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Introduction

About

This is user guide for the dcmqi (DICOM for Quantitative Imaging) library.
With dcmqi you can:
  • Convert certain types of quantitative image analysis results into standardized DICOM form. This can help you with
    • sharing data in archives like TCIA​
    • interoperating with PACS and commercial tools
    • supporting data queries to both image data and analysis results
    • standardizing data semantics
    • making your data self-described and better prepared for new uses
  • Convert DICOM data into a commonly used research file formats like JSON and NIfTI.
  • Integrate DICOM concepts into your analysis workflows so that intermediate results are encoded in a standardized manner, making it easy to share your data.

License

dcmqi is distributed under 3-clause BSD license.

Tutorials

Check out our introductory tutorial!

Support

You can communicate you feedback, feature requests, comments or problem reports using any of the methods below:

Acknowledgments

To acknowledge dcmqi in an academic paper, please cite
Herz C, Fillion-Robin J-C, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. Cancer Research. 2017;77(21):e87–e90 http://cancerres.aacrjournals.org/content/77/21/e87.
If you like dcmqi, please give the dcmqi repository a star on github. This is an easy way to show thanks and it can help us qualify for useful services that are only open to widely recognized open projects.
This work is supported primarily by the National Institutes of Health, National Cancer Institute, Informatics Technology for Cancer Research (ITCR) program, grant Quantitative Image Informatics for Cancer Research (QIICR) (U24 CA180918, PIs Kikinis and Fedorov). We also acknowledge support of the following grants: Neuroimage Analysis Center (NAC) (P41 EB015902, PI Kikinis) and National Center for Image Guided Therapy (NCIGT) (P41 EB015898, PI Tempany).

References

  1. 1.
    Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, Onken M, Riesmeier J, Pieper S, Kikinis R, Buatti J, Beichel RR. (2016) DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ 4:e2057 https://doi.org/10.7717/peerj.2057​
  2. 2.
    Herz C, Fillion-Robin J-C, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. Cancer Research. 2017;77(21):e87–e90 http://cancerres.aacrjournals.org/content/77/21/e87.
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