Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates

Abstract

AbstractPurposeThe purpose of the study is to identify differences between axisymmetric diffusion kurtosis imaging (DKI) and standard DKI, their consequences for biophysical parameter estimates, and the protocol choice influence on parameter estimation.MethodsNoise‐free and noisy, synthetic diffusion MRI human brain data is simulated using standard DKI for a standard and the fast “199” acquisition protocol. First the noise‐free “baseline” difference between both DKI models is estimated and the influence of fiber complexity is investigated. Noisy data is used to establish the signal‐to‐noise ratio at which the baseline difference exceeds noise variability. The influence of protocol choices and denoising is investigated. The five axisymmetric DKI tensor metrics (AxTM), the parallel and perpendicular diffusivity and kurtosis and mean of the kurtosis tensor are used to compare both DKI models. Additionally, the baseline difference is also estimated for the five parameters of the WMTI‐Watson model.ResultsThe parallel and perpendicular kurtosis and all of the WMTI–Watson parameters had large baseline differences. Using a Westin or FA mask reduced the number of voxels with large baseline difference, that is, by selecting voxels with less complex fibers. For the noisy data, precision was worsened by the fast “199” protocol but adaptive denoising can help counteract these effects.ConclusionFor the diffusivities and mean of the kurtosis tensor, axisymmetric DKI with a standard protocol delivers similar results as standard DKI. Fiber complexity is one main driver of the baseline differences. Using the “199” protocol worsens precision in noisy data but adaptive denoising mitigates these effects.