横向松弛分布反演算法中的不确定性研究

Shanxue Chen, Ran Li, Jie Yu, Hong-zhi Wang, Xue-long Zhang
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引用次数: 1

摘要

在复杂多相体系的研究中,核磁共振弛豫谱常被用作分子种类、结构和动力学的指纹。奇异值分解(SVD)、非负最小二乘(NNLS)、实体迭代重建技术(SIRT)等反演算法已广泛应用于核磁共振数据分析以获得T1或T2谱。然而,由于这种反演的病态性质,很难确定反演结果的可靠性。本文根据以上三种算法的思想,在MATLAB中实现了具体的模型。通过将核磁共振分析仪采集的一系列真实衰减数据的反演谱与噪声模拟模型进行匹配,收敛到真实分布,并对原始核磁共振数据中噪声的影响进行了评价。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study of Uncertainties in the Inversion Algorithms for Transverse Relaxation Distribution
Nuclear magnetic resonance (NMR) relaxation spectrum is often used as fingerprints of molecular species, structure and dynamics in the study of complex multiphase system. Inversion algorithms such as singular value decomposition (SVD), Non-negative least square (NNLS), Solid iteration rebuild technique (SIRT) have been widely used in analyzing NMR data to obtain a T1 or T2 spectrum. However, due to the ill-conditioned nature of such inversion, it is difficult to determine the reliability of the inversion result. The concrete model is realized in MATLAB according the thought of the above three algorithms in this article. We converged to the true distribution by matching up the inversion spectrum from a series of true decay data collected from the NMR analyst instrument and a noisy simulated model, then evaluated the effects of noise in the original NMR data.
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