Published August 14, 2025 | Version 25.0.1
Software Open

NiTransforms: A Python tool to read, represent, manipulate, and apply N-dimensional spatial transforms

Authors/Creators

  • 1. Department of Psychology, Stanford University, Stanford, CA, USA
  • 2. Basque Center on Cognition Brain and Language, San Sebastian, Spain
  • 3. Charite Universitatsmedizin Berlin, Germany
  • 4. University of Montréal, Montréal, Canada
  • 5. University of California Berkeley, Berkeley, CA, USA
  • 6. Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  • 7. Dartmouth College, Hanover, NH, United States
  • 8. Machine Learning Team, National Institute of Mental Health, USA
  • 9. Child Mind Institute, New York, NY, USA
  • 10. Stanford University, Stanford, CA, USA
  • 11. McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA; and Department of Otolaryngology, Harvard Medical School, Boston, MA, USA

Description

Spatial transforms formalize mappings between coordinates of objects in biomedical images. Transforms typically are the outcome of image registration methodologies, which estimate the alignment between two images. Image registration is a prominent task present in nearly all standard image processing and analysis pipelines. The proliferation of software implementations of image registration methodologies has resulted in a spread of data structures and file formats used to preserve and communicate transforms. This segregation of formats precludes the compatibility between tools and endangers the reproducibility of results. We propose a software tool capable of converting between formats and resampling images to apply transforms generated by the most popular neuroimaging packages and libraries (AFNI, FSL, FreeSurfer, ITK, and SPM). The proposed software is subject to continuous integration tests to check the compatibility with each supported tool after every change to the code base. Compatibility between software tools and imaging formats is a necessary bridge to ensure the reproducibility of results and enable the optimization and evaluation of current image processing and analysis workflows.

Files

nipy/nitransforms-25.0.1.zip

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Additional details

Related works

Is supplement to
Software: https://github.com/nipy/nitransforms/tree/25.0.1 (URL)

Software

Repository URL
https://github.com/nipy/nitransforms