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Diffusion-weighted MRS (DWS) of brain metabolites enables the study of cell-specific alterations in tissue microstructure by probing the diffusion of intracellular metabolites. In particular, the diffusion properties of neuronal N-acetylaspartate (NAA), typically co-measured with N-acetylaspartyl glutamate (NAAG) (NAA + NAAG = tNAA), have been shown to be sensitive to intraneuronal/axonal damage in pathologies such as stroke and multiple sclerosis. Lacking, so far, are empirical assessments of the reproducibility of DWS measures across time and subjects, as well as a systematic investigation of the optimal acquisition parameters for DWS experiments, both of which are sorely needed for clinical applications of the method. In this study, we acquired comprehensive single-volume DWS datasets of the human corpus callosum at 3 T and 7 T. We investigated the inter- and intra-subject variability of empirical and modeled diffusion properties of tNAA [D(avg) (tNAA) and D(model) (tNAA), respectively]. Subsequently, we used a jackknife-like resampling approach to explore the variance of these properties in partial data subsets reflecting different total scan durations. The coefficients of variation (C(V)) and repeatability coefficients (C(R)) for D(avg) (tNAA) and D(model) (tNAA) were calculated for both 3 T and 7 T, with overall lower variability in the 7 T results. Although this work is limited to the estimation of the diffusion properties in the corpus callosum, we show that a careful choice of diffusion-weighting conditions at both field strengths allows the accurate measurement of tNAA diffusion properties in clinically relevant experimental time. Based on the resampling results, we suggest optimized acquisition schemes of 13-min duration at 3T and 10-min duration at 7 T, whilst retaining low variability (C(V) ≈ 8%) for the tNAA diffusion measures. Power calculations for the estimation of D(model )(tNAA) and D(avg) (tNAA) based on the suggested schemes show that less than 21 subjects per group are sufficient for the detection of a 10% effect between two groups in case-control studies., (Copyright © 2015 John Wiley & Sons, Ltd.)

We present a method for the robust and accurate estimation of brain metabolite transverse relaxation times (T2 ) from multiple spin-echo data acquired with a single-shot Carr-Purcell-Meiboom-Gill (CPMG) spectroscopic sequence. Each acquired echo consists of a small number of complex time-domain data points. The amplitudes of the spectral components in each echo are calculated by solving a set of linear equations in which previously estimated frequencies and linewidths serve as prior information. These priors are obtained from a short MRS experiment in which a large number of time-domain data points are acquired, and are subsequently estimated using linear prediction with singular value decomposition (LPSVD) processing. We show that this process can be used to accurately and rapidly measure the T2 values for the main singlet resonances in single-volume MRS measurements in the brain. The proposed method can be generalized to any set of MRS experiments comprising repeated measurements of amplitude changes, e.g. as a function of an experimental parameter, such as TE, inversion time or diffusion weighting., (Copyright © 2013 John Wiley & Sons, Ltd.)

One of the most important applications of diffusion tensor image (DTI) is the in-vivo investigation of brain white-matter connectivity. However, DTI has difficulties on the reconstruction of axonal fibers. The data that is used to reconstruct the fibers is affected by partial volume effect, particularly at the border between gray and white matter, and the typical voxel size used in the acquisition of the MR images is larger than the characteristic size of the axonal fiber. The proposed vector criterion tracking method uses selective eigenvector interpolation for fiber tracking. As a result, the method produces faster and more stable trajectories with reducing memory consumption, compared to other current methods.