Effects of Inaccurate Response Function Calibration on Characteristics of the Fiber Orientation Distribution in Diffusion MRI

Abstract

14the response function inaccuracy than fiber populations with more orthogonal separation 15 angles. Furthermore, the FOD characteristics show deviations as a result of modified shape 16 and scaling factors of the response function. Results with the in vivo data demonstrate that the 17 deviations of the FODs and spurious peaks can further deviate the termination of propagation 18 in fiber tracking. This work highlights the importance of proper definition of the response 19 function and how specific calibration factors can affect the FOD and fiber tractography results. 20 3 21 Keywords: Diffusion MRI; constrained spherical deconvolution (CSD); response function; fiber 22 orientation distribution (FOD); brain fiber tractography; apparent fiber density (AFD). 23 4 24

The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion magnetic resonance imaging data

“…Previous work has shown a difference in sensitivity of the spherical deconvolution methods to the shape of the response function (Guo et al, 2019; Parker et al, 2013), where the choice of too isotropic response functions can lead to lower angular resolution in the deconvolution process, but can concomitantly mitigate spurious fibers. Gradient nonlinearities leading to lower effective b ‐values can in some cases lead to less anisotropic response functions, especially at lower b ‐values and crossing fibers with small separation angles.…”

“…dRL-mod was able to largely mitigate the effects of gradient deviations on directionestimates, providing similar angular deviations to dRL on signals unaffected by gradient nonlinearities. On the other hand, changes in peak magnitude were more pronounced in dRL-mod than CSDmod, indicating that the effect of gradient-nonlinearities on measures such as apparent fiber density (Raffelt et al, 2012) Previous work has shown a difference in sensitivity of the spherical deconvolution methods to the shape of the response function (Guo et al, 2019;Parker et al, 2013), where the choice of too isotro- 4.4 | Implications for multishell analyses, other methods, and future work…”

What’s new and what’s next in diffusion MRI preprocessing

“…each voxel has a unique B-matrix (or set of b -values and gradient directions). Not accounting for this can lead to significant biases, as was shown in the case of gradient nonlinearities for the estimated diffusion coefficient (up to 30% even on 1.5T 40 mT/m ( Bammer et al, 2003 )), diffusion tensor directions and diffusion/kurtosis tensor scalar measures (up to 10% and 3% respectively ( Mesri et al, 2020 )), fibre orientation distribution functions and derived fibre directions (several degrees, ( Guo et al, 2020 ; Morez et al, 2021 )), tissue signal fractions (up to 34% for WM ( Morez et al, 2021 )), tractography and connectivity analysis ( Guo et al, 2019 ; Mesri et al, 2020 ; Morez et al, 2021 ), group statistics (changes in significance and effect sizes ( Mesri et al, 2020 )), and measures derived from sequences beyond Stejskal-Tanner encoding ( Paquette et al, 2020 ). In addition, B-matrix deviations can increase the variability between scanners ( Hansen et al, 2021 ; Tax et al, 2019a).…”

Computing and visualising intra‐voxel orientation‐specific relaxation–diffusion features in the human brain

“…While this technique can be used to separate the sub‐voxel signal contributions from WM, GM, and CSF, it still assumes a single kernel for all voxels of a given tissue type, which needs to be calibrated a priori (Tax, Jeurissen, Vos, Viergever, & Leemans, 2014). Inaccuracies of the calibrated kernels can further bias the estimated fractions and fibre ODFs (Guo et al, 2019; Parker et al, 2013). Alternatively, the voxel‐wise kernel can be estimated by first factoring out the ODF through the computation of rotational invariants, and then fitting the data to signal models that set a pre‐defined number of microscopic environments with potentially constrained diffusion properties (Kaden, Kelm, Carson, Does, & Alexander, 2016; Novikov et al, 2019; Novikov, Veraart, Jelescu, & Fieremans, 2018).…”