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
Affiliations
Siawoosh Mohammadi
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; Corresponding author at: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
Tobias Streubel
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
Leonie Klock
Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
Luke J. Edwards
Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Antoine Lutti
Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
Kerrin J. Pine
Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Sandra Weber
Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
Patrick Scheibe
Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Gabriel Ziegler
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
Jürgen Gallinat
Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
Simone Kühn
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
Martina F. Callaghan
Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, UCL, London, UK
Nikolaus Weiskopf
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
Karsten Tabelow
Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstrasse 39, Berlin 10117, Germany
Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) 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, R1, and MTsat 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 R1 and decreased from PD to MTsat 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.