Reproducibility and optimization of in  vivo human diffusion‐weighted MRS of the corpus callosum at 3T and 7T

Abstract

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 3T and 7T. We investigated the inter- and intra-subject variability of empirical and modeled diffusion properties of tNAA [Davg(tNAA) and Dmodel(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 (CV) and repeatability coefficients (CR) for Davg(tNAA) and Dmodel(tNAA) were calculated for both 3T and 7T, with overall lower variability in the 7T 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 7T, whilst retaining low variability (CV ≈ 8%) for the tNAA diffusion measures. Power calculations for the estimation of Dmodel(tNAA) and Davg(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.

Differentiating between axonal damage and demyelination in healthy aging by combining diffusion-tensor imaging and diffusion-weighted spectroscopy in the human corpus callosum at 7 T

“…The high specificity of MRS techniques is provided at the cost of an intrinsic low signal-to-noise ratio (SNR), which is further exacerbated by the addition of diffusion weighting and error propagation in the calculation of diffusion measures. The reproducibility of the model employed here for calculating metabolite diffusion metrics was tested previously in a study investigating the inter-and intra-subject variability of both empirical and modelled diffusion properties of tNAA, and evaluating the optimized protocol to be used in clinically relevant acquisition times in the same region of interest (Wood, et al, 2015). The acquisition scheme employed in this study was carefully evaluated following the guidelines reported in Wood et al 2015.…”

“…The reproducibility of the model employed here for calculating metabolite diffusion metrics was tested previously in a study investigating the inter-and intra-subject variability of both empirical and modelled diffusion properties of tNAA, and evaluating the optimized protocol to be used in clinically relevant acquisition times in the same region of interest (Wood, et al, 2015). The acquisition scheme employed in this study was carefully evaluated following the guidelines reported in Wood et al 2015. Power calculations in the same study showed that, based on the suggested acquisition scheme, at 7T the minimum number of subjects in each group necessary to detect a 10% difference in D(tNAA), ADC(tNAA) and ADC(tCr) between two groups is 13, 12, and 7, respectively.…”

“…Power calculations in the same study showed that, based on the suggested acquisition scheme, at 7T the minimum number of subjects in each group necessary to detect a 10% difference in D(tNAA), ADC(tNAA) and ADC(tCr) between two groups is 13, 12, and 7, respectively. The acquisition scheme employed in the current study is expected to give less variability than the one in (Wood, et al, 2015), as it employs similar number of averages with one more b-value. This emphasizes the accuracy and reproducibility of DW-MRS measurements at 7T.…”

“…The maps of the main eigenvector (e 1 ) were derived and confined to the VOI using MIPAV (Medical Image Processing, Analysis and Visualization) software, JIST (Java Image Science Toolbox) and in-house developed MATLAB® codes (Mathworks Inc., Natick MA, USA) as described in (Wood, et al, 2015). These maps were used in the modeling procedure described below.…”

Robust diffusion tensor imaging by spatiotemporal encoding: Principles and in vivo demonstrations

“…A diffusion tensor can then be extracted on the basis of , where S( b ) and S(0) are signal intensities with and without the diffusion‐sensitizing gradients. When incorporated into MRI measurements these calculations lead to a variety of diffusion tensor imaging (DTI) modalities , of widespread use in preclinical and clinical tractography . Because all DTI modalities aim to represent the effects introduced by random molecular displacements spanning sub‐mm ranges, tissue motions happening on similar length‐scales can harm their performance.…”