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首页> 外文期刊>Magma: Magnetic resonance materials in physics, biology, and medicine >Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a Fitting Tool for Arrays of Interrelated Datasets
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Two-dimensional linear-combination model fitting of magnetic resonance spectra to define the macromolecule baseline using FiTAID, a Fitting Tool for Arrays of Interrelated Datasets

机译：磁共振谱的二维线性组合模型拟合，使用FiTAID（一种用于关联数据集数组的拟合工具）来定义大分子基线

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摘要

To propose the determination of the macromolecular baseline (MMBL) in clinical 1H MR spectra based on T_1 and T_2 differentiation using 2D fitting in FiTAID, a general Fitting Tool for Arrays of Interrelated Datasets. Materials and methods: Series of localized inversion-recovery (IR) and 2DJ separation spectra of the brain were recorded at 3T. The MMBL was determined by three 2D evaluation methods based on (1) IR spectra only, (2) 2DJ spectra only, (3) both IR and 2DJ spectra (2DJ-IR). Their performance was compared using synthetic spectra and based on variability and reproducibility as obtained in vivo from 12 subjects in 20 examinations. Results: All methods performed well for synthetic data. In vivo, 2DJ-only yielded larger variations than the other methods. IR-only and 2DJ-IR yielded similar performance. FiTAID is illustrated with further applications where linear-combination model fitting of interrelated arrays of spectra is advantageous. Conclusion: 2D-Fitting offers the possibility to determine the MMBL based on a range of complementary experimental spectra not relying on smoothness criteria or global assumptions on T_1. Since 2DJ-IR includes information from spectra with different inversion and echo times, it is expected to be more robust in cases with more variable data quality and overlap with lipid resonances.
机译：拟议在FiTAID中使用2D拟合，基于T_1和T_2差异，确定临床1H MR光谱中大分子基线（MMBL），这是一种用于相关数据集数组的通用拟合工具。材料和方法：在3T处记录一系列的大脑局部反转恢复（IR）和2DJ分离谱。 MMBL是通过以下三种2D评估方法确定的：（1）仅IR光谱，（2）仅2DJ光谱，（3）IR和2DJ光谱（2DJ-IR）。使用合成光谱并基于在20个检查中从12位受试者体内获得的变异性和可重复性，比较了它们的性能。结果：对于合成数据，所有方法均表现良好。在体内，仅2DJ产生的变异大于其他方法。仅IR和2DJ-IR产生了相似的性能。在进一步的应用中说明了FiTAID，其中光谱的相关阵列的线性组合模型拟合是有利的。结论：二维拟合提供了基于一系列补充实验光谱来确定MMBL的可能性，而这些光谱不依赖于T_1的平滑度标准或全局假设。由于2DJ-IR包含来自具有不同反演和回波时间的光谱的信息，因此在数据质量可变性更高且与脂质共振重叠的情况下，它有望变得更加强大。

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