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Statistical Approaches to Mitigating Inter-Scanner Differences in Magnetic Resonance Imaging Studies; By Russell Shinohara, Associate Professor of Biostatistics, University of Pennsylvania Perelman School of Medicine
January 24, 2020 @ 11:30 am - 12:30 pm
While magnetic resonance imaging (MRI) studies are critical for the diagnosis, monitoring, and study for a wide variety of diseases, their use in quantitative analysis can be complex. An increasingly recognized issue involves the differences between MRI scanners that are used in large multi-center studies. To address this, the current state of the art is to “regress out” or “adjust for” scanner differences. Our group has found these methods to be insufficient, and have advocated for the adaptation of methods pioneered in genomics to help mitigate inter- scanner differences which can vary across the brain and result in both mean and variance shifts. We further study the implications of differences in correlation structures across and between images, and how this affects downstream inference.