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.. _home:
###########################
Diffusion Imaging In Python
###########################
DIPY_ is the paragon 3D/4D+ imaging library in Python. Contains generic methods for
spatial normalization, signal processing, machine learning, statistical analysis
and visualization of medical images. Additionally, it contains
specialized methods for computational anatomy including diffusion,
perfusion and structural imaging.
**********
Highlights
**********
**DIPY 1.3.0** is now available. New features include:
- Gibbs Ringing correction 10X faster.
- Spherical harmonics basis definitions updated.
- Added SMT2 metrics from mean signal diffusion kurtosis.
- New interface functions added to the registration module.
- New linear transform added to the registration module.
- New tutorials for DIPY command line interfaces.
- Fixed compatibility issues with different dependencies.
- Tqdm (multiplatform progress bar for data downloading) dependency added.
- Large documentation update.
- Bundle section highlight from BUAN added in Horizon.
- Closed 134 issues and merged 49 pull requests.
See :ref:`Older Highlights <old_highlights>`.
*************
Announcements
*************
- :doc:`DIPY 1.3.0 <release_notes/release1.3>` released November 3, 2020.
- :doc:`DIPY 1.2.0 <release_notes/release1.2>` released September 9, 2020.
- :doc:`DIPY 1.1.1 <release_notes/release1.1>` released January 10, 2020.
See some of our :ref:`Past Announcements <old_news>`
***************
Getting Started
***************
Here is a quick snippet showing how to calculate `color FA` also known as the
DEC map. We use a Tensor model to reconstruct the datasets which are
saved in a Nifti file along with the b-values and b-vectors which are saved as
text files. Finally, we save our result as a Nifti file ::
fdwi = 'dwi.nii.gz'
fbval = 'dwi.bval'
fbvec = 'dwi.bvec'
from dipy.io.image import load_nifti, save_nifti
from dipy.io import read_bvals_bvecs
from dipy.core.gradients import gradient_table
from dipy.reconst.dti import TensorModel
data, affine = load_nifti(fdwi)
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data)
save_nifti('colorfa.nii.gz', tenfit.color_fa, affine)
As an exercise, you can try to calculate `color FA` with your datasets. You will need
to replace the filepaths `fdwi`, `fbval` and `fbvec`. Here is what
a slice should look like.
.. image:: _static/colorfa.png
:align: center
**********
Next Steps
**********
You can learn more about how you to use DIPY_ with your datasets by reading the examples in our :ref:`documentation`.
.. We need the following toctree directive to include the documentation
.. in the document hierarchy - see http://sphinx.pocoo.org/concepts.html
.. toctree::
:hidden:
documentation
stateoftheart
*******
Support
*******
We acknowledge support from the following organizations:
- The department of Intelligent Systems Engineering of Indiana University.
- The National Institute of Biomedical Imaging and Bioengineering, NIH.
- The Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation, through the
University of Washington eScience Institute Data Science Environment.
- Google supported DIPY through the Google Summer of Code Program during
Summer 2015, 2016 and 2018.
- The International Neuroinformatics Coordination Facility.
.. include:: links_names.inc
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