fiberorient

fiberorient is a package for performing structure tensor analysis and calculating orientation distribution functions from 3D imaging data.

Code and methodology stem from work published as:

Trinkle, S., Foxley, S., Kasthuri, N., La Rivière, P., “Synchrotron x-ray micro-CT as a validation dataset for diffusion mri in whole mouse brain,” Magnetic Resonance in Medicine, vol. 86, no. 2, pp. 1067–1076, 2021.

For more information, see my blog post.

Installation

fiberorient can be installed with pip:

pip install fiberorient

For local installation, first clone the repo:

git clone https://github.com/scott-trinkle/fiberorient.git

cd into the directory:

cd fiberorient

and run (preferably in a virtual environment):

pip install -e .

Usage

The primary utility of fiberorient is estimating the orientation of local structures in 3D imaging data using structure tensor analysis:

from fiberorient import StructureTensor

img = np.load('img_data')  # some 3D image data
st = StructureTensor(d_sigma=1, n_sigma=3)
st.fit(img)
vectors = st.get_vectors(img)

fiberorient is also used to express groups of vectors as orientation distribution functions (ODFs) on a basis of real, even spherical harmonic functions:

from fiberorient.odf import ODF
from fiberorient.util import make_sphere

odf = ODF(degree=8)
odf.fit(vectors)

sphere = make_sphere(3000)
odf_on_sphere = odf.to_sphere(sphere)

The package also includes a number of utilities in the vis module for generating useful data visualizations, as well as metrics for comparing arrays of vectors and ODFs in the metrics module.

Further examples are available as Jupyter notebooks in examples/