Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity.

Siawoosh Mohammadi, Tobias Streubel, Leonie Klock, Luke J Edwards, Antoine Lutti, Kerrin J Pine, Sandra Weber, Patrick Scheibe, Gabriel Ziegler, Jürgen Gallinat, Simone Kühn, Martina F Callaghan, Nikolaus Weiskopf, Karsten Tabelow

Journal: NeuroImage 2022;262():119529

PMID: 35926761

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Abstract

Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R and R, proton density PD, and magnetization transfer saturation MT) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R, and MT maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the method's sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R and decreased from PD to MT by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.

Address: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. Electronic address: [email protected].; Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.; Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.; Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.; Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, Germany; Germany Weierstrass Institute for Applied Analysis and Stochastics, German Center for Neurodegenerative Diseases, Magdeburg, Berlin, Germany.; Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Max Planck Institute for Human Development, Lise Meitner Group for Environmental Neuroscience, Berlin, Germany.; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, UCL, London, UK.; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Felix Bloch Institute for Solid State Physics, Leipzig University, Linnéstraße 5, Leipzig 04103, Germany.; Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstrasse 39, Berlin 10117, Germany.

DOI: 10.1016/j.neuroimage.2022.119529