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弥散加权波谱:一种在短回波时间 1H-MRS 中确定大分子共振的新方法。

Diffusion-weighted spectroscopy: a novel approach to determine macromolecule resonances in short-echo time 1H-MRS.

机构信息

Laboratory of Functional and Metabolic Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.

出版信息

Magn Reson Med. 2010 Oct;64(4):939-46. doi: 10.1002/mrm.22490.

DOI:10.1002/mrm.22490
PMID:20564591
Abstract

Quantification of short-echo time proton magnetic resonance spectroscopy results in >18 metabolite concentrations (neurochemical profile). Their quantification accuracy depends on the assessment of the contribution of macromolecule (MM) resonances, previously experimentally achieved by exploiting the several fold difference in T(1). To minimize effects of heterogeneities in metabolites T(1), the aim of the study was to assess MM signal contributions by combining inversion recovery (IR) and diffusion-weighted proton spectroscopy at high-magnetic field (14.1 T) and short echo time (= 8 msec) in the rat brain. IR combined with diffusion weighting experiments (with δ/Δ = 1.5/200 msec and b-value = 11.8 msec/μm(2)) showed that the metabolite nulled spectrum (inversion time = 740 msec) was affected by residuals attributed to creatine, inositol, taurine, choline, N-acetylaspartate as well as glutamine and glutamate. While the metabolite residuals were significantly attenuated by 50%, the MM signals were almost not affected (< 8%). The combination of metabolite-nulled IR spectra with diffusion weighting allows a specific characterization of MM resonances with minimal metabolite signal contributions and is expected to lead to a more precise quantification of the neurochemical profile.

摘要

短回波时间质子磁共振波谱分析可定量检测>18 种代谢物浓度(神经化学特征)。其定量准确性取决于大分子(MM)共振贡献的评估,此前通过利用 T1 的数倍差异来实现。为了最小化代谢物 T1 异质性的影响,本研究旨在评估 MM 信号贡献,方法是在大鼠脑内高磁场(14.1 T)和短回波时间(=8 msec)下,将反转恢复(IR)和扩散加权质子波谱相结合。IR 与扩散加权实验相结合(δ/Δ=1.5/200 msec,b 值=11.8 msec/μm2)表明,代谢物零谱(反转时间=740 msec)受到肌酸、肌醇、牛磺酸、胆碱、N-乙酰天冬氨酸以及谷氨酰胺和谷氨酸残基的影响。尽管代谢物残基的信号显著衰减了 50%,但 MM 信号几乎不受影响(<8%)。代谢物零化 IR 光谱与扩散加权相结合,可特异性地对 MM 共振进行特征描述,最小化代谢物信号贡献,并有望更精确地定量神经化学特征。

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