from dipy.data.fetcher import (fetch_qtdMRI_test_retest_2subjects,
read_qtdMRI_test_retest_2subjects)
from dipy.reconst import qtdmri, dti
import matplotlib.pyplot as plt
import numpy as np
"""
Download and read the data for this tutorial.
:math:`q\tau`-dMRI requires data with multiple gradient directions, gradient
strength and diffusion times. We will use the test-retest acquisitions of two
mice that were used in the test-retest study by [Fick2017]_. The data itself
is freely available and citeable at [Wassermann2017]_.
"""
fetch_qtdMRI_test_retest_2subjects()
data, cc_masks, gtabs = read_qtdMRI_test_retest_2subjects()
"""
data contains 4 qt-dMRI datasets of size [80, 160, 5, 515]. The first two are
the test-retest datasets of the first mouse and the second two are those of the
second mouse. cc_masks contains 4 corresponding binary masks for the corpus
callosum voxels in the middle slice that were used in the test-retest study.
Finally, gtab contains the qt-dMRI gradient tables for the DWIs in the dataset.
The data consists of 515 DWIs, divided over 35 shells, with 7 "gradient
strength shells" up to 491 mT/m, 5 equally spaced "pulse separation shells"
(big_delta) between [10.8-20] ms and a pulse duration (small_delta) of 5ms.
To visualize qt-dMRI acquisition schemes in an intuitive way, the qtdmri module
provides a visualization function to illustrate the relationship between
gradient strength (G), pulse separation (big_delta) and b-value:
from dipy.data.fetcher import (fetch_qtdMRI_test_retest_2subjects,
read_qtdMRI_test_retest_2subjects)
from dipy.reconst import qtdmri, dti
import matplotlib.pyplot as plt
import numpy as np
"""
Download and read the data for this tutorial.
:math:`q\tau`-dMRI requires data with multiple gradient directions, gradient
strength and diffusion times. We will use the test-retest acquisitions of two
mice that were used in the test-retest study by [Fick2017]_. The data itself
is freely available and citeable at [Wassermann2017]_.
"""
fetch_qtdMRI_test_retest_2subjects()
data, cc_masks, gtabs = read_qtdMRI_test_retest_2subjects()
"""
data contains 4 qt-dMRI datasets of size [80, 160, 5, 515]. The first two are
the test-retest datasets of the first mouse and the second two are those of the
second mouse. cc_masks contains 4 corresponding binary masks for the corpus
callosum voxels in the middle slice that were used in the test-retest study.
Finally, gtab contains the qt-dMRI gradient tables for the DWIs in the dataset.
The data consists of 515 DWIs, divided over 35 shells, with 7 "gradient
strength shells" up to 491 mT/m, 5 equally spaced "pulse separation shells"
(big_delta) between [10.8-20] ms and a pulse duration (small_delta) of 5ms.
To visualize qt-dMRI acquisition schemes in an intuitive way, the qtdmri module
provides a visualization function to illustrate the relationship between