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Diffusion Imaging In Python#

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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.

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Highlights#

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DIPY 1.7.0 is now available. New features include:

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  • NF: BundleWarp - Streamline-based nonlinear registration method for bundles added.

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  • NF: DKI+ - Diffusion Kurtosis modeling with advanced constraints added.

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  • NF: Synb0 - Synthetic b0 creation added using deep learning added.

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  • NF: New Parallel Transport Tractography (PTT) added.

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  • NF: Fast Streamline Search algorithm added.

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  • NF: New denoising methods based on 1D CNN added.

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  • Handle Asymmetric Spherical Functions.

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  • Large update of DIPY Horizon features.

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  • Multiple Workflows updated

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  • Large codebase cleaning.

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  • Large documentation update. Integration of Sphinx-Gallery.

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  • Closed 53 issues and merged 34 pull requests.

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See Older Highlights.

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Announcements#

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  • DIPY 1.7.0 released April 23, 2023.

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  • DIPY 1.6.0 released January 16, 2023.

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  • DIPY 1.5.0 released March 11, 2022.

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See some of our Past Announcements

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Getting Started#

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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

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fdwi = 'dwi.nii.gz'
-fbval = 'dwi.bval'
-fbvec = 'dwi.bvec'
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-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
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-data, affine = load_nifti(fdwi)
-bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
-gtab = gradient_table(bvals, bvecs)
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-tenmodel = TensorModel(gtab)
-tenfit = tenmodel.fit(data)
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-save_nifti('colorfa.nii.gz', tenfit.color_fa, affine)
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+ Diffusion Imaging in Python +

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+ An open-source, user-friendly and growing imaging library for 3D/4D+ imaging. +

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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.

-_images/colorfa.png -
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Next Steps#

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You can learn more about how you to use DIPY with your datasets by reading the examples in our Documentation.

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Support#

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We acknowledge support from the following organizations:

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  • The department of Intelligent Systems Engineering of Indiana University.

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  • The National Institute of Biomedical Imaging and Bioengineering, NIH.

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  • The Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation, through the -University of Washington eScience Institute Data Science Environment.

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  • Google supported DIPY through the Google Summer of Code Program during -Summer 2015, 2016 and 2018.

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  • The International Neuroinformatics Coordination Facility.

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Explore DIPY

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+ Command-Line Interfaces +
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Command-Line Interfaces

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+ All the algorithms are available using CLI. You can also create your own algorithms. +

+ + + Know more + + +
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