Extended neighbourhood based linear reconstruction of Diffusion Kurtosis Imaging

Abstract

Diffusion weighted magnetic resonance imaging(DW-MRI) is used for the quantification of water diffusion with the availability of various tensor based models such as Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI). The increased significance of DKI parameters for the assessment of neurologic diseases as compared to DTI parameters has been shown in several recent studies. Kurtosis tensors were reconstructed using either linear or non-linear least squares approaches including several variants of these approaches. In this work, we proposed an extended linear least squares(LLS) reconstruction of DKI parameters which makes use of the correlation existing in the DW-MRI data in order to have a robust and accurate estimation of the kurtosis parameters. All the available methods of DKI reconstruction uses an independent voxel-wise estimation of the kurtosis parameters. The proposed method attempts to make use of the spatial correlation in DW-MRI by including the neighbourhood voxels for the estimation of kurtosis parameters voxel-wise. Our study includes simulation and real data experiments for validation of the proposed method. The estimation from the proposed method revealed better details and accuracy as compared to LLS and weighted LLS approaches. The proposed method is also robust to noise which is illustrated by using noise corrupted data for different levels of added rician noise.