A resource for development and comparison of harmonisation methods for multi-modal brain MRI data
Asante Ntata1, Olivier Mougin2, Matteo Bastiani1, Fidel Alfaro Almagro3, Jon Campbell3, Paul S Morgan2, Mark Jenkinson3,4, and Stamatios N Sotiropoulos1,3
1Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom, 2Sir Peter Mansfield Imaging Centre, School of Physics, University of Nottingham, Nottingham, United Kingdom, 3Wellcome Centre for Integrative Neuroimaging (WIN - FMRIB), University of Oxford, Oxford, United Kingdom, 4Australian Institute for Machine Learning, University of Adelaide, Adelaide, Australia
A key challenge in robustly extracting quantitative information from MRI data is the dependence of derived features on nuisance factors, such as the scanning protocol, hardware and software, which are different between vendors and vary with site. While there exist several harmonisation approaches, what’s missing is objective ways and datasets to compare them. Here we present a novel multi-modal neuroimaging data resource for evaluating and comparing harmonisation approaches based on a “travelling heads” paradigm. We further demonstrate how such a resource can be used to a) map the need for harmonisation for different imaging-derived features, b) evaluate existing harmonisation approaches.
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