Meeting Banner
Abstract #3199

SNR Dependence of Mean Kurtosis and How to Correct It

Elodie Andr1, Christophe Phillips1, 2, Ezequiel Farrher3, Ivan I. Maximov4, Farida Grinberg3, Nadim Jon Shah4, 5, Evelyne Balteau1

1Cyclotron research center, University of Lige, Lige, Belgium; 2Department of Electrical Engineering and Computer Science, University of Lige, Lige, Belgium; 3Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich, Juelich, Germany; 4Institute of Neuroscience and Medicine - 4, Forschungszentrum Juelich GmbH, Juelich, Germany; 5Department of Neurology, Faculty of Medicine, RWTH Aachen University, JARA, Aachen, Germany

The use of high b-values in diffusion kurtosis imaging makes the derived parameters very sensitive to low signal to noise. Here we show the dependence of mean kurtosis on SNR and demonstrate that noise correction is a necessary step, leading to more reproducible metrics.

Keywords

accordingly actual adapted alone analytical applied approximated atlas audience basic bias body built capsule central channel coil coinciding combination complex computer corona corr correct corrected correction corrections critical crucial dashed defined delineated dependence dependent described deviation diffusion discussed distribution divided efficient electrical emphasized engineering erroneous especially estimation evaluates example exhibit experiment expression extra faculty filter formula frontal function generally globally head healthy herein histograms illustrates impact improved independent inter interior internal intra introduced introducing kurtosis lead lobe local longer look magnitude maps materials matrix medical medicine metrics motion multichannel must networks neurology noise onto optional otherwise oxford particularly people position positions power practically prevent prior procedures process processing protocol pulses realigned receive reduced reduction refocused remaining repetitions reproducible rigid scanner schemes science scientists shah simple slice solutions spatial step steps strong structural subject subjects supported table target temporal track training trans transform true trust twice variability varying volumes volunteer volunteers white