A new virtue of phantom MRI data: explaining variance in human participant data

Area of Science:

• Neuroimaging
• Medical Physics

Background:

• Magnetic Resonance Imaging (MRI) is crucial for brain studies but susceptible to various signal-affecting factors.
• Unexplained variance in MRI data reduces statistical power and reproducibility.
• Phantom data can serve as a proxy for scanner characteristics to model variance.

Purpose of the Study:

• To investigate if phantom data and seasonal variation can explain variance in human MRI data.
• To assess the impact of scanner performance and environmental factors on neuroimaging metrics.

Main Methods:

• Utilized human participant MRI data and weekly phantom data for scanner quality assurance (QA).
• Modeled signal-to-noise ratio (SNR) using phantom data variables to identify key predictors.
• Incorporated phantom SNR into models of human anatomical MRI morphometric measures.

Main Results:

• Phantom SNR and seasonal variation significantly predicted gray matter volume after correction.
• No other brain matter areas showed significant prediction by phantom SNR or seasonal variables.
• Results suggest seasonal variations may stem from scanner performance rather than human biological factors.

Conclusions:

• Seasonal variation and phantom SNR are important considerations for MRI studies.
• These factors can help account for variance, enhancing MRI study power and reproducibility.
• Phantom QA metrics and scanning parameters offer value beyond routine quality assurance.