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Abstract #2464

Towards more robust and reproducible Diffusion Kurtosis Imaging

Rafael N Henriques1, Sune N. Jespersen2,3, Derek K. Jones4,5, and Jelle Veraart6
1Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal, 2Department of Clinical Medicine, Aarhus University, Aarhus, Denmark, 3Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark, 4School of Psychology, Cardiff University, Cardiff, United Kingdom, 5Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia, 6Center for Biomedical Imaging, NYU Grossman School of Medicine, New York, NY, United States

The general utility of Diffusion Kurtosis Imaging (DKI) is challenged by its poor robustness to imaging artifacts and thermal noise that often lead to implausible kurtosis values. A robust scalar kurtosis index can be estimated from powder-averaged diffusion-weighted data. We introduce a novel DKI estimator that uses this scalar kurtosis index as a proxy for the mean kurtosis to regularize the fit. The regularized DKI estimator improves the robustness and reproducibility of the kurtosis metrics and results in parameter maps with enhanced quality and contrast; thereby promoting the wider use of DKI in clinical research and potentially diagnostics.

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