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ORMIR_XCT

By: Michael T. Kuczynski, Nathan J. Neeteson, Kathryn S. Stok, Andrew J. Burghardt, Michelle A. Espinosa Hernandez, Jared Vicory, Justin J. Tse, Pholpat Durongbhan, Serena Bonaretti, Andy Kin On Wong, Steven K. Boyd, Sarah L. Manske, 2023
Version: 1.0

  • ORMIR_XCT is a Python package for processing high resolution peripheral computed tomography (HR-pQCT) scans.
  • Development of this project began during the “Building the Jupyter Community in Musculoskeletal Imaging Research” workshop hosted by the Open and Reproducible Musculoskeletal Imaging Research (ORMIR) group.

Installation

Step 1: Install the ORMIR_XCT Anaconda Environment:

conda env create -f environment.yml

If using an Apple M1, M2, or M3 processor, run the following command instead: CONDA_SUBDIR=osx-64 conda env create -f environment.yml

Step 2: Activate the Anaconda Environment:

conda activate ormir_xct

Step 3: Install the Package:

pip install -e .

Step 4: Run Scripts:

The modules in the ormir_xct directory can now be run. Examples for each module are provided in the examples directory.


Example Jupyter Notebooks

  • Example Jupyter Notebooks demonstrating the major functionality of the ORMIR_XCT package are provided in the examples directory.

Ways to Contribute

Reporting Bugs

  • Bugs can be reported by creating a new GitHub issue in this repository. For each bug, please provide details on how to reproduce the bug and the specific error message (if possible).

Contributing New Features

  • To add a new feature, expand existing functionality, add documentation, or other contributions, please submit a new GitHub issue outlining your contribution in detail.
  • When submitting a new pull request, ensure you outline what you have changed and why it is necessary to make this change.

Citation

When using the ORMIR_XCT package, please use the following citation:

  • Kuczynski et al., (2024). ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing. Journal of Open Source Software, 9(97), 6084, https://doi.org/10.21105/joss.06084