dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion between commonly used imaging research formats and the standard DICOM representation: https://qiicr.gitbooks.io/dcmqi-guide
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README.md

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Introduction

dcmqi (DICOM (dcm) for Quantitative Imaging (qi)) is a collection of libraries and command line tools with minimum dependencies to support standardized communication of quantitative image analysis research data using DICOM standard.

Specifically, the current focus of development for dcmqi is to support conversion of the following data types to and from DICOM:

As an introduction to the motivation, capabilities and advantages of using the DICOM standard, and the objects mentioned above, you might want to read this open access paper:

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

dcmqi is developed and maintained by the QIICR project.

Getting started

License

dcmqi is distributed under 3-clause BSD license.

Our goal is to support and encourage adoption of the DICOM standard in both academic and research tools. We will be happy to hear about your usage of dcmqi, but you don't have to report back to us.

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. 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. 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.