Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK.
Magn Reson Med. 2021 Jan;85(1):13-29. doi: 10.1002/mrm.28385. Epub 2020 Aug 14.
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.
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 4-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.
Simulated analyses demonstrate metabolite errors result from 2 main sources: bias from an inflexible baseline (underfitting) and increased variance from an overly flexible baseline (overfitting). 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.
ABfit has been shown to perform accurate baseline estimation and is suitable for fully automated routine MRS analysis.
准确的基线建模对于可靠的 MRS 分析和解释至关重要——特别是在短回波时间，增强的代谢物信息与升高的基线干扰相一致。基线平滑度的程度是代谢物估计的关键分析参数，在这项研究中，提出了一种估计其最佳值的新方法。
描述了一种自适应基线拟合算法 (ABfit)，该算法将样条基函数纳入频域分析模型中，并使用惩罚参数来强制基线平滑。在一系列候选分析中，在不同的平滑度惩罚范围内执行分析，作为 4 阶段算法的一部分，并使用赤池信息量准则估计适当的惩罚。ABfit 应用于一组具有不同基线特征的模拟光谱和在 3T 场强下获得的二维 MRSI 实验数据。
模拟分析表明代谢物误差有两个主要来源：来自不灵活基线的偏差（欠拟合）和来自过于灵活基线的方差增加（过拟合）。在理想的平坦基线情况下，ABfit 被证明可以正确估计高度刚性的基线，对于更现实的光谱，可以在偏差和方差之间找到合理的折衷。对实验数据的分析表明，与已知的代谢物比值与灰质和白质组织贡献体积之间的相关性具有良好的一致性。
ABfit 已被证明可以进行准确的基线估计，适用于全自动常规 MRS 分析。
