Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA.
Magnetic Resonance Methodology group of the University Institute for Diagnostic and Interventional Neuroradiology and the Department of Biomedical Research, University Bern, Bern, Switzerland.
Magn Reson Med. 2022 Jan;87(1):11-32. doi: 10.1002/mrm.28942. Epub 2021 Aug 2.
Fitting of MRS data plays an important role in the quantification of metabolite concentrations. Many different spectral fitting packages are used by the MRS community. A fitting challenge was set up to allow comparison of fitting methods on the basis of performance and robustness.
Synthetic data were generated for 28 datasets. Short-echo time PRESS spectra were simulated using ideal pulses for the common metabolites at mostly near-normal brain concentrations. Macromolecular contributions were also included. Modulations of signal-to-noise ratio (SNR); lineshape type and width; concentrations of γ-aminobutyric acid, glutathione, and macromolecules; and inclusion of artifacts and lipid signals to mimic tumor spectra were included as challenges to be coped with.
Twenty-six submissions were evaluated. Visually, most fit packages performed well with mostly noise-like residuals. However, striking differences in fit performance were found with bias problems also evident for well-known packages. In addition, often error bounds were not appropriately estimated and deduced confidence limits misleading. Soft constraints as used in LCModel were found to substantially influence the fitting results and their dependence on SNR.
Substantial differences were found for accuracy and precision of fit results obtained by the multiple fit packages.
MRS 数据的拟合在代谢物浓度的定量中起着重要作用。MRS 社区使用许多不同的光谱拟合软件包。本拟合挑战旨在根据性能和稳健性比较拟合方法。
为 28 个数据集生成合成数据。使用常见代谢物的理想脉冲模拟短回波时间 PRESS 光谱，这些代谢物的浓度接近正常脑浓度。还包括大分子贡献。调制信号噪声比（SNR）；线谱类型和宽度；γ-氨基丁酸、谷胱甘肽和大分子的浓度；以及包括伪影和脂质信号以模拟肿瘤光谱，这些都是需要应对的挑战。
评估了 26 项提交的结果。从视觉上看，大多数拟合软件包的表现都很好，残差大多像噪声。然而，对于知名软件包也存在明显的拟合性能差异，也存在偏差问题。此外，通常误差界限没有得到适当估计，推断的置信限也存在误导性。在 LCModel 中使用的软约束被发现会显著影响拟合结果及其对 SNR 的依赖性。
多个拟合软件包获得的拟合结果的准确性和精密度存在显著差异。
