Reconstruct with Generalized Q-Sampling Imaging

We show how to apply Generalized Q-Sampling Imaging [1] to diffusion MRI datasets. You can think of GQI as an analytical version of DSI orientation distribution function (ODF) (Garyfallidis, PhD thesis, 2012).

First import the necessary modules:

import numpy as np

from dipy.core.gradients import gradient_table
from dipy.data import get_fnames, get_sphere
from dipy.direction import peaks_from_model
from dipy.io.gradients import read_bvals_bvecs
from dipy.io.image import load_nifti
from dipy.reconst.gqi import GeneralizedQSamplingModel

Download and get the data filenames for this tutorial.

fraw, fbval, fbvec = get_fnames(name="taiwan_ntu_dsi")

img contains a nibabel Nifti1Image object (data) and gtab contains a GradientTable object (gradient information e.g. b-values). For example to read the b-values it is possible to write:

print(gtab.bvals)

Load the raw diffusion data and the affine.

data, affine, voxel_size = load_nifti(fraw, return_voxsize=True)
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
bvecs[1:] = bvecs[1:] / np.sqrt(np.sum(bvecs[1:] * bvecs[1:], axis=1))[:, None]
gtab = gradient_table(bvals, bvecs=bvecs)
print(f"data.shape {data.shape}")

data.shape (96, 96, 60, 203)

This dataset has anisotropic voxel sizes, therefore reslicing is necessary.

Instantiate the model and apply it to the data.

gqmodel = GeneralizedQSamplingModel(gtab, sampling_length=3)

The parameter sampling_length is used here to

Lets just use one slice only from the data.

dataslice = data[:, :, data.shape[2] // 2]

mask = dataslice[..., 0] > 50

gqfit = gqmodel.fit(dataslice, mask=mask)

Load an ODF reconstruction sphere

sphere = get_sphere(name="repulsion724")

Calculate the ODFs with this specific sphere

ODF = gqfit.odf(sphere)

print(f"ODF.shape {ODF.shape}")

ODF.shape (96, 96, 724)

Using peaks_from_model we can find the main peaks of the ODFs and other properties.

gqpeaks = peaks_from_model(
    model=gqmodel,
    data=dataslice,
    sphere=sphere,
    relative_peak_threshold=0.5,
    min_separation_angle=25,
    mask=mask,
    return_odf=False,
    normalize_peaks=True,
)

gqpeak_values = gqpeaks.peak_values

gqpeak_indices show which sphere points have the maximum values.

gqpeak_indices = gqpeaks.peak_indices

It is also possible to calculate GFA.

GFA = gqpeaks.gfa

print(f"GFA.shape {GFA.shape}")

GFA.shape (96, 96)

With parameter return_odf=True we can obtain the ODF using gqpeaks.ODF

gqpeaks = peaks_from_model(
    model=gqmodel,
    data=dataslice,
    sphere=sphere,
    relative_peak_threshold=0.5,
    min_separation_angle=25,
    mask=mask,
    return_odf=True,
    normalize_peaks=True,
)

This ODF will be of course identical to the ODF calculated above as long as the same data and mask are used.

print(np.sum(gqpeaks.odf != ODF) == 0)

True

The advantage of using peaks_from_model is that it calculates the ODF only once and saves it or deletes if it is not necessary to keep.

References

[1]

Fang-Cheng Yeh, Van Jay Wedeen, and Wen-Yih Isaac Tseng. Generalized $q$-Sampling Imaging. IEEE Transactions on Medical Imaging, 29(9):1626–1635, 2010. URL: https://doi.org/10.1109/TMI.2010.2045126, doi:10.1109/TMI.2010.2045126.