Quality control for functional magnetic resonance imaging using automated data analysis and Shewhart charting

Area of Science:

• Neuroimaging
• Medical Physics
• Data Science

Background:

• Functional magnetic resonance imaging (fMRI) studies require rigorous quality control (QC) for reliable results.
• Manual data analysis for fMRI QC is time-consuming and hinders regular implementation.
• Existing QC methods may fail to detect subtle system faults or performance degradations.

Purpose of the Study:

• To present a novel data acquisition and analysis protocol for automated fMRI quality control.
• To improve the efficiency and sensitivity of fMRI QC procedures.
• To ensure the long-term stability and performance of fMRI systems.

Main Methods:

• Acquisition of single-timepoint data for signal-to-ghost and signal-to-noise ratio measurement.
• Acquisition of multiple-timepoint data for short-term drift assessment.
• Implementation of an automated data processing scheme with Shewhart charting for trend analysis.

Main Results:

• The automated protocol successfully measured key QC parameters including signal-to-ghost, signal-to-noise ratios, and short-term drift.
• Automated Shewhart charting identified significant parameter changes over time.
• The protocol detected system faults and deteriorations missed by conventional QC methods.

Conclusions:

• An automated fMRI QC protocol offers an efficient and effective solution for maintaining data quality.
• The proposed method enhances the detection of system issues, ensuring the integrity of fMRI studies.
• This automated approach supports regular and reliable QC, crucial for advancing neuroimaging research.