We are excited to announce the release of a new major version of pyAFQ!

pyAFQ is an open-source software tool for tractometry of brain white matter in diffusion MRI measurements. It implements a complete and automated data processing pipeline for tractometry, from preprocessed dMRi data to white matter tract identification, as well as quantification of tissue properties along the length of the major long-range brain white matter connections.

The new release includes major advances relative to versions in the 2.X series of the software, including:

  • PyAFQ 3.0 now uses T1w images for registration, tissue segmentation, and brain segmentation. When segmentations are not provided, it uses the synthseg software/model (https://pubmed.ncbi.nlm.nih.gov/36857946/).

  • Asymmetric ODFs and seeding in the white-matter/gray-matter interface is used by default to better handle the superficial white matter, based on previous work by Poirier and Descoteaux (https://pubmed.ncbi.nlm.nih.gov/38244878/)

  • Significant performance improvements include numba-accelerated probalistic tracking, which is used by default and options to use cuda, metal, and webgpu accelerated tracking, based on https://github.com/dipy/GPUStreamlines. Whole-brain tractography in under 1 minute!

  • New and improved tract definitions, including sub-bundles of the vertical occipital fasiculus.

  • Major documentation overhaul, with many new examples and better organization.

pyAFQ heavily relies on methods implemented in DIPY (https://dipy.org). To learn more about the software and the ecosystem of tools within which it is embedded, please visit https://tractometry.org and read our paper published last year in PLoS Computational Biology; https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1013323.

Demonstrating the range of applications of these tools, recent publications from our group that use pyAFQ include:

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