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

Abstract

Background: Magnetic resonance imaging (MRI) is an important yet complex data acquisition technology for studying the brain. MRI signals can be affected by many factors and many sources of variance are often simply attributed to “noise”. Unexplained variance in MRI data hinders the statistical power of MRI studies and affects their reproducibility. We hypothesized that it would be possible to use phantom data as a proxy of scanner characteristics with a simplistic model of seasonal variation to explain some variance in human MRI data. Methods: We used MRI data from human participants collected in several studies, as well as phantom data collected weekly for scanner quality assurance (QA) purposes. From phantom data we identified the variables most likely to explain variance in acquired data and assessed their statistical significance by using them to model signal-to-noise ratio (SNR), a fundamental MRI QA metric. We then included phantom data SNR in the models of morphometric measures obtained from human anatomical MRI data from the same scanner. Results: Phantom SNR and seasonal variation, after multiple comparisons correction, were statistically significant predictors of the volume of gray brain matter. However, a sweep over 16 other brain matter areas and types revealed no statistically significant predictors among phantom SNR or seasonal variables after multiple comparison correction. Conclusions: Seasonal variation and phantom SNR may be important factors to account for in MRI studies. Our results show weak support that seasonal variations are primarily caused by biological human factors instead of scanner performance variation. The phantom QA metric and scanning parameters are useful for more than just QA. Using QA metrics, scanning parameters, and seasonal variation data can help account for some variance in MRI studies, thus making them more powerful and reproducible.

The art and science of using quality control to understand and improve fMRI data

“…An additional element of QC is QC of the acquisition hardware, which should be checked regularly as part of the operational procedures of any fMRI research facility. Since there are already multiple resources for this type of fMRI QC ( Friedman et al, 2006 ; Liu et al, 2015 ; Cheng and Halchenko, 2020 ), we are limiting our scope to QC that is specific to the data collected during a study. The appendix summarizes the suggestions in this framework for use as a guide when designing a study-specific QC protocol.…”

A resource for development and comparison of multi-modal brain 3T MRI harmonisation approaches

“…Such factors can affect IDPs in non-trivial ways (Takao et al, 2011;Zhu et al, 2011), leading to biases and increased variability in measurements obtained from different settings (Chen et al, 2014;Jovicich et al, 2006;Vollmar et al, 2010). This is true, even in cases where scans have been acquired with a rigid acquisition protocol or calibrated with phantoms; quantitative measurements can still show variance reflecting non-biological causes (Cheng and Halchenko, 2020;Lee et al, 2021). This lack of consistency or "harmonisation" across sites and scanners impedes and reduces the potential for quantitative applications of MRI.…”

Assessing radiomics feature stability with simulated CT acquisitions

“…Especially for CT, advances in 3D printing technologies allow printing volumetric patient images using materials with attenuation properties comparable to human tissue. Recent work reported variability studies using such phantoms [23][24][25] .…”