REKINDLE: Robust Extraction of Kurtosis INDices with Linear Estimation

Abstract

Purpose: Recent literature shows that diffusion tensor properties can be estimated more accurately with diffusion kurtosis imaging (DKI) than with diffusion tensor imaging (DTI). Furthermore, the additional non-Gaussian diffusion features from DKI can be sensitive markers for tissue characterization. Despite these benefits, DKI is more susceptible to data artifacts than DTI due to its increased model complexity, higher acquisition demands, and longer scanning times. To increase the reliability of diffusion tensor and kurtosis estimates, we propose a robust estimation procedure for DKI. Methods: We have developed a robust and linear estimation framework, coined REKINDLE (Robust Extraction of Kurtosis INDices with Linear Estimation), consisting of an iteratively reweighted linear least squares approach. Simulations are performed, in which REKINDLE is evaluated and compared with the widely used RESTORE (Robust EStimation of Tensors by Outlier REjection) method. Results: Simulations demonstrate that in the presence of outliers, REKINDLE can estimate diffusion and kurtosis indices reliably and with a 10-fold reduction in computation time compared with RESTORE. Conclusion: We have presented and evaluated REKINDLE, a linear and robust estimation framework for DKI. While REKINDLE has been developed for DKI, it is by design also applicable to DTI and other diffusion models that can be linearized.