Toward more robust and reproducible diffusion kurtosis imaging

Purpose: 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. Theory and Methods: 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. Results: The regularized DKI estimator improves the robustness and reproducibility of the kurtosis metrics and results in parameter maps with enhanced quality and contrast. Conclusion: Our novel DKI estimator promotes the wider use of DKI in clinical research and potentially diagnostics by improving the reproducibility and precision of DKI fitting and, as such, enabling enhanced visual, quantitative, and statistical analyses of DKI parameters.

Automated summary

The study introduces a novel DKI estimator that improves the robustness and reproducibility of kurtosis metrics by using a robust scalar kurtosis index, resulting in parameter maps with enhanced quality and contrast. This new DKI estimator promotes wider use of DKI in clinical research and diagnostics by enhancing the reproducibility and precision of DKI fitting, enabling enhanced visual, quantitative, and statistical analyses of DKI parameters.