Suppr超能文献

基于化学位移编码磁共振成像的甘油三酯成分定量脂肪光谱建模

Fat spectral modeling on triglyceride composition quantification using chemical shift encoded magnetic resonance imaging.

作者信息

Simchick Gregory, Yin Amelia, Yin Hang, Zhao Qun

机构信息

Physics and Astronomy, University of Georgia, Athens, GA, United States; Bio-Imaging Research Center, University of Georgia, Athens, GA, United States.

Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States; Center for Molecular Medicine, University of Georgia, Athens, GA, United States.

出版信息

Magn Reson Imaging. 2018 Oct;52:84-93. doi: 10.1016/j.mri.2018.06.012. Epub 2018 Jun 19.

Abstract

PURPOSE

To explore, at a high field strength of 7T, the performance of various fat spectral models on the quantification of triglyceride composition and proton density fat fraction (PDFF) using chemical-shift encoded MRI (CSE-MRI).

METHODS

MR data was acquired from CSE-MRI experiments for various fatty materials, including oil and butter samples and in vivo brown and white adipose mouse tissues. Triglyceride composition and PDFF were estimated using various a priori 6- or 9-peak fat spectral models. To serve as references, NMR spectroscopy experiments were conducted to obtain material specific fat spectral models and triglyceride composition estimates for the same fatty materials. Results obtained using the spectroscopy derived material specific models were compared to results obtained using various published fat spectral models.

RESULTS

Using a 6-peak fat spectral model to quantify triglyceride composition may lead to large biases at high field strengths. When using a 9-peak model, triglyceride composition estimations vary greatly depending on the relative amplitudes of the chosen a priori spectral model, while PDFF estimations show small variations across spectral models. Material specific spectroscopy derived spectral models produce estimations that better correlate with NMR spectroscopy estimations in comparison to those obtained using non-material specific models.

CONCLUSION

At a high field strength of 7T, a material specific 9-peak fat spectral model, opposed to a widely accepted or generic human liver model, is necessary to accurately quantify triglyceride composition when using CSE-MRI estimation methods that assume the spectral model to be known as a priori information. CSE-MRI allows for the quantification of the spatial distribution of triglyceride composition for certain in vivo applications. Additionally, PDFF quantification is shown to be independent of the chosen a priori spectral model, which agrees with previously reported results obtained at lower field strengths (e.g. 3T).

摘要

目的

在7T高场强下,利用化学位移编码磁共振成像(CSE-MRI)探索各种脂肪光谱模型在甘油三酯成分定量和质子密度脂肪分数(PDFF)测定方面的性能。

方法

通过CSE-MRI实验采集了各种脂肪物质的磁共振数据,包括油和黄油样本以及小鼠体内棕色和白色脂肪组织。使用各种先验的6峰或9峰脂肪光谱模型估算甘油三酯成分和PDFF。为作为参考,进行了核磁共振波谱实验,以获得相同脂肪物质的材料特定脂肪光谱模型和甘油三酯成分估计值。将使用光谱衍生的材料特定模型获得的结果与使用各种已发表的脂肪光谱模型获得的结果进行比较。

结果

使用6峰脂肪光谱模型定量甘油三酯成分在高场强下可能导致较大偏差。使用9峰模型时,甘油三酯成分估计值因所选先验光谱模型的相对幅度而有很大差异,而PDFF估计值在不同光谱模型之间变化较小。与使用非材料特定模型获得的结果相比,材料特定光谱衍生的光谱模型产生的估计值与核磁共振波谱估计值的相关性更好。

结论

在7T高场强下,当使用将光谱模型作为先验信息的CSE-MRI估计方法时,与广泛接受的通用人体肝脏模型相比,需要一个材料特定的9峰脂肪光谱模型来准确量化甘油三酯成分。CSE-MRI允许在某些体内应用中定量甘油三酯成分的空间分布。此外,PDFF定量显示与所选先验光谱模型无关,这与先前在较低场强(如3T)下获得的结果一致。

相似文献

本文引用的文献

9
Quantification of liver fat in the presence of iron overload.铁过载情况下肝脏脂肪的定量分析。
J Magn Reson Imaging. 2017 Feb;45(2):428-439. doi: 10.1002/jmri.25382. Epub 2016 Jul 13.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验