Influence of Noise Correction on Intra- and Inter-Subject Variability of Quantitative Metrics in Diffusion Kurtosis Imaging

Abstract

Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.

Kurtosis analysis of neural diffusion organization

“…However, the kurtosis and diffusion tensors contain the majority of the information obtainable with small diffusion weightings (Jensen and Helpern, 2010), and so this loss is likely to be minor when low b-value dMRI methods, such as DKI, are employed. In addition, by basing the modeling solely on these two tensors, KANDO can benefit from the advanced post-processing methods already available for DKI (André et al, 2014; Ghosh et al, 2014; Glenn et al, 2014; Kuder et al, 2012; Tabesh et al, 2011; Masutani and Aoki, 2014; Poot et al, 2010; Tax et al, 2014; Veraart et al, 2013; Veraart et al, 2011). …”

Novel Diffusion-Kurtosis-Informed Template Reduces Distortions due to Partial Volume Effects and Improves Statistical between-Group Comparisons

“…The post-processing steps were described in detail elsewhere [30]. In brief, DWIs were corrected for eddy-current distortions and head motion using the FDT toolkit available in FSL [33]; bias due to background noise was reduced using the power-images method [34][35][36]; DT/KT metrics were evaluated as described elsewhere with the help of the ExploreDTI toolkit [37]. The non-linear affine transformation available in FSL was used to align the FA maps to the FA template in the JHU space and the transformation matrix was applied for coregistration of the non-FA images.…”

Local estimation of the noise level in MRI using structural adaptation

“…This approach has been already considered in the literature for the estimation of the diffusion tensor (Landman et al, 2009a) or for the extended diffusion kurtosis model (Veraart et al, 2011a,b;Ghosh et al, 2013;André et al, 2014). The procedure avoids the bias in the parameter estimation that is caused by the skewness of the vdistribution causing the deviation of its expectation value from the non-centrality parameter in a noisy situation, see Eq.…”