Experimental considerations for fast kurtosis imaging

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Abstract

Purpose:

The clinical use of kurtosis imaging is impeded by long acquisitions and postprocessing. Recently, estimation of mean kurtosis tensor Symbol and mean diffusivity (Symbol) was made possible from 13 distinct diffusion weighted MRI acquisitions (the 1-3-9 protocol) with simple postprocessing. 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).

Theory and Methods:

1-3-9/1-9-9 data are acquired in rat and human brains, and estimates of Symbol, FA, Symbol from human brains are compared with traditional estimates from an extensive diffusion kurtosis imaging 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 Symbol and Symbol are comparable to nonlinear least squares estimation, and is improved with the 1-9-9 protocol. The compensation strategy vastly improves parameter estimation in nonideal data.

Conclusion:

The framework offers a robust and compact method for estimating several diffusion metrics. The protocol is easily implemented. Magn Reson Med 76:1455–1468, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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