Toward more robust and reproducible diffusion kurtosis imaging.

Rafael N Henriques,Jelle Veraart,Derek K Jones,Sune N Jespersen

doi:10.1002/mrm.28730

Abstract

PurposeThe 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 MethodsA 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.ResultsThe regularized DKI estimator improves the robustness and reproducibility of the kurtosis metrics and results in parameter maps with enhanced quality and contrast.ConclusionOur 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.

Highlights

Despite a growing interest in biophysical models of diffusion in white matter to develop specific biomarkers of microstructural changes,[1] signal representations, for example, diffusion tensor imaging (DTI)[2] or diffusion kurtosis imaging (DKI),[3] retain the potential to become invaluable tools in diagnostic and clinical research settings
DKI forms a straightforward extension of DTI and provides, aside from the diffusion tensor, an estimate of the diffusion kurtosis tensor which quantifies the degree of directional non-Gaussian diffusion.[3,35,36]
We introduce a regularized DKI estimator in which the estimated mean kurtosis is evaluated against a robust prediction of the mean kurtosis, which in turn is derived from the powder kurtosis

Summary

Funding information

Wellcome Trust, Grant/Award Number: 096646/Z/11/Z and 104943/Z/14/Z; National Institute of Neurological Disorders and Stroke, Grant/Award Number: R01 NS088040; Engineering and Physical Sciences Research Council, Grant/Award Number: EP/M029778/1; National Institute of Biomedical Imaging and Bioengineering, Grant/Award Number: P41 EB017183 and R01 EB025133

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