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....

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Vydáno v:Magnetic resonance in medicine Ročník 86; číslo 3; s. 1600 - 1613
Hlavní autoři: Henriques, Rafael N., Jespersen, Sune N., Jones, Derek K., Veraart, Jelle
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Wiley Subscription Services, Inc 01.09.2021
John Wiley and Sons Inc
Témata:
Benchmarking
Diffusion
Diffusion Magnetic Resonance Imaging
diffusion MRI
Diffusion Tensor Imaging
DKI
Full Papers—Computer Processing and Modeling
Kurtosis
parameter estimation
Parameters
Reproducibility
Reproducibility of Results
Robustness
Statistical analysis
Thermal imaging
Thermal noise
diffusion MRI
robustness
parameter estimation
DKI
kurtosis
ISSN:0740-3194, 1522-2594, 1522-2594
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Shrnutí: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.
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ISSN:0740-3194
1522-2594
1522-2594
DOI:10.1002/mrm.28730