Draft Status
Draft - team will hold off on page creation
Category
DICOM
Key Investigators
- Dave Dinh (Consultant, Brigham and Women's Hospital, USA)
- Deepa Krishnaswamy (Brigham and Women's Hospital, USA)
- Matt McCormick (Fideus Labs, USA)
- Andras Lasso (Queen's, Canada)
- Steve Pieper (Isomics, USA)
- Tina Kapur (Brigham and Women's Hospital, USA)
Project Description
DICOM de-identification (De-ID) efforts often overlap in the rules used to process DICOM metadata. This project aims to translate an existing DICOM De-ID standard into a set of actionable rules that can serve both as an implementation guide and as a verifiable audit trail for tools and AI systems.
For example, a standard may permit multiple actions for a given metadata field. A baseline reference can recommend a default action, while still allowing users to specify alternative behaviors as needed. The proposed generator will take user input and produce:
- A reference list of itemized, actionable rules
- A unit and end-to-end test specification that users can apply to their specific use cases
The initial user interface for the generator will be a command-line (CLI) tool. It will operate based on a predefined decision tree and output rules and test specifications in formats suitable for humans, AI systems, and supported programming languages.
In practice, DICOM De-ID outputs can vary depending on modality, imaging protocols, contractual requirements, and application domains. However, there remains a consistent need to validate that outputs conform to a predefined set of rules. This library represents a step forward in programmatically generating verifiable rules for reliable and consistent implementation.
Objective
No response
Approach and Plan
We want to create a well tested CLI based tool that takes in user input and generates the following:
- A reference list of itemized, actionable rules
- A unit and end-to-end test specification that users can apply to their specific use cases
Progress and Next Steps
Illustrations
No response
Background and References
https://dicom.nema.org/medical/dicom/current/output/html/part15.html#sect_E.1.1
https://github.com/clintools/dicom-curate
https://github.com/pydicom/deid
Draft Status
Draft - team will hold off on page creation
Category
DICOM
Key Investigators
Project Description
DICOM de-identification (De-ID) efforts often overlap in the rules used to process DICOM metadata. This project aims to translate an existing DICOM De-ID standard into a set of actionable rules that can serve both as an implementation guide and as a verifiable audit trail for tools and AI systems.
For example, a standard may permit multiple actions for a given metadata field. A baseline reference can recommend a default action, while still allowing users to specify alternative behaviors as needed. The proposed generator will take user input and produce:
The initial user interface for the generator will be a command-line (CLI) tool. It will operate based on a predefined decision tree and output rules and test specifications in formats suitable for humans, AI systems, and supported programming languages.
In practice, DICOM De-ID outputs can vary depending on modality, imaging protocols, contractual requirements, and application domains. However, there remains a consistent need to validate that outputs conform to a predefined set of rules. This library represents a step forward in programmatically generating verifiable rules for reliable and consistent implementation.
Objective
No response
Approach and Plan
We want to create a well tested CLI based tool that takes in user input and generates the following:
Progress and Next Steps
Illustrations
No response
Background and References
https://dicom.nema.org/medical/dicom/current/output/html/part15.html#sect_E.1.1
https://github.com/clintools/dicom-curate
https://github.com/pydicom/deid