Guided and Privacy-Preserving DICOM Validation for Multi-Site Studies and Domain-Specific Guidelines
Poster No:
1851
Submission Type:
Abstract Submission
Authors:
Ashley Stewart1, Gabriele Amorosino2, Jelle Veraart3, Anibal Heinsfeld2, Steffen Bollmann4, Franco Pestilli2
Institutions:
1The University of Queenland, Brisbane City, QLD, 2The University of Texas at Austin, Austin, TX, 3New York University, New York, NY, 4The University of Queensland, Brisbane, Queensland
First Author:
Co-Author(s):
Introduction:
Compliance with MRI protocols is critical to ensure data quality, study integrity, and reproducibility, but it remains challenging in several scenarios. First, in multi-site studies like clinical trials, maintaining protocol compliance is complicated by variations in scanner vendors, hardware/software versions, and user practices. Second, implementing state-of-the-art imaging guidelines, such as the 2024 Quantitative Susceptibility Mapping (QSM) Consensus recommendations, can be challenging for new studies or researchers unfamiliar with these specific imaging techniques. Configuring these protocols requires setting multiple scanner parameters, often leading to suboptimal protocols without expert guidance. Third, replicating protocols from landmark studies like the Human Connectome Project (Van Essen, 2013), ABCD Study (Casey, 2018), or UK Biobank (Bycroft, 2018) is similarly complex due to specific protocol requirements. In all cases, discrepancies between the planned protocol, scanner settings, and stored DICOMs can compromise data quality and subsequent analyses.
To address these challenges, we developed dicompare, an open-source DICOM validation tool (https://astewartau.github.io/dicompare). dicompare analyzes data at the DICOM stage, preserving all available metadata and avoiding assumptions required to convert to formats like the evolving Brain Imaging Data Structure. It validates MRI scanning sessions with specifications encoded in a session template, which can be generated from a reference session or selected from a pre-existing template library for subdomains like QSM.
dicompare runs locally in any modern web browser without installation or data transfer to the cloud, preserving privacy and making it suited to clinical environments. By validating collected data, dicompare eliminates unseen discrepancies between the planned protocol and the data collected in ongoing MRI studies.
To address these challenges, we developed dicompare, an open-source DICOM validation tool (https://astewartau.github.io/dicompare). dicompare analyzes data at the DICOM stage, preserving all available metadata and avoiding assumptions required to convert to formats like the evolving Brain Imaging Data Structure. It validates MRI scanning sessions with specifications encoded in a session template, which can be generated from a reference session or selected from a pre-existing template library for subdomains like QSM.
dicompare runs locally in any modern web browser without installation or data transfer to the cloud, preserving privacy and making it suited to clinical environments. By validating collected data, dicompare eliminates unseen discrepancies between the planned protocol and the data collected in ongoing MRI studies.
Methods:
dicompare provides a browser-based interface for DICOM validation built using WebAssembly (Haas, 2017) and Pyodide (Pyodide/pyodide, 2024) and hosted on GitHub Pages. This enables offline DICOM validation within the web browser's local sandbox.
The underlying functionality is powered by a Python package that can be run independently of the web interface. The package provides two command-line entry points and a Python API for programmable use. The main functionalities are to generate customizable templates that describe scanning sessions retrospectively and to validate incoming sessions against a chosen template. User-friendly web interfaces are available for both functionalities (see Figure 1).
The user-generated templates that describe a session use a JSON format auto-populated with parameter values parsed from reference DICOMs. The template can be interactively customized to specify string patterns and numerical tolerances. A template can also be written using a Python script format for more complex validation needs. For instance, the QSM consensus guidelines (QSM Consensus Organization Committee, 2024) recommend a uniform echo spacing (ΔTE), an example of a more sophisticated rule that numerical tolerances of specific fields alone cannot capture. Examples of these Python-based templates are accessible through the web interface and GitHub repository.
The robustness of our developments is ensured through continuous integration testing with GitHub Actions and PyTest.
The underlying functionality is powered by a Python package that can be run independently of the web interface. The package provides two command-line entry points and a Python API for programmable use. The main functionalities are to generate customizable templates that describe scanning sessions retrospectively and to validate incoming sessions against a chosen template. User-friendly web interfaces are available for both functionalities (see Figure 1).
The user-generated templates that describe a session use a JSON format auto-populated with parameter values parsed from reference DICOMs. The template can be interactively customized to specify string patterns and numerical tolerances. A template can also be written using a Python script format for more complex validation needs. For instance, the QSM consensus guidelines (QSM Consensus Organization Committee, 2024) recommend a uniform echo spacing (ΔTE), an example of a more sophisticated rule that numerical tolerances of specific fields alone cannot capture. Examples of these Python-based templates are accessible through the web interface and GitHub repository.
The robustness of our developments is ensured through continuous integration testing with GitHub Actions and PyTest.
·Overview of dicompare. Users can generate a session template based on a reference session (top row) and validate incoming data with a chosen template to generate a compliance report (bottom row).
Results:
dicompare is hosted via GitHub pages, runs in the browser, and provides interfaces for generating a session template and validating incoming sessions against a chosen template (see Figure 2).
·User interface for dicompare. a) Interface for generating a session template; b) Interface for compliance checks, showing a compliance summary with one error visible.
Conclusions:
dicompare addresses a critical gap in preemptive quality assurance for neuroimaging by directly enabling flexible and privacy-preserving validation of DICOM files in the browser. Leveraging WASM eliminates installation and external data upload requirements, ensuring compatibility with clinical constraints and privacy regulations.
Modeling and Analysis Methods:
Other Methods 2
Neuroinformatics and Data Sharing:
Workflows 1
Informatics Other
Novel Imaging Acquisition Methods:
Imaging Methods Other
Keywords:
Computing
Data analysis
Data Organization
MRI
Open-Source Code
Open-Source Software
Workflows
Other - Quality Assurance, QSM,
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
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9. Pyodide/pyodide. (2024). [Python]. pyodide. https://github.com/pyodide/pyodide (Original work published 2018).