Skip to content
forked from QIICR/dcmqi

dcmqi (DICOM for Quantitative Imaging) is a free, open source library that can help with the conversion between imaging research formats and the standard DICOM representation for image analysis results

License

Notifications You must be signed in to change notification settings

michaelonken/dcmqi

 
 

Repository files navigation

OpenHub codecov

CI Build Linux Windows Mac
Build Status for latest GitHub CI Linux GitHub CI Windows GitHub CI Mac

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, dcmqi can help you with the 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 NCI Imaging Data Commons 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 commercial 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 project has been supported in part by the following funded initiatives:

References

  1. Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S., Aerts, H. J. W. L., Homeyer, A., Lewis, R., Akbarzadeh, A., Bontempi, D., Clifford, W., Herrmann, M. D., Höfener, H., Octaviano, I., Osborne, C., Paquette, S., Petts, J., Punzo, D., Reyes, M., Schacherer, D. P., Tian, M., White, G., Ziegler, E., Shmulevich, I., Pihl, T., Wagner, U., Farahani, K. & Kikinis, R. NCI Imaging Data Commons. Cancer Res. 81, 4188–4193 (2021). https://dx.doi.org/10.1158/0008-5472.CAN-21-0950

  2. 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

  3. 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.

About

dcmqi (DICOM for Quantitative Imaging) is a free, open source library that can help with the conversion between imaging research formats and the standard DICOM representation for image analysis results

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • C++ 76.1%
  • CMake 16.7%
  • Python 3.5%
  • Java 2.0%
  • XSLT 0.8%
  • Makefile 0.6%
  • Other 0.3%