Note
Go to the end to download the full example code
Crossing invariant fiber response function with FORECAST model#
We show how to obtain a voxel specific response function in the form of axially symmetric tensor and the fODF using the FORECAST model from [Anderson2005] , [Kaden2016] and [Zucchelli2017].
First import the necessary modules:
import numpy as np
import matplotlib.pyplot as plt
from dipy.reconst.forecast import ForecastModel
from dipy.viz import actor, window
from dipy.data import fetch_hbn, get_sphere
import nibabel as nib
import os.path as op
from dipy.core.gradients import gradient_table
Download and read the data for this tutorial. Our implementation of FORECAST requires multi-shell data.fetch_hbn() provides data that was acquired using b-values of 1000 and 2000 as part of the Healthy Brain Network study [Alexander2017] and was preprocessed and quality controlled in the HBN-POD2 dataset [RichieHalford2022].
data_path = fetch_hbn(["NDARAA948VFH"])[1]
dwi_path = op.join(
data_path, "derivatives", "qsiprep", "sub-NDARAA948VFH",
"ses-HBNsiteRU", "dwi")
img = nib.load(op.join(
dwi_path,
"sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi.nii.gz"))
gtab = gradient_table(
op.join(dwi_path,
"sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi.bval"),
op.join(dwi_path,
"sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-preproc_dwi.bvec"))
data = np.asarray(img.dataobj)
mask_img = nib.load(
op.join(dwi_path,
"sub-NDARAA948VFH_ses-HBNsiteRU_acq-64dir_space-T1w_desc-brain_mask.nii.gz"))
brain_mask = mask_img.get_fdata()
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Let us consider only a single slice for the FORECAST fitting
data_small = data[:, :, 50:51]
mask_small = brain_mask[:, :, 50:51]
Instantiate the FORECAST Model.
“sh_order” is the spherical harmonics order used for the fODF.
dec_alg is the spherical deconvolution algorithm used for the FORECAST basis fitting, in this case we used the Constrained Spherical Deconvolution (CSD) algorithm.
fm = ForecastModel(gtab, sh_order=6, dec_alg='CSD')
Fit the FORECAST to the data
f_fit = fm.fit(data_small, mask_small)
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Calculate the crossing invariant tensor indices [Kaden2016] : the parallel diffusivity, the perpendicular diffusivity, the fractional anisotropy and the mean diffusivity.
d_par = f_fit.dpar
d_perp = f_fit.dperp
fa = f_fit.fractional_anisotropy()
md = f_fit.mean_diffusivity()
Show the indices and save them in FORECAST_indices.png.
fig = plt.figure(figsize=(6, 6))
ax1 = fig.add_subplot(2, 2, 1, title='parallel diffusivity')
ax1.set_axis_off()
ind = ax1.imshow(d_par[:, :, 0].T, interpolation='nearest',
origin='lower', cmap=plt.cm.gray)
plt.colorbar(ind, shrink=0.6)
ax2 = fig.add_subplot(2, 2, 2, title='perpendicular diffusivity')
ax2.set_axis_off()
ind = ax2.imshow(d_perp[:, :, 0].T, interpolation='nearest',
origin='lower', cmap=plt.cm.gray)
plt.colorbar(ind, shrink=0.6)
ax3 = fig.add_subplot(2, 2, 3, title='fractional anisotropy')
ax3.set_axis_off()
ind = ax3.imshow(fa[:, :, 0].T, interpolation='nearest',
origin='lower', cmap=plt.cm.gray)
plt.colorbar(ind, shrink=0.6)
ax4 = fig.add_subplot(2, 2, 4, title='mean diffusivity')
ax4.set_axis_off()
ind = ax4.imshow(md[:, :, 0].T, interpolation='nearest',
origin='lower', cmap=plt.cm.gray)
plt.colorbar(ind, shrink=0.6)
plt.savefig('FORECAST_indices.png', dpi=300, bbox_inches='tight')
FORECAST scalar indices.
Load an ODF reconstruction sphere
sphere = get_sphere('repulsion724')
Compute the fODFs.
odf = f_fit.odf(sphere)
print('fODF.shape (%d, %d, %d, %d)' % odf.shape)
fODF.shape (108, 129, 1, 724)
Display a part of the fODFs
odf_actor = actor.odf_slicer(odf[30:60, 30:60, :], sphere=sphere,
colormap='plasma', scale=0.6)
scene = window.Scene()
scene.add(odf_actor)
window.record(scene, out_path='fODFs.png', size=(600, 600), magnification=4)
Fiber Orientation Distribution Functions, in a small ROI of the brain.
References#
[Anderson2005]
Anderson A. W., “Measurement of Fiber Orientation Distributions Using High Angular Resolution Diffusion Imaging”, Magnetic Resonance in Medicine, 2005.
[Kaden2016]
(1,2)
Kaden E. et al., “Quantitative Mapping of the Per-Axon Diffusion Coefficients in Brain White Matter”, Magnetic Resonance in Medicine, 2016.
[Zucchelli2017]
Zucchelli E. et al., “A generalized SMT-based framework for Diffusion MRI microstructural model estimation”, MICCAI Workshop on Computational DIFFUSION MRI (CDMRI), 2017.
[Alexander2017]
Alexander LM, Escalera J, Ai L, et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data. 2017;4:170181.
[RichieHalford2022]
Richie-Halford A, Cieslak M, Ai L, et al. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Scientific Data. 2022;9(1):1-27.
Total running time of the script: (1 minutes 16.956 seconds)