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Parallel Transport Tractography
Parallel Transport Tractography (PTT) [Aydogan2021]
from dipy.direction import peaks_from_model
from dipy.data import default_sphere
from dipy.io.streamline import save_trk
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from dipy.data import get_sphere
from dipy.direction import PTTDirectionGetter
from dipy.reconst.shm import CsaOdfModel
from dipy.core.gradients import gradient_table
from dipy.data import get_fnames
from dipy.io.gradients import read_bvals_bvecs
from dipy.io.image import load_nifti, load_nifti_data
from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel,
auto_response_ssst)
from dipy.tracking import utils
from dipy.tracking.local_tracking import LocalTracking
from dipy.tracking.streamline import Streamlines
from dipy.tracking.stopping_criterion import ThresholdStoppingCriterion
from dipy.viz import window, actor, colormap, has_fury
# Enables/disables interactive visualization
interactive = False
hardi_fname, hardi_bval_fname, hardi_bvec_fname = get_fnames('stanford_hardi')
label_fname = get_fnames('stanford_labels')
data, affine, hardi_img = load_nifti(hardi_fname, return_img=True)
labels = load_nifti_data(label_fname)
bvals, bvecs = read_bvals_bvecs(hardi_bval_fname, hardi_bvec_fname)
gtab = gradient_table(bvals, bvecs)
seed_mask = (labels == 2)
white_matter = (labels == 1) | (labels == 2)
seeds = utils.seeds_from_mask(seed_mask, affine, density=1)
response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7)
csd_model = ConstrainedSphericalDeconvModel(gtab, response, sh_order=6)
csd_fit = csd_model.fit(data, mask=white_matter)
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We use the GFA of the CSA model to build a stopping criterion.
csa_model = CsaOdfModel(gtab, sh_order=6)
gfa = csa_model.fit(data, mask=white_matter).gfa
stopping_criterion = ThresholdStoppingCriterion(gfa, .25)
Prepare the PTT direction getter using the fiber ODF (FOD) obtain with CSD. Start the local tractography using PTT direction getter.
sphere = get_sphere(name='repulsion724')
fod = csd_fit.odf(sphere)
pmf = fod.clip(min=0)
ptt_dg = PTTDirectionGetter.from_pmf(pmf, max_angle=15, probe_length=0.5,
sphere=sphere)
# Parallel Transport Tractography
streamline_generator = LocalTracking(direction_getter=ptt_dg,
stopping_criterion=stopping_criterion,
seeds=seeds,
affine=affine,
step_size=0.2)
streamlines = Streamlines(streamline_generator)
sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
save_trk(sft, "tractogram_ptt_dg_pmf.trk")
if has_fury:
scene = window.Scene()
scene.add(actor.line(streamlines, colormap.line_colors(streamlines)))
window.record(scene, out_path='tractogram_ptt_dg_pmf.png',
size=(800, 800))
if interactive:
window.show(scene)
References
[Aydogan2021]
Aydogan DB, Shi Y. Parallel Transport Tractography. IEEE Trans Med Imaging. 2021 Feb;40(2):635-647. doi: 10.1109/TMI.2020.3034038. Epub 2021 Feb 2. PMID: 33104507; PMCID: PMC7931442.
Total running time of the script: ( 1 minutes 30.201 seconds)