PT - JOURNAL ARTICLE AU - Martin Wilson TI - Adaptive Baseline Fitting for <sup>1</sup>H MR Spectroscopy Analysis AID - 10.1101/2020.02.17.949495 DP - 2020 Jan 01 TA - bioRxiv PG - 2020.02.17.949495 4099 - http://biorxiv.org/content/early/2020/05/27/2020.02.17.949495.short 4100 - http://biorxiv.org/content/early/2020/05/27/2020.02.17.949495.full AB - Purpose Accurate baseline modeling is essential for reliable MRS analysis and interpretation — particularly at short echo-times, where enhanced metabolite information coincides with elevated baseline interference. The degree of baseline smoothness is a key analysis parameter for metabolite estimation, and in this study a new method is presented to estimate its optimal value.Methods An adaptive baseline fitting algorithm (ABfit) is described, incorporating a spline basis into a frequency-domain analysis model, with a penalty parameter to enforce baseline smoothness. A series of candidate analyses are performed over a range of smoothness penalties, as part of a four stage algorithm, and the Akaike information criterion is used to estimate the appropriate penalty. ABfit is applied to a set of simulated spectra with differing baseline features and experimentally acquired 2D MRSI — both at a field strength of 3 Tesla.Results Simulated analyses demonstrate metabolite errors result from two main sources: bias from an inflexible baseline (underfitting) and increased variance from an overly flexible baseline (over-fitting). In the case of an ideal flat baseline ABfit is shown to correctly estimate a highly rigid baseline, and for more realistic spectra a reasonable compromise between bias and variance is found. Analysis of experimentally acquired data demonstrates good agreement with known correlations between metabolite ratios and the contributing volumes of gray and white matter tissue.Conclusion ABfit has been shown to perform accurate baseline estimation and is suitable for fully-automated routine MRS analysis.Competing Interest StatementThe authors have declared no competing interest.