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在 9.4T 下大鼠脑内的体内大分子信号:参数化、样条基线估计和 T1 弛豫时间。

In vivo macromolecule signals in rat brain H-MR spectra at 9.4T: Parametrization, spline baseline estimation, and T relaxation times.

机构信息

CIBM Center for Biomedical Imaging, Switzerland.

Animal Imaging and Technology, EPFL, Lausanne, Switzerland.

出版信息

Magn Reson Med. 2021 Nov;86(5):2384-2401. doi: 10.1002/mrm.28910. Epub 2021 Jul 15.

Abstract

PURPOSE

Reliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short-echo time (TE) H-MR spectra. An experimentally acquired single MM spectrum is commonly used. Higher spectral resolution at ultra-high field (UHF) led to increased interest in using a parametrized MM spectrum together with flexible spline baselines to address unpredicted spectroscopic components. Herein, we aimed to: (1) implement an advanced methodological approach for post-processing, fitting, and parametrization of 9.4T rat brain MM spectra; (2) assess the concomitant impact of the LCModel baseline and MM model (ie, single vs parametrized); and (3) estimate the apparent T relaxation times for seven MM components.

METHODS

A single inversion recovery sequence combined with advanced AMARES prior knowledge was used to eliminate the metabolite residuals, fit, and parametrize 10 MM components directly from 9.4T rat brain in vivo H-MR spectra at different TEs. Monte Carlo simulations were also used to assess the concomitant influence of parametrized MM and DKNTMN parameter in LCModel.

RESULTS

A very stiff baseline (DKNTMN ≥ 1 ppm) in combination with a single MM spectrum led to deviations in metabolite concentrations. For some metabolites the parametrized MM showed deviations from the ground truth for all DKNTMN values. Adding prior knowledge on parametrized MM improved MM and metabolite quantification. The apparent T ranged between 12 and 24 ms for seven MM peaks.

CONCLUSION

Moderate flexibility in the spline baseline was required for reliable quantification of real/experimental spectra based on in vivo and Monte Carlo data. Prior knowledge on parametrized MM improved MM and metabolite quantification.

摘要

目的

可靠地检测和拟合大分子(MM)对于准确定量脑短回波时间(TE)H-MR 谱至关重要。通常使用实验获得的单 MM 谱。在超高场(UHF)下,更高的光谱分辨率导致人们越来越感兴趣地使用参数化 MM 谱以及灵活的样条基线来解决不可预测的光谱成分。在此,我们旨在:(1)实施一种用于后处理、拟合和参数化 9.4T 大鼠脑 MM 谱的先进方法学方法;(2)评估 LCModel 基线和 MM 模型(即单参数与参数化)的伴随影响;(3)估计七个 MM 成分的表观 T 弛豫时间。

方法

使用单反转恢复序列结合先进的 AMARES 先验知识,从不同 TE 的体内 9.4T 大鼠脑 H-MR 谱中直接消除代谢物残差、拟合和参数化 10 个 MM 成分。还使用蒙特卡罗模拟来评估 LCModel 中参数化 MM 和 DKNTMN 参数的伴随影响。

结果

非常刚性的基线(DKNTMN≥1ppm)与单 MM 谱结合使用会导致代谢物浓度出现偏差。对于某些代谢物,对于所有 DKNTMN 值,参数化 MM 显示出与真实值的偏差。添加参数化 MM 的先验知识可以改善 MM 和代谢物的定量。七个 MM 峰的表观 T 范围在 12 到 24ms 之间。

结论

基于体内和蒙特卡罗数据,真实/实验谱的可靠定量需要样条基线具有适度的灵活性。参数化 MM 的先验知识改善了 MM 和代谢物的定量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d8ac/8596437/984226f70941/MRM-86-2384-g005.jpg

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