In silico GABA+ MEGA‐PRESS: Effects of signal‐to‐noise ratio and linewidth on modeling the 3 ppm GABA+ resonance,NMR in Biomedicine

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In silico GABA+ MEGA‐PRESS: Effects of signal‐to‐noise ratio and linewidth on modeling the 3 ppm GABA+ resonance
NMR in Biomedicine ( IF 2.7 ) Pub Date : 2020-09-28 , DOI: 10.1002/nbm.4410
Helge Jörn Zöllner _{1,

2} , Georg Oeltzschner _{3,

4} , Alfons Schnitzler ₁ , Hans-Jörg Wittsack ₂

Affiliation

To investigate the GABA+ modeling accuracy of MEGA‐PRESS GABA+‐edited MRS data with various spectral quality scenarios, the influence of varying signal‐to‐noise ratio (SNR) and linewidth on the model estimates was quantified. MEGA‐PRESS data from 46 volunteers were averaged to generate a template MEGA‐PRESS spectrum, which was modeled and quantified to generate a GABA+ level ground truth. This spectrum was then manipulated by adding 427 combinations of varying artificial noise levels and line broadening, mimicking variations in GABA+ SNR and B₀ homogeneity. GABA+ modeling and quantification was performed with 100 simulated spectra per condition using automated routines in both Gannet 3.0 and Tarquin. The GABA+ estimation error was calculated as the relative deviation to the quantified GABA+ ground truth levels to assess the accuracy of GABA+ modeling. Finally, the accordance between the simulations and different in vivo scenarios was assessed. The GABA+ estimation error was smaller than 5% for all GABA+ SNR values with creatine linewidths lower than 9.7 Hz in Gannet 3.0 or unequal 10.6 Hz in Tarquin. The standard deviation of the GABA+ amplitude over 100 spectra per condition varied between 3.1 and 17% (Gannet 3.0) and between 1 and 11% (Tarquin) over the in vivo relevant GABA+ SNR range between 2.6 and 3.5. GABA+ edited studies might be realized for voxels with low GABA+ SNR at the cost of higher group‐level variance. The accuracy of GABA+ modeling had no relation to commonly used quality metrics. The Tarquin algorithm was found to be more robust against linewidth changes than the fitting algorithm in Gannet.

中文翻译：

计算机 GABA+ MEGA-PRESS：信噪比和线宽对 3 ppm GABA+ 共振建模的影响

为了研究具有各种光谱质量场景的 MEGA-PRESS GABA+ 编辑的 MRS 数据的 GABA+ 建模精度，量化了不同的信噪比 (SNR) 和线宽对模型估计的影响。对来自 46 名志愿者的 MEGA-PRESS 数据进行平均以生成模板 MEGA-PRESS 谱，对其进行建模和量化以生成 GABA+ 水平的基本事实。然后通过添加 427 种不同人工噪声水平和谱线展宽的组合来操纵该光谱，模拟 GABA+ SNR 和B _0的变化同质性。使用 Gannet 3.0 和 Tarquin 中的自动化程序对每个条件下 100 个模拟光谱进行 GABA+ 建模和量化。GABA+ 估计误差计算为与量化的 GABA+ 真实水平的相对偏差，以评估 GABA+ 建模的准确性。最后，评估了模拟与不同体内场景之间的一致性。对于在 Gannet 3.0 中肌酸线宽低于 9.7 Hz 或在 Tarquin 中不等于 10.6 Hz 的所有 GABA+ SNR 值，GABA+ 估计误差小于 5%。在 2.6 和 3.5 之间的体内相关 GABA+ SNR 范围内，每种条件下 100 个光谱的 GABA+ 幅度的标准偏差在 3.1 和 17% (Gannet 3.0) 和 1% 到 11% (Tarquin) 之间变化。GABA+ 编辑的研究可能会以较高的组级方差为代价实现 GABA+ SNR 较低的体素。GABA+ 建模的准确性与常用的质量指标无关。Tarquin 算法被发现比 Gannet 中的拟合算法更能抵抗线宽变化。

更新日期：2020-09-28

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