We show how to apply Generalized Q-Sampling Imaging (Yeh et al. IEEE TMI 2010) 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 nibabel as nib
from dipy.data import fetch_taiwan_ntu_dsi, read_taiwan_ntu_dsi, get_sphere
from dipy.align.aniso2iso import resample
from dipy.reconst.gqi import GeneralizedQSamplingModel
from dipy.reconst.odf import peaks_from_model
Download and read the data for this tutorial.
fetch_taiwan_ntu_dsi()
img, gtab = read_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 = img.get_data()
print('data.shape (%d, %d, %d, %d)' % data.shape)
data.shape (96, 96, 60, 203)
This dataset has anisotropic voxel sizes, therefore reslicing is necessary.
affine = img.get_affine()
Read the voxel size from the image header.
voxel_size = img.get_header().get_zooms()[:3]
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('symmetric724')
Calculate the ODFs with this specific sphere
ODF = gqfit.odf(sphere)
print('ODF.shape (%d, %d, %d)' % 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=.8,
min_separation_angle=45,
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('GFA.shape (%d, %d)' % GFA.shape)
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=.8,
min_separation_angle=45,
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.
np.sum(gqpeaks.odf != ODF) == 0
True
The advantage of using peaks_from_models is that it calculates the ODF only once and saves it or deletes if it is not necessary to keep.
Example source code
You can download the full source code of this example. This same script is also included in the dipy source distribution under the doc/examples/ directory.