Experimental considerations for fast kurtosis imaging

Abstract

Purpose The clinical use of kurtosis imaging is impeded by long acquisitions and post-processing. Recently, estimation of mean kurtosis tensor W̅ and mean diffusivity (D̅) was made possible from 13 distinct DWI acquisitions (the 1-3-9 protocol) with simple post-processing. Here, we analyze the effects of noise and nonideal diffusion encoding, and propose a new correction strategy. We also present a 1-9-9 protocol with increased robustness to experimental imperfections and minimal additional scan time. This refinement does not affect computation time and also provides a fast estimate of fractional anisotropy (FA). Methods 1-3-9/1-9-9 data are acquired in rat and human brains, and estimates of D̅, FA, W̅ from human brains are compared to traditional estimates from an extensive DKI data set. Simulations are used to evaluate the influence of noise and diffusion encodings deviating from the scheme, and the performance of the correction strategy. Optimal b-values are determined from simulations and data. Results Accuracy and precision in D̅ and W̅ are comparable to nonlinear least squares estimation, and is improved with the 1-9-9 protocol. The compensation strategy vastly improves parameter estimation in non-ideal data. Conclusion The framework offers a robust and compact method for estimating several diffusion metrics. The protocol is easily implemented.

Efficient experimental designs for isotropic generalized diffusion tensor MRI (IGDTI)

“…For SNR levels higher than 50, the precision of the estimated parameters improves with increasing SNR. These results are consistent with similar findings in the literature (29) and with our empirical results (Figs. 3 and 6).…”

“…3 and 6). However, as suggested in Hansen et al (29), increasing orientational sampling at lower b-values may improve both the accuracy and the rotation invariance of these estimated parameters.…”

Recent Developments in Fast Kurtosis Imaging

“…While not explored initially, subsequent analysis of these effects showed the 1-3-9 scheme to be quite sensitive to severe encoding deviations. A simple remedy was demonstrated in Hansen et al [41] where the 1-3-9 scheme was extended so that all of the nine directions in Table 1 …”

“…The effect of the 1-9-9 method's increased data foundation is apparent when comparing the schemes' robustness to experimental imperfections: numerical analysis reveals that the 1-9-9 scheme has precision similar to nonlinear least squares (NLS) fitting to large data sets, vastly outperforming the 1-3-9 scheme [41]. The effects of imperfect encoding was also addressed in a series of simulations showing that the 1-9-9 estimation quality (evaluated as the %-error compared to the ground truth value) remains below 10% even when b-values vary by as much as 10% among directions and the withinshell encoding directions deviate up to 10 • from the prescribed directions.…”

“…Here we review recent developments enabling fast kurtosis imaging based on small data sets (13 or 19 images) and with near-instant post-processing [37][38][39][40][41][42]. The review is structured such that first the theoretical foundation of DKI and WMTI is recapitulated briefly.…”