Abstract #4891

Harmonization of Longitudinal MRI Scans in the Presence of Scanner Changes

Blake E. Dewey^1,2, Can Zhao¹, Aaron Carass¹, Jiwon Oh³, Peter A Calabresi³, Peter C. M. van Zijl^4,5, and Jerry L Prince^1,5

¹Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States, ²Kirby Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ³Neurology, Johns Hopkins University, Baltimore, MD, United States, ⁴F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ⁵Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, United States

Longitudinal studies are frequently hampered by changes to scanning protocols, forcing research centers to forgo recommended upgrades to scanning equipment, software, and scan protocol design to allow for consistent scanning. Using a harmonization method that utilizes deep learning and a small (n=12) overlap cohort to learn specific differences between structural MR images before and after a significant scanning change and examined longitudinal data acquired annually over 10 years to determine if bias induced by the scanner change is still present after harmonization. We assessed these results using quantitative metrics for contrast and probed volumetric results using automated segmentation algorithms.

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