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DICOM for Quantitative Imaging

Key Investigators

• Andrey Fedorov (BWH/HMS)
• Christian Herz (BWH/HMS)
• JC (Kitware)
• Curt Lisle (KnowledgeVis, LLC)
• Joost van Griethuysen (NKI)
• Csaba Pinter (Queen's)
• Christian Bauer (U.Iowa)

Project Description

Objective

1. Discuss with the interested attendees capabilities of the DICOM standard and available support in tools for standardized communication of data produced by image analysis.
2. Triage open issues for dcmqi, prioritize and fix at least some, revisit documentation and related open issues.
3. Work on converting an existing radiomics dataset into DICOM representation.

Approach and Plan

Issues to fix:

• QIICR/QuantitativeReporting#201
• QIICR/QuantitativeReporting#209
• QIICR/QuantitativeReporting#210

Progress and Next Steps

• Fixed several dcmqi issues identified by the users during the project week
• Finished scripts for converting segmentations and corresponding radiomics features (extracted by pyradiomics) to DICOM for TCIA LIDC IDRI dataset (result of conversion for LIDC-IDRI-0011); issues identified:
  • need to integrate pyradiomics features with IBSI-based ontology
  • improve presentation of large number of features in Quantitative Reporting

Illustrations

Background and References

• https://github.com/qiicr/dcmqi

• https://qiicr.gitbooks.io/dicom4qi/content/

• 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.

• 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

• https://github.com/Radiomics/pyradiomics

• van Griethuysen, J. J. M., Fedorov, A., Parmar, C., Hosny, A., Aucoin, N., Narayan, V., Beets-Tan, R. G. H., Fillon-Robin, J. C., Pieper, S., Aerts, H. J. W. L. (2017). Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Research, 77(21), e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339