DCMQI

DCMQI is an open-source software tool that facilitates the interoperability of quantitative imaging (QI) biomarker research data with clinical systems using the DICOM standard. It provides a set of libraries, APIs, and command-line tools for converting and encoding QI analysis results and relevant clinical data into standardized DICOM objects, such as DICOM Real World Value Mapping, Segmentation, and Structured Reporting.

The tool was developed and applied in the context of head and neck cancer (HNC) treatment response assessment using PET/CT imaging. It supports various processing steps, including SUV normalization, tumor segmentation, and extraction of volumetric measurements. DCMQI enables the creation of DICOM-compliant representations of the analysis results, which can be validated for consistency and interoperability.

Topic

Biomarkers;Tomography;Medical imaging;MRI;Informatics

Detail

  • Operation: Quantification;Deposition;Image analysis;Parsing

  • Software interface: Command-line user interface

  • Language: Shell,C++,Python

  • License: BSD 3-Clause "New" or "Revised" License

  • Cost: Free of charge with restrictions

  • Version name: v1.3.1

  • Credit: National Institutes of Health, National Cancer Institute.

  • Input: -

  • Output: -

  • Contact: Andriy Fedorov andrey.fedorov@gmail.com

  • Collection: -

  • Maturity: Stable

Publications

  • 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.
  • Fedorov A, et al. 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. 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. 2016; 4:e2057. doi: 10.7717/peerj.2057
  • https://doi.org/10.7287/PEERJ.PREPRINTS.1541V2
  • PMID: 27257542
  • PMC: PMC4888317

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