Abstract #1658

ON-Harmony: A multi-site, multi-modal travelling-heads resource for brain MRI harmonisation

Shaun Warrington¹, Andrea Torchi¹, Olivier Mougin², Jon Campbell³, Asante Ntata¹, Martin Craig¹, Stephania Assimopoulos¹, Fidel Alfaro-Almagro³, Karla Miller³, Mark Jenkinson^3,4, Paul Morgan^1,5, and Stamatios Sotiropoulos^1,5

¹Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK, Nottingham, United Kingdom, ²Sir Peter Mansfield Imaging Centre, School of Physics, University of Nottingham, Nottingham, UK, Nottingham, United Kingdom, ³Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, Oxford, United Kingdom, ⁴Australian Institute for Machine Learning (AIML), School of Computer and Mathematical Sciences, The University of Adelaide, Adelaide, Australia, Adelaide, Australia, ⁵Nottingham NIHR Biomedical Research Centre, Nottingham University Hospitals NHS Trust, Queen’s Medical Centre, Nottingham UK, Nottingham, United Kingdom

Synopsis

Keywords: Data Acquisition, Multimodal, Harmonisation

Motivation: MRI quantifiability and repeatability is hindered by non-biological sources of variability (e.g. scanner hardware/software). Approaches to harmonising acquisition and data/features exist, but objective evaluation is lacking.

Goal(s): To develop one of the most comprehensive brain multi-modal MRI harmonisation resources.

Approach: We ran a travelling-heads study consisting of 20 participants, 8 3T scanners across major MRI vendors (GE/Siemens/Philips), with 5 MRI modalities (T1w/T2w/dMRI/fMRI/SWI). Nine participants had 5 additional within-scanner repeats.

Results: We showcase how the resource can be used to map between-scanner, within-scanner, between-subject variability for hundreds of neuroimaging-derived features and to evaluate efficacy of explicit and implicit harmonisation approaches.

Impact: Our openly-released multimodal brain MRI harmonisation resource considers a range of vendors and scanner generations (wide/narrow bore, low/high gradients, different coil numbers). It can therefore enable powerful studies of between-scanner effects and provide a testbed for evaluation of harmonisation approaches.

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