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PURPOSE: The clinical use of kurtosis imaging is impeded by long acquisitions and postprocessing. Recently, estimation of mean kurtosis tensor W¯ and mean diffusivity ( D¯) 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 D¯, FA, W¯ 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 D¯ and W¯ 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 000:000-000, 2015. © 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.

Exploratory analysis of functional MRI data aims at revealing known as well as unknown properties in a data-driven manner devoid of hypotheses on the time course of the hemodynamic response. This uncommitted approach usually precedes confirmatory modeling and may point to unexpected results that otherwise would be lost. Common approaches include clustering methods, principal component analysis (PCA) and in particular independent component analysis (ICA). ICA assumes that the measured activity patterns consist of linear combinations of a set of statistically independent source signals. Under favorable circumstances, one of more of these signals describe activation patterns, while others model noise and other nuisance factors. This work introduces a competing method for fMRI analysis known as sparse principal component analysis (SPCA). We argue that SPCA is less committed than ICA and show that similar results, with better suppression of noise, are obtained.