Stefan Winzeck1,2, Maíra Siqueira Pinto3, Virginia F. J. Newcombe2, Ben Glocker1, David K. Menon2, and Marta M. Correia4
1BioMedIA, Department of Computing, Imperial College London, London, United Kingdom, 2Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, United Kingdom, 3Universitair Ziekenhuis Antwerpen, Antwerp, Belgium, 4MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
1BioMedIA, Department of Computing, Imperial College London, London, United Kingdom, 2Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, United Kingdom, 3Universitair Ziekenhuis Antwerpen, Antwerp, Belgium, 4MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
Z-scoring and ComBat could both reduce variation in FA and MD derived from different scanning protocols. The signal harmonisations on a regional or voxel-wise level were equally successful when DWI data were processed identically.
Figure1. Harmonisation of FA and MD on ROI-wise level via Z-scoring or ComBat was equally effective. While Z-scoring projected diffusion metrics independently (here to reference scheme 30x2), ComBat adjusted signal across all acquisition schemes.
Table 1. Coefficient of Variation of JHU ROI Means Before and After Data Harmonisation. All CV values displayed as mean [min, max] in %.
