Tracking with Robust Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD)

Here, we demonstrate fiber tracking using a probabilistic direction getter and RUMBA-SD, a model introduced in [CanalesRodriguez2015]. This model adapts Richardson-Lucy deconvolution by assuming Rician or Noncentral Chi noise instead of Gaussian, which more accurately reflects the noise from MRI scanners (see also Reconstruction with Robust and Unbiased Model-BAsed Spherical Deconvolution). This tracking tutorial is an extension on An introduction to the Probabilistic Direction Getter.

We start by loading sample data and identifying a fiber response function.

from numpy.linalg import inv
import matplotlib.pyplot as plt

from dipy.core.gradients import gradient_table
from dipy.data import get_fnames, small_sphere
from dipy.direction import ProbabilisticDirectionGetter
from dipy.io.gradients import read_bvals_bvecs
from dipy.io.image import load_nifti, load_nifti_data
from dipy.io.stateful_tractogram import Space, StatefulTractogram
from dipy.io.streamline import save_trk
from dipy.reconst.csdeconv import auto_response_ssst
from dipy.tracking import utils
from dipy.tracking.local_tracking import LocalTracking
from dipy.tracking.streamline import Streamlines, transform_streamlines
from dipy.tracking.stopping_criterion import ThresholdStoppingCriterion
from dipy.viz import window, actor, colormap
from dipy.reconst.rumba import RumbaSDModel


# Enables/disables interactive visualization
interactive = False

hardi_fname, hardi_bval_fname, hardi_bvec_fname = get_fnames('stanford_hardi')
label_fname = get_fnames('stanford_labels')
t1_fname = get_fnames('stanford_t1')

data, affine, hardi_img = load_nifti(hardi_fname, return_img=True)
labels = load_nifti_data(label_fname)
t1_data, t1_aff = load_nifti(t1_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=2)

response, ratio = auto_response_ssst(gtab, data, roi_radii=10, fa_thr=0.7)

sphere = small_sphere

We can now initialize a RumbaSdModel model and fit it globally by setting voxelwise to False. For this example, TV regularization (use_tv) will be turned off for efficiency, although its usage can provide more coherent results in fiber tracking. The fit will take about 5 minutes to complete.

rumba = RumbaSDModel(gtab, wm_response=response[0], n_iter=200,
                     voxelwise=False, use_tv=False, sphere=sphere)
rumba_fit = rumba.fit(data, mask=white_matter)
odf = rumba_fit.odf()  # fODF
f_wm = rumba_fit.f_wm  # white matter volume fractions

To establish stopping criterion, a common technique is to use the Generalized Fractional Anisotropy (GFA). One point of caution is that RUMBA-SD by default separates the fODF from an isotropic compartment. This can bias GFA results computed on the fODF, although it will still generally be an effective criterion.

However, an alternative stopping criterion that takes advantage of this feature is to use RUMBA-SD’s white matter volume fraction map.

stopping_criterion = ThresholdStoppingCriterion(f_wm, .25)

We can visualize a slice of this mask.

sli = f_wm.shape[2] // 2
plt.figure()

plt.subplot(1, 2, 1).set_axis_off()
plt.imshow(f_wm[:, :, sli].T, cmap='gray', origin='lower')

plt.subplot(1, 2, 2).set_axis_off()
plt.imshow((f_wm[:, :, sli] > 0.25).T, cmap='gray', origin='lower')

plt.savefig('f_wm_tracking_mask.png')

White matter volume fraction slice

These discrete fODFs can be used as a PMF in the ProbabilisticDirectionGetter for sampling tracking directions. The PMF must be strictly non-negative; RUMBA-SD already adheres to this constraint so no further manipulation of the fODFs is necessary.

prob_dg = ProbabilisticDirectionGetter.from_pmf(odf, max_angle=30.,
                                                sphere=sphere)
streamline_generator = LocalTracking(prob_dg, stopping_criterion, seeds,
                                     affine, step_size=.5)
streamlines = Streamlines(streamline_generator)

color = colormap.line_colors(streamlines)
streamlines_actor = actor.streamtube(
    list(transform_streamlines(streamlines, inv(t1_aff))),
    color, linewidth=0.1)

vol_actor = actor.slicer(t1_data)
vol_actor.display(x=40)
vol_actor2 = vol_actor.copy()
vol_actor2.display(z=35)

scene = window.Scene()
scene.add(vol_actor)
scene.add(vol_actor2)
scene.add(streamlines_actor)
if interactive:
    window.show(scene)

window.record(scene, out_path='tractogram_probabilistic_rumba.png',
              size=(800, 800))

sft = StatefulTractogram(streamlines, hardi_img, Space.RASMM)
save_trk(sft, "tractogram_probabilistic_rumba.trk")

RUMBA-SD tractogram

References

[CanalesRodriguez2015]

Canales-Rodríguez, E. J., Daducci, A., Sotiropoulos, S. N., Caruyer, E., Aja-Fernández, S., Radua, J., Mendizabal, J. M. Y., Iturria-Medina, Y., Melie-García, L., Alemán-Gómez, Y., Thiran, J.-P., Sarró, S., Pomarol-Clotet, E., & Salvador, R. (2015). Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization. PLOS ONE, 10(10), e0138910. https://doi.org/10.1371/journal.pone.0138910