Model selection criteria for data-driven determination of 1H-MRS basis-set composition.

Motivation: The composition of metabolite basis sets impacts their estimates, but no consensus or objective methods exist to decide which compounds should be included or excluded for a particular dataset. Goal(s): To develop an objective, data-driven procedure for determining basis-set composition. Approach: An iterated fitting algorithm uses information criteria scores to select the most appropriate metabolite basis functions directly from the data. We tested two “stopping conditions” using in-vivo-like simulated spectra. Results: The algorithm correctly, consistently identified large parts of the ground-truth set. Stopping conditions set reliable bounds on the basis-set composition. Refinement for low-concentration compounds is expected to further improve accuracy. Impact: Model selection for data-driven assessment of basis set composition has the potential to provide objective criteria and remove operator bias of linear-combination modeling. This may reduce analytic variability and help establish practices for low-concentration and pathology-specific metabolites.