The products of different scanners and imaging sites vary, meaning that structural MRI studies can have biases and can lack reproducibility. The investigators proposed a statistical method called RAVEL, which normalizes magnetic resonance imaging (MRI) intensities across scanners and imaging sites. RAVEL is a quick, easy method that allows imaging from multiple MRI scans to be quantitatively integrated, so that we can replicate new scientific discoveries.
The investigators modeled scanner effects, using a control region of the brain that is not associated with clinical covariates. They showed that RAVEL makes MRI scans more comparable in imaging databases. RAVEL promises increased sensitivity to disease-related changes; in particular, it makes the brain regions associated with Alzheimer’s disease in a large multi-center study more replicable.