REKINDLE: Robust Extraction of Kurtosis INDices with Linear Estimation

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

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.