REKINDLE : Robust Extraction of Kurtosis INDices with Linear Estimation
作者： Chantal M.W. Tax , Willem M. Otte , Max A. Viergever , Rick M. Dijkhuizen , Alexander Leemans
DOI: 10.1002/MRM.25165
关键词: Speech recognition 、 Linear least squares 、 Diffusion MRI 、 Diffusion Kurtosis Imaging 、 Kurtosis 、 Outlier 、 Diffusion (business) 、 Algorithm 、 Computer science 、 Computation 、 Reduction (complexity)
摘要: Purpose: Recent literature shows that diffusion tensor properties can be estimated more accurately with kurtosis imaging (DKI) than (DTI). Furthermore, the additional non-Gaussian features from DKI sensitive markers for tissue characterization. Despite these benefits, is susceptible to data artifacts DTI due its increased model complexity, higher acquisition demands, and longer scanning times. To increase reliability of estimates, we propose a robust estimation procedure DKI. Methods: We have developed linear framework, coined REKINDLE (Robust Extraction Kurtosis INDices Linear Estimation), consisting an iteratively reweighted least squares approach. Simulations are performed, in which evaluated compared widely used RESTORE EStimation Tensors by Outlier REjection) method. Results: demonstrate presence outliers, estimate indices reliably 10-fold reduction computation time RESTORE. Conclusion: presented REKINDLE, framework While has been DKI, it design also applicable other models linearized.
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