Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA

Area of Science:

• Medical Imaging
• Neuroimaging
• Data Science

Background:

• Multisite data pooling requires adherence to standardized Magnetic Resonance Imaging (MRI) physics parameters.
• Manual protocol compliance checks are difficult due to DICOM complexity and resource limitations.
• Deviations in MRI acquisition protocols can compromise signal-to-noise ratio (SNR), statistical power, and study validity.

Purpose of the Study:

• To develop an automated tool for assessing MRI protocol compliance.
• To investigate the prevalence and patterns of protocol non-compliance in large-scale neuroimaging datasets.
• To highlight the importance of continuous protocol monitoring in research.

Main Methods:

• Developed mrQA, an open-source tool for automatic protocol compliance assessment.
• Applied mrQA to standard data formats like DICOM and BIDS.
• Analyzed over 20 open neuroimaging datasets, including the ABCD study.

Main Results:

• Protocol non-compliance is pervasive across analyzed neuroimaging datasets.
• Common deviations include parameters like Repetition Time, Echo Time, Flip Angle, and Phase Encoding Direction.
• Scanner manufacturers (GE, Philips) showed higher non-compliance rates than Siemens in the ABCD dataset.

Conclusions:

• Automated tools like mrQA are crucial for ensuring data quality in multisite research.
• Continuous monitoring of MRI protocol compliance immediately post-acquisition is recommended to prevent data integrity issues.
• The mrQA tool is applicable to any DICOM-based dataset, extending its utility beyond neuroimaging.