A data-agnostic and automatic tool for quality protocol adherence

Motivation: The vast diversity of imaging modalities, their complex acquisitions and the variability of the console interfaces across MRI vendors considerably complicates the adherence of the acquired data to the imaging protocol in large multi-site studies. Goal(s): To develop a tool which agnostically and automatically assesses the fidelity of the acquired data to a predefined set of rules. Approach: The arbitrary DICOM metadata of the acquired data is compared to JSON-based predefined rules. Results: The proposed approach shows potential to identify data that deviates from the predefined rules. This facilitates the detection of protocol deviations in real-time, minimizes potential human error and reduces costs. Impact: The protocol adherence automation tool agnostically and automatically assesses the fidelity of the acquired data to a predefined set of rules. Consequently, it allows real-time identification of protocol deviations across manufacturers and sites minimizing potential human error and reducing costs.