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Abstract #1264

Robust and efficient R2* estimation in human brain using log-linear weighted least squares

Luke J. Edwards1, Siawoosh Mohammadi1,2, Kerrin J. Pine1, Martina F. Callaghan3, and Nikolaus Weiskopf1,4
1Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 3Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, 4Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany


In vivo maps of R2* in human brain hold promise for neuroscientific investigations. We tested several R2* fitting routines (nonlinear least squares [NLLS; silver standard], auto-regression on linear operations [ARLO], log-linear weighted least square [WLS], and log-linear ordinary least squares [OLS]) for dual flip angle multi-echo FLASH data in simulations and challenging 400 µm resolution in vivo 7T data. Log-linear WLS was found to give a good trade-off between accuracy, precision, and computational time. The method will be available in a future version of the open source hMRI toolbox (

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