Potential clinical impact of multiparametric quantitative MR spectroscopy in neurological disorders: A review and analysis

Abstract

Purpose: Unlike conventional MR spectroscopy (MRS), which only measures metabolite concentrations, multiparametric MRS also quantifies their longitudinal (T₁ ) and transverse (T₂ ) relaxation times, as well as the radiofrequency transmitter inhomogeneity (B₁₊ ). To test whether knowledge of these additional parameters can improve the clinical utility of brain MRS, we compare the conventional and multiparametric approaches in terms of expected classification accuracy in differentiating controls from patients with neurological disorders.

Theory and methods: A literature review was conducted to compile metabolic concentrations and relaxation times in a wide range of neuropathologies and regions of interest. Simulations were performed to construct receiver operating characteristic curves and compute the associated areas (area under the curve) to examine the sensitivity and specificity of MRS for detecting each pathology in each region. Classification accuracy was assessed using metabolite concentrations corrected using population-averages for T₁ , T₂ , and B₁₊ (conventional MRS); using metabolite concentrations corrected using per-subject values (multiparametric MRS); and using an optimal linear multiparametric estimator comprised of the metabolites' concentrations and relaxation constants (multiparametric MRS). Additional simulations were conducted to find the minimal intra-subject precision needed for each parameter.

Results: Compared with conventional MRS, multiparametric approaches yielded area under the curve improvements for almost all neuropathologies and regions of interest. The median area under the curve increased by 0.14 over the entire dataset, and by 0.24 over the 10 instances with the largest individual increases.

Conclusions: Multiparametric MRS can substantially improve the clinical utility of MRS in diagnosing and assessing brain pathology, motivating the design and use of novel multiparametric sequences.

Keywords: MRS; multiparametric MRI; qMRI; quantitative MRI; relaxation times; single-voxel spectroscopy.

Fig 1.

The benefits of multiparametric MRS. Left: median AUCs, taken over all pathologies and regions (Table 2), for conventional spectroscopy (C1), multiparametric MRS concentrations (C2) and optimal linear classifiers (C3), as well as the effect of knowing B₁₊ (V1), ultra-long TR (V2) and ultra-short TE (V3). Error bars represent median absolute deviations (MADs). Right: Median±MAD AUCs using the ten cases from Table 2 which show the most marked improvement to AUC (compared between (C1) and (C3)).

Fig. 2.

The effect of precision (intra-subject variability) on multiparametric MRS. The AUC is plotted as a function of the multiparametric MRS’s method coefficient of variation (CV) for each of the variables: T₁, T₂ and B₁₊, for both cases (C2) (using only the concentrations corrected using per-subject metabolite concentrations) and (C3) (using a full multiparametric classifier). Dashed lines correspond to (C2) while solid lines correspond to (C3). Note that, for multiparametric classification, only errors in the concentrations and relaxation parameters (but not B₁₊) are modeled.

Fig. 3.

Visual interpretation of the AUC. Shown are two normal distributions of unit standard deviation, which represent some quantity which varies between patients and controls. As the distance between the distributions increases, so do the AUC and Cohen’s d, both variables used to describe effect size.