from collections.abc import Iterable
import numpy as np
import nibabel as nib
import logging
from tqdm import tqdm
import dipy.data as dpd
from dipy.align import resample
from dipy.direction import (DeterministicMaximumDirectionGetter,
ProbabilisticDirectionGetter)
from dipy.io.stateful_tractogram import StatefulTractogram, Space
from dipy.tracking.stopping_criterion import (ThresholdStoppingCriterion,
CmcStoppingCriterion,
ActStoppingCriterion)
from nibabel.streamlines.tractogram import LazyTractogram
from dipy.tracking.local_tracking import (LocalTracking,
ParticleFilteringTracking)
from AFQ._fixes import tensor_odf
from AFQ.tractography.utils import gen_seeds, get_percentile_threshold
[docs]def track(params_file, directions="prob", max_angle=30., sphere=None,
seed_mask=None, seed_threshold=0, thresholds_as_percentages=False,
n_seeds=1, random_seeds=False, rng_seed=None, stop_mask=None,
stop_threshold=0, step_size=0.5, minlen=50, maxlen=250,
odf_model="CSD", tracker="local", trx=False):
"""
Tractography
Parameters
----------
params_file : str, nibabel img.
Full path to a nifti file containing CSD spherical harmonic
coefficients, or nibabel img with model params.
directions : str
How tracking directions are determined.
One of: {"det" | "prob"}
Default: "prob"
max_angle : float, optional.
The maximum turning angle in each step. Default: 30
sphere : Sphere object, optional.
The discretization of direction getting. default:
dipy.data.default_sphere.
seed_mask : array, optional.
Float or binary mask describing the ROI within which we seed for
tracking.
Default to the entire volume (all ones).
seed_threshold : float, optional.
A value of the seed_mask above which tracking is seeded.
Default to 0.
n_seeds : int or 2D array, optional.
The seeding density: if this is an int, it is is how many seeds in each
voxel on each dimension (for example, 2 => [2, 2, 2]). If this is a 2D
array, these are the coordinates of the seeds. Unless random_seeds is
set to True, in which case this is the total number of random seeds
to generate within the mask. Default: 1
random_seeds : bool
Whether to generate a total of n_seeds random seeds in the mask.
Default: False.
rng_seed : int
random seed used to generate random seeds if random_seeds is
set to True. Default: None
stop_mask : array or str, optional.
If array: A float or binary mask that determines a stopping criterion
(e.g. FA).
If tuple: it contains a sequence that is interpreted as:
(pve_wm, pve_gm, pve_csf), each item of which is either a string
(full path) or a nibabel img to be used in particle filtering
tractography.
A tuple is required if tracker is set to "pft".
Defaults to no stopping (all ones).
stop_threshold : float or tuple, optional.
If float, this a value of the stop_mask below which tracking is
terminated (and stop_mask has to be an array).
If str, "CMC" for Continuous Map Criterion [Girard2014]_.
"ACT" for Anatomically-constrained tractography [Smith2012]_.
A string is required if the tracker is set to "pft".
Defaults to 0 (this means that if no stop_mask is passed,
we will stop only at the edge of the image).
thresholds_as_percentages : bool, optional
Interpret seed_threshold and stop_threshold as percentages of the
total non-nan voxels in the seed and stop mask to include
(between 0 and 100), instead of as a threshold on the
values themselves.
Default: False
step_size : float, optional.
The size of a step (in mm) of tractography. Default: 0.5
minlen: int, optional
The miminal length (mm) in a streamline. Default: 20
maxlen: int, optional
The miminal length (mm) in a streamline. Default: 250
odf_model : str, optional
One of {"DTI", "CSD", "DKI"}. Defaults to use "DTI"
tracker : str, optional
Which strategy to use in tracking. This can be the standard local
tracking ("local") or Particle Filtering Tracking ([Girard2014]_).
One of {"local", "pft"}. Default: "local"
trx : bool, optional
Whether to return the streamlines compatible with input to TRX file
(i.e., as a LazyTractogram class instance).
Default: False
Returns
-------
list of streamlines ()
References
----------
.. [Girard2014] Girard, G., Whittingstall, K., Deriche, R., &
Descoteaux, M. Towards quantitative connectivity analysis: reducing
tractography biases. NeuroImage, 98, 266-278, 2014.
