Abstract #1264

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

Luke J. Edwards¹, Siawoosh Mohammadi^1,2, Kerrin J. Pine¹, Martina F. Callaghan³, and Nikolaus Weiskopf^1,4

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

Synopsis

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 (hmri.info).

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