Statistical analyses of motion-corrupted MRI relaxometry data computed from multiple scans

Nadège Corbin; Rita Oliveira; Quentin Raynaud; Giulia Di Domenicantonio; Bogdan Draganski; Ferath Kherif; Martina F Callaghan; Antoine Lutti

doi:10.1016/j.jneumeth.2023.109950

Statistical analyses of motion-corrupted MRI relaxometry data computed from multiple scans

J Neurosci Methods. 2023 Oct 1:398:109950. doi: 10.1016/j.jneumeth.2023.109950. Epub 2023 Aug 19.

Authors

Nadège Corbin¹, Rita Oliveira², Quentin Raynaud², Giulia Di Domenicantonio², Bogdan Draganski³, Ferath Kherif², Martina F Callaghan⁴, Antoine Lutti⁵

Affiliations

¹ Centre de Résonance Magnétique des Systèmes Biologiques, UMR5536, CNRS/University Bordeaux, Bordeaux, France; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK.
² Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
³ Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; Neurology Department, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
⁴ Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK.
⁵ Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland. Electronic address: antoine.lutti@chuv.ch.

PMID: 37598941
DOI: 10.1016/j.jneumeth.2023.109950

Abstract

Background: Consistent noise variance across data points (i.e. homoscedasticity) is required to ensure the validity of statistical analyses of MRI data conducted using linear regression methods. However, head motion leads to degradation of image quality, introducing noise heteroscedasticity into ordinary-least square analyses.

New method: The recently introduced QUIQI method restores noise homoscedasticity by means of weighted least square analyses in which the weights, specific for each dataset of an analysis, are computed from an index of motion-induced image quality degradation. QUIQI was first demonstrated in the context of brain maps of the MRI parameter R2 * , which were computed from a single set of images with variable echo time. Here, we extend this framework to quantitative maps of the MRI parameters R1, R2 * , and MTsat, computed from multiple sets of images.

Results: QUIQI restores homoscedasticity in analyses of quantitative MRI data computed from multiple scans. QUIQI allows for optimization of the noise model by using metrics quantifying heteroscedasticity and free energy.

Comparison with existing methods: QUIQI restores homoscedasticity more effectively than insertion of an image quality index in the analysis design and yields higher sensitivity than simply removing the datasets most corrupted by head motion from the analysis.

Conclusion: QUIQI provides an optimal approach to group-wise analyses of a range of quantitative MRI parameter maps that is robust to inherent homoscedasticity.

Keywords: Heteroscedasticity; Motion corruption; Quantitative MRI; Statistical group analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms*
Brain / diagnostic imaging
Magnetic Resonance Imaging* / methods
Motion
Reproducibility of Results

Grants and funding

203147/Z/16/Z/WT_/Wellcome Trust/United Kingdom