A data-driven algorithm to determine 1H-MRS basis set composition.

Abstract

Purpose: Metabolite amplitude estimates derived from linear combination modeling of MR spectra depend on the precise list of constituent metabolite basis functions used (the "basis set"). The absence of clear consensus on the "ideal" composition or objective criteria to determine the suitability of a particular basis set contributes to the poor reproducibility of MRS. In this proof-of-concept study, we demonstrate a novel, data-driven approach for deciding the basis-set composition using Akaike information criteria (AIC).

Methods: We have developed an algorithm that iteratively adds metabolites to the basis set using iterative modeling, informed by AIC scores. We investigated two quantitative "stopping conditions," referred to as max-AIC and zero-amplitude, and whether to optimize the selection of basis set on a per-spectrum basis or at the group level. The algorithm was tested using two groups of synthetic in vivo-like spectra representing healthy brain and tumor spectra, respectively, and the derived basis sets (and metabolite amplitude estimates) were compared to the ground truth.

Results: All derived basis sets correctly identified high-concentration metabolites and provided reasonable fits of the spectra. At the single-spectrum level, the two stopping conditions derived the underlying basis set with 84% to 88% accuracy. When optimizing across a group, basis set determination accuracy improved to 89% to 92%.

Conclusion: Data-driven determination of the basis set composition is feasible. With refinement, this approach could provide a valuable data-driven way to derive or refine basis sets, reducing the operator bias of MRS analyses, enhancing the objectivity of quantitative analyses, and increasing the clinical viability of MRS.