"""
logger = logging.getLogger('AFQ')
logger.info("Loading Image...")
if isinstance(params_file, str):
params_img = nib.load(params_file)
else:
params_img = params_file
model_params = params_img.get_fdata()
odf_model = odf_model.upper()
directions = directions.lower()
# We need to calculate the size of a voxel, so we can transform
# from mm to voxel units:
minlen = int(minlen / step_size)
maxlen = int(maxlen / step_size)
seeds = gen_seeds(
seed_mask, seed_threshold,
n_seeds, thresholds_as_percentages,
random_seeds, rng_seed, params_img.affine)
if sphere is None:
sphere = dpd.default_sphere
logger.info("Getting Directions...")
if directions == "det":
dg = DeterministicMaximumDirectionGetter
elif directions == "prob":
dg = ProbabilisticDirectionGetter
else:
raise ValueError(f"Unrecognized direction '{directions}'.")
if odf_model == "DTI" or odf_model == "DKI":
evals = model_params[..., :3]
evecs = model_params[..., 3:12].reshape(params_img.shape[:3] + (3, 3))
odf = tensor_odf(evals, evecs, sphere)
dg = dg.from_pmf(odf, max_angle=max_angle, sphere=sphere)
else:
dg = dg.from_shcoeff(model_params, max_angle=max_angle, sphere=sphere)
if tracker == "local":
if stop_mask is None:
stop_mask = np.ones(params_img.shape[:3])
if len(np.unique(stop_mask)) <= 2:
stopping_criterion = ThresholdStoppingCriterion(stop_mask,
0.5)
else:
if thresholds_as_percentages:
stop_threshold = get_percentile_threshold(
stop_mask, stop_threshold)
stop_mask_copy = np.copy(stop_mask)
stop_thresh_copy = np.copy(stop_threshold)
stopping_criterion = ThresholdStoppingCriterion(stop_mask_copy,
stop_thresh_copy)
my_tracker = LocalTracking
elif tracker == "pft":
if not isinstance(stop_threshold, str):
raise RuntimeError(
"You are using PFT tracking, but did not provide a string ",
"'stop_threshold' input. ",
"Possible inputs are: 'CMC' or 'ACT'")
if not (isinstance(stop_mask, Iterable) and len(stop_mask) == 3):
raise RuntimeError(
"You are using PFT tracking, but did not provide a length "
"3 iterable for `stop_mask`. "
"Expected a (pve_wm, pve_gm, pve_csf) tuple.")
pves = []
pve_imgs = []
vox_sizes = []
for ii, pve in enumerate(stop_mask):
if isinstance(pve, str):
img = nib.load(pve)
else:
img = pve
pve_imgs.append(img)
pves.append(pve_imgs[-1].get_fdata())
average_voxel_size = np.mean(vox_sizes)
pve_wm_img, pve_gm_img, pve_csf_img = pve_imgs
pve_wm_data, pve_gm_data, pve_csf_data = pves
pve_wm_data = resample(
pve_wm_data, model_params[..., 0],
pve_wm_img.affine,
params_img.affine).get_fdata()
pve_gm_data = resample(
pve_gm_data, model_params[..., 0],
pve_gm_img.affine,
params_img.affine).get_fdata()
pve_csf_data = resample(
pve_csf_data, model_params[..., 0],
pve_csf_img.affine,
params_img.affine).get_fdata()
vox_sizes.append(np.mean(params_img.header.get_zooms()[:3]))
my_tracker = ParticleFilteringTracking
if stop_threshold == "CMC":
stopping_criterion = CmcStoppingCriterion.from_pve(
pve_wm_data,
pve_gm_data,
pve_csf_data,
step_size=step_size,
average_voxel_size=average_voxel_size)
elif stop_threshold == "ACT":
stopping_criterion = ActStoppingCriterion.from_pve(
pve_wm_data,
pve_gm_data,
pve_csf_data)
logger.info(
f"Tracking with {len(seeds)} seeds, 2 directions per seed...")
return _tracking(my_tracker, seeds, dg, stopping_criterion, params_img,
step_size=step_size, minlen=minlen,
maxlen=maxlen, random_seed=rng_seed,
trx=trx)
[docs]def _tracking(tracker, seeds, dg, stopping_criterion, params_img,
step_size=0.5, minlen=40, maxlen=200,
random_seed=None, trx=False):
"""
Helper function
"""
if len(seeds.shape) == 1:
seeds = seeds[None, ...]
tracker = tqdm(tracker(
dg,
stopping_criterion,
seeds,
params_img.affine,
step_size=step_size,
minlen=minlen,
maxlen=maxlen,
return_all=False,
random_seed=random_seed))
if trx:
return LazyTractogram(lambda: tracker,
affine_to_rasmm=params_img.affine)
else:
return StatefulTractogram(tracker, params_img, Space.RASMM)
