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REKINDLE: Robust extraction of kurtosis INDices with linear estimation
Author(s) -
Tax Chantal M.W.,
Otte Willem M.,
Viergever Max A.,
Dijkhuizen Rick M.,
Leemans Alexander
Publication year - 2015
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.25165
Subject(s) - kurtosis , statistics , estimation , computer science , mathematics , artificial intelligence , pattern recognition (psychology) , economics , management
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. Magn Reson Med 73:794–808, 2015. © 2014 Wiley Periodicals, Inc.