High-resolution diffusion kurtosis imaging at 3T enabled by advanced post-processing

An empirical equation for the magnetization transfer (MT) FLASH signal is derived by analogy to dual-excitation FLASH, introducing a novel semiquantitative parameter for MT, the percentage saturation imposed by one MT pulse during TR. This parameter is obtained by a linear transformation of the inverse signal, using two reference experiments of proton density and T(1) weighting. The influence of sequence parameters on the MT saturation was studied. An 8.5-min protocol for brain imaging at 3 T was based on nonselective sagittal 3D-FLASH at 1.25 mm isotropic resolution using partial acquisition techniques (TR/TE/alpha = 25ms/4.9ms/5 degrees or 11ms/4.9ms/15 degrees for the T(1) reference). A 12.8 ms Gaussian MT pulse was applied 2.2 kHz off-resonance with 540 degrees flip angle. The MT saturation maps showed an excellent contrast in the brain due to clearly separated distributions for white and gray matter and cerebrospinal fluid. Within the limits of the approximation (excitation <15 degrees , TR/T(1) less sign 1) the MT term depends mainly on TR, the energy and offset of the MT pulse, but hardly on excitation and T(1) relaxation. It is inherently compensated for inhomogeneities of receive and transmit RF fields. The MT saturation appeared to be a sensitive parameter to depict MS lesions and alterations of normal-appearing white matter. (c) 2008 Wiley-Liss, Inc.

This article presents two related advancements to the diffusional kurtosis imaging estimation framework to increase its robustness to noise, motion, and imaging artifacts. The first advancement substantially improves the estimation of diffusion and kurtosis tensors parameterizing the diffusional kurtosis imaging model. Rather than utilizing conventional unconstrained least squares methods, the tensor estimation problem is formulated as linearly constrained linear least squares, where the constraints ensure physically and/or biologically plausible tensor estimates. The exact solution to the constrained problem is found via convex quadratic programming methods or, alternatively, an approximate solution is determined through a fast heuristic algorithm. The computationally more demanding quadratic programming-based method is more flexible, allowing for an arbitrary number of diffusion weightings and different gradient sets for each diffusion weighting. The heuristic algorithm is suitable for real-time settings such as on clinical scanners, where run time is crucial. The advantage offered by the proposed constrained algorithms is demonstrated using in vivo human brain images. The proposed constrained methods allow for shorter scan times and/or higher spatial resolution for a given fidelity of the diffusional kurtosis imaging parametric maps. The second advancement increases the efficiency and accuracy of the estimation of mean and radial kurtoses by applying exact closed-form formulae. Copyright © 2010 Wiley-Liss, Inc.

To assess the diagnostic accuracy of diffusion kurtosis magnetic resonance imaging parameters in grading gliomas. The institutional review board approved this prospective study, and informed consent was obtained from all patients. Diffusion parameters-mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis, and radial and axial kurtosis-were compared in the solid parts of 17 high-grade gliomas and 11 low-grade gliomas (P<.05 significance level, Mann-Whitney-Wilcoxon test, Bonferroni correction). MD, FA, mean kurtosis, radial kurtosis, and axial kurtosis in solid tumors were also normalized to the corresponding values in contralateral normal-appearing white matter (NAWM) and the contralateral posterior limb of the internal capsule (PLIC) after age correction and were compared among tumor grades. Mean, radial, and axial kurtosis were significantly higher in high-grade gliomas than in low-grade gliomas (P = .02, P = .015, and P = .01, respectively). FA and MD did not significantly differ between glioma grades. All values, except for axial kurtosis, that were normalized to the values in the contralateral NAWM were significantly different between high-grade and low-grade gliomas (mean kurtosis, P = .02; radial kurtosis, P = .03; FA, P = .025; and MD, P = .03). When values were normalized to those in the contralateral PLIC, none of the considered parameters showed significant differences between high-grade and low-grade gliomas. The highest sensitivity and specificity for discriminating between high-grade and low-grade gliomas were found for mean kurtosis (71% and 82%, respectively) and mean kurtosis normalized to the value in the contralateral NAWM (100% and 73%, respectively). Optimal thresholds for mean kurtosis and mean kurtosis normalized to the value in the contralateral NAWM for differentiating high-grade from low-grade gliomas were 0.52 and 0.51, respectively. There were significant differences in kurtosis parameters between high-grade and low-grade gliomas; hence, better separation was achieved with these parameters than with conventional diffusion imaging parameters.