In:
Magnetic Resonance in Medicine, Wiley, Vol. 76, No. 5 ( 2016-11), p. 1455-1468
Abstract:
The clinical use of kurtosis imaging is impeded by long acquisitions and postprocessing. Recently, estimation of mean kurtosis tensor and mean diffusivity ( ) 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 , FA, 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 and 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.
Type of Medium:
Online Resource
ISSN:
0740-3194
,
1522-2594
Language:
English
Publisher:
Wiley
Publication Date:
2016
detail.hit.zdb_id:
605774-3
detail.hit.zdb_id:
1493786-4
