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Toward more robust and reproducible diffusion kurtosis imaging.
Rafael N Henriques1, Sune N Jespersen2,3, Derek K Jones4,5
1Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal.
Magnetic Resonance in Medicine
|April 8, 2021
Summary
Diffusion kurtosis imaging (DKI) can be improved with a novel, robust estimator. This method enhances kurtosis metric precision and quality for better clinical research and diagnostics.
Area of Science:
- Neuroimaging
- Biomedical Engineering
Background:
- Diffusion kurtosis imaging (DKI) offers valuable insights but suffers from artifacts and noise, leading to unreliable kurtosis values.
- Poor robustness limits the clinical utility and diagnostic potential of conventional DKI methods.
Purpose of the Study:
- To develop a novel DKI estimator that enhances robustness and reproducibility.
- To improve the quality and contrast of DKI parameter maps for better analysis.
Main Methods:
- Introduced a novel DKI estimator utilizing a robust scalar kurtosis index derived from powder-averaged diffusion-weighted data.
- Employed the scalar kurtosis index as a proxy for mean kurtosis to regularize the DKI fitting process.
Main Results:
- The regularized DKI estimator significantly improved the robustness and reproducibility of kurtosis metrics.
- Resulting parameter maps exhibited enhanced quality and contrast compared to conventional methods.
Conclusions:
- The novel DKI estimator enhances the precision and reproducibility of DKI fitting.
- This promotes wider adoption of DKI in clinical research and diagnostics for improved analyses.
