Empirical Comparison of Diffusion Kurtosis Imaging and Diffusion Basis Spectrum Imaging Using the Same Acquisition in Healthy Young Adults

Abstract

As diffusion tensor imaging gains widespread use, many researchers have been motivated to go beyond the tensor model and fit more complex diffusion models, to gain a more complete description of white matter microstructure and associated pathology. Two such models are diffusion kurtosis imaging (DKI) and diffusion basis spectrum imaging (DBSI). It is not clear which DKI parameters are most closely related to DBSI parameters, so in the interest of enabling comparisons between DKI and DBSI studies, we conducted an empirical survey of the interrelation of these models in 12 healthy volunteers using the same diffusion acquisition. We found that mean kurtosis is positively associated with the DBSI fiber ratio and negatively associated with the hindered ratio. This was primarily driven by the radial component of kurtosis. The axial component of kurtosis was strongly and specifically correlated with the restricted ratio. The joint spatial distributions of DBSI and DKI parameters are tissue-dependent and stable across healthy individuals. Our contribution is a better understanding of the biological interpretability of the parameters generated by the two models in healthy individuals.

Microstructural changes of peripheral nerves in early multiple sclerosis: A prospective magnetic resonance neurography study

“…This is in line with our current DTI results as well as with previous MRN studies using T2 relaxometry in MS [8–10]. The negative correlation between RD and RK as well as the positive association between RK and FA values has been consistently described in previous investigations [38, 39].…”

Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit

“…This is more evident in the MK, AK and RK maps. MK remains almost stable in terms of reproducibility across the different fiber crossings compared to the other maps, including DKI-derived DTI metrics (38) . However, we also observed a lower variability in the 60 0 phantoms in all the datasets, which could be related to DKI's ability to resolve fibers close to this configuration, but further analysis (outside the scope of the current study) is necessary to support this hypothesis.We have found the CoV for separately printed 3D phantoms with identical crossing angles to be between 2 and 8%, depending on the DKI parameter.…”

Design and Validation of Diffusion MRI Models of White Matter

“…Nearly all models outperform DTI in capturing tissue diffusion properties, but this is a very low benchmark. Biophysical models need to be increasingly compared to one another, and to higher order signal representations such as DKI, to understand convergent model properties likely to be successful in broad applications (Jelescu et al, 2015; Kamiya et al, 2017; Reisert et al, 2017; Wang et al, 2017). Their reliability in the case of pathological tissue also needs thorough investigation.…”