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Abstract #0700

REKINDLE: Robust Extraction of Kurtosis INDices with Linear Estimation

Chantal M.W. Tax1, Willem M. Otte1, Max A. Viergever1, Rick M. Dijkhuizen1, Alexander Leemans1

1Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands

Diffusion kurtosis imaging (DKI) provides new avenues for an accurate and complete tissue characterization within clinically feasible scanning times. In a clinical setting, however, such benefits are often nullified by numerous acquisition artifacts. In this work, we propose to extend the popular Robust Estimation of Tensors by Outlier Rejection (RESTORE) approach, which is widely used in diffusion tensor imaging (DTI), to DKI. In addition, a linearized framework, coined REKINDLE (Robust Extraction of Kurtosis INDices with Linear Estimation), has been developed that drastically reduces the computational cost without compromising the estimation reliability.

Keywords

able accuracy accurate accurately acquisition added addition applications artifact artifacts avenues benefit benefits bottom cerebellum challenging channel characteristics characterization clearly clinical clinically coil coined collected color common commonly complete computation computational confidence consist context convergence corrupted cost created criteria dataset deal degree demands diffusion diffusivity displays drastically eight encoded equal estimation even excluding extension extraction fast feasible female final fitting fractional framework frameworks function furthermore gives gradient ground head healthy improves initial intensities interleaving interval investigated isotropic kurtosis least limited linear linearized longer maps measures medical metrics middle minutes model motion moves neural noise nonlinear nullified numerous often outliers particular pathological performance perturbations picture popular presence procedure procedures processing propose pulsation quantify radial recomputed reduces reduction rejection rekindle relation removed repeated residuals restore rick risk robust robustly roughly scanner scanning sciences sensitive sequentially shot simulated slices slow sphere spin squares steps subject tenfold tensor tensors tissue truth uneven uniformly visible volunteer