, metric, dt, fm,
bm, subject, group_id, ind, out_dir)
for mn in range(len(metric_files_names_csa)):
ab = os.path.split(metric_files_names_csa[mn])
metric_name = ab[1]
fm = metric_name[:-5]
bm = os.path.split(mb[io])[1][:-4]
logging.info("bm = " + bm)
logging.info("metric = " + metric_files_names_csa[mn])
dt = dict()
metric = load_peaks(metric_files_names_csa[mn])
peak_values(transformed_orig_bundles, metric, dt, fm, bm,
subject, group_id, ind, out_dir)
print("total time taken in minutes = ", (-t + time())/60)
def buan_bundle_profiles(model_bundle_folder,
bundle_folder,
orig_bundle_folder,
metric_folder,
group_id,
subject,
no_disks=100,
out_dir=''):
"""
Applies statistical analysis on bundles and saves the results
in a directory specified by ``out_dir``.
Parameters
----------
model_bundle_folder : string
Path to the input model bundle files. This path may contain
wildcards to process multiple inputs at once.
bundle_folder : string
Path to the input bundle files in common space. This path may
contain wildcards to process multiple inputs at once.
orig_folder : string
Path to the input bundle files in native space. This path may
contain wildcards to process multiple inputs at once.
metric_folder : string
Path to the input dti metric or/and peak files. It will be used as
metric for statistical analysis of bundles.
group_id : integer
what group subject belongs to either 0 for control or 1 for patient.
subject : string
subject id e.g. 10001.
no_disks : integer, optional
Number of disks used for dividing bundle into disks.
out_dir : string, optional
Output directory. (default current directory)
References
----------
.. [Chandio2020] Chandio, B.Q., Risacher, S.L., Pestilli, F., Bullock, D.,
Yeh, FC., Koudoro, S., Rokem, A., Harezlak, J., and Garyfallidis, E.
Bundle analytics, a computational framework for investigating the
shapes and profiles of brain pathways across populations.
Sci Rep 10, 17149 (2020)
"""
t = time()
dt = dict()
mb = glob(os.path.join(model_bundle_folder, "*.trk"))
print(mb)
mb.sort()
bd = glob(os.path.join(bundle_folder, "*.trk"))
bd.sort()
print(bd)
org_bd = glob(os.path.join(orig_bundle_folder, "*.trk"))
org_bd.sort()
print(org_bd)
n = len(org_bd)
n = len(mb)
for io in range(n):
mbundles = load_tractogram(mb[io],
reference='same',
bbox_valid_check=False).streamlines
bundles = load_tractogram(bd[io],
reference='same',
bbox_valid_check=False).streamlines
orig_bundles = load_tractogram(org_bd[io],
reference='same',
bbox_valid_check=False).streamlines
if len(orig_bundles) > 5:
indx = assignment_map(bundles, mbundles, no_disks)
ind = np.array(indx)
metric_files_names_dti = glob(
os.path.join(metric_folder, "*.nii.gz"))
metric_files_names_csa = glob(os.path.join(metric_folder,
"*.pam5"))
_, affine = load_nifti(metric_files_names_dti[0])
affine_r = np.linalg.inv(affine)
transformed_orig_bundles = transform_streamlines(
orig_bundles, affine_r)
for mn in range(len(metric_files_names_dti)):
ab = os.path.split(metric_files_names_dti[mn])
metric_name = ab[1]
fm = metric_name[:-7]
bm = os.path.split(mb[io])[1][:-4]
logging.info("bm = " + bm)
dt = dict()
logging.info("metric = " + metric_files_names_dti[mn])
metric, _ = load_nifti(metric_files_names_dti[mn])
anatomical_measures(transformed_orig_bundles, metric, dt, fm,
bm, subject, group_id, ind, out_dir)
for mn in range(len(metric_files_names_csa)):
ab = os.path.split(metric_files_names_csa[mn])
metric_name = ab[1]
fm = metric_name[:-5]
bm = os.path.split(mb[io])[1][:-4]
logging.info("bm = " + bm)
logging.info("metric = " + metric_files_names_csa[mn])
dt = dict()
metric = load_peaks(metric_files_names_csa[mn])
peak_values(transformed_orig_bundles, metric, dt, fm, bm,
subject, group_id, ind, out_dir)
print("total time taken in minutes = ", (-t + time()) / 60)
window.show(scene)
"""
.. figure:: af_l_before_assignment_maps.png
:align: center
AF_L before assignment maps
"""
"""
Creating 100 bundle assignment maps on AF_L using BUAN [Chandio2020]_
"""
n = 100
indx = assignment_map(model_af_l, model_af_l, n)
indx = np.array(indx)
colors = [np.random.rand(3) for si in range(n)]
disks_color = []
for i in range(len(indx)):
disks_color.append(tuple(colors[indx[i]]))
"""
let's visualize Arcuate Fasiculus Left (AF_L) bundle after assignment maps
"""
interactive = False
scene = window.Scene()
