Proton magnetic resonance spectroscopy (1H-MRS) offers a growing variety of methods for querying potential diagnostic biomarkers of multiple sclerosis in living central nervous system tissue. For the past three decades, 1H-MRS has enabled the acquisition of a rich dataset suggestive of numerous metabolic alterations in lesions, normal-appearing white matter, gray matter, and spinal cord of individuals with multiple sclerosis, but this body of information is not free of seeming internal contradiction. The use of 1H-MRS signals as diagnostic biomarkers depends on reproducible and generalizable sensitivity and specificity to disease state that can be confounded by a multitude of influences, including experiment group classification and demographics; acquisition sequence; spectral quality and quantifiability; the contribution of macromolecules and lipids to the spectroscopic baseline; spectral quantification pipeline; voxel tissue and lesion composition; T1 and T2 relaxation; B1 field characteristics; and other features of study design, spectral acquisition and processing, and metabolite quantification about which the experimenter may possess imperfect or incomplete information. The direct comparison of 1H-MRS data from individuals with and without multiple sclerosis poses a special challenge in this regard, as several lines of evidence suggest that experimental cohorts may differ significantly in some of these parameters. We review the existing findings of in vivo 1H-MRS on central nervous system metabolic abnormalities in multiple sclerosis and its subtypes within the context of study design, spectral acquisition and processing, and metabolite quantification and offer an outlook on technical considerations, including the growing use of machine learning, by future investigations into diagnostic biomarkers of multiple sclerosis measurable by 1H-MRS.

The semi-adiabatic localization by adiabatic selective refocusing (sLASER) sequence provides single-shot full intensity signal with clean localization and minimal chemical shift displacement error and was recommended by the international MRS Consensus Group as the preferred localization sequence at high-and ultra-high fields. Acrossvendor standardization of the sLASER sequence at 3 tesla has been challenging due to the B 1 requirements of the adiabatic inversion pulses and maximum B 1 limitations on some platforms. The aims of this study were to design a short-echo sLASER sequence that can be executed within a B 1 limit of 15 μT by taking advantage of gradientmodulated RF pulses, to implement it on three major platforms and to evaluate the between-vendor reproducibility of its perfomance with phantoms and in vivo. In addition, voxel-based first and second order B 0 shimming and voxel-based B 1 adjustments of RF pulses were implemented on all platforms. Amongst the gradient-modulated pulses considered (GOIA, FOCI and BASSI), GOIA-WURST was identified as the optimal refocusing pulse that provides good voxel selection within a maximum B 1 of 15 μT based on localization efficiency, contamination error and ripple artifacts of the Dinesh K Deelchand and Adam Berrington share equal authorship

The aim of this study was to develop a novel software platform for the simulation of magnetic resonance spin systems, capable of simulating a large number of spatial points (128 3 ) for large in vivo spin systems (up to seven coupled spins) in a time frame of the order of a few minutes. The quantum mechanical density-matrix formalism is applied, a coherence pathway filter is utilized for handling unwanted coherence pathways, and the 1D projection method, which provides a substantial reduction in computation time for a large number of spatial points, is extended to include sequences of an arbitrary number of RF pulses. The novel software package, written in MATLAB, computes a basis set of 23 different metabolites (including the two anomers of glucose, seven coupled spins) with 128 3 spatial points in 26 min for a three-pulse experiment on a personal desktop computer. The simulated spectra are experimentally verified with data from both phantom and in vivo MEGA-sLASER experiments. Recommendations are provided regarding the various assumptions made when computing a basis set for in vivo MRS with respect to the number of spatial points simulated and the consideration of relaxation.

Purpose To develop an algorithm which can robustly eliminate all unwanted coherence pathways for an arbitrary magnetic resonance spectroscopy experiment through the adjustment of the relative amplitudes of crusher gradients, thereby reducing the effects of spurious echoes and mislocalization. Theory and Methods The effect of crushing gradients for all coherence pathways was modeled according to the associated physics, and a cost function was optimized which maximally crushes all unwanted coherence pathways, while unaffecting the desired coherence pathway(s). The efficacy of the method was tested versus literature schemes from 2 separate MR spectroscopy (MRS) sequences: sLASER and MEGA‐sLASER with both phantom and in vivo experiments. Results Improved crushing power for 2 separate MRS sequences was demonstrated for 2 crushing schemes adopted from the literature in both phantom and in vivo data. Conclusion A novel algorithm and associated software was developed based on rigorous treatment of all coherence pathways, which can be applied to any arbitrary magnetic resonance spectroscopic pulse sequence of interest and any arbitrary coupled spin system. This developed method solves a long‐standing problem in in vivo MRS and is expected to critically improve the data quality of virtually all MRS applications.

Due to inherent time constraints for in vivo experiments, it is infeasible to repeat multiple MRS scans to estimate standard deviations on the desired measured parameters. As such, the Cramér-Rao lower bounds (CRLBs) have become the routine method to approximate standard deviations for in vivo experiments. Cramér-Rao lower bounds, however, as the name suggests, are theoretically a lower bound on the standard deviation and it is not clear if and under what circumstances this approximation is valid. Realistic synthetic 3 T spectra were used to investigate the relationship between estimated CRLBs, true CRLBs and standard deviations. Here we demonstrate that, although the CRLBs are theoretically truly a lower bound on the standard deviation (not an equality) for the problem typically encountered in quantification, they are still an adequate approximation to standard deviation as long as the model perfectly characterizes the data. In the case when the macromolecule basis deviates from the measured macromolecules it was shown that the CRLBs can deviate from standard deviations by approximately 50% for N-acetylaspartic acid, creatine and glutamate and of the order of 100% or more for myo-inositol and γ-aminobutyric acid.In the case when the model perfectly reflects the data the CRLBs are within approximately 10% of standard deviations for all metabolites. The result of the CRLB being within 10% of standard deviations means that, for an accurate model, novel quantification methods such as machine learning or deep learning will not be able to obtain substantially more precise estimates for the desired parameters than traditional maximum-likelihood estimation.