NEAR FIELD IMAGING

主成分分析(PCA)是一种强有力的分析方法 核磁共振波谱数据集的定量分析。它有一个优势 独立于模型，使其非常适合于分析 具有复杂或未知线形的光谱。上一首 主成分分析的应用要求数据集中的所有光谱都是 同相，或已实施迭代方法来分析频谱 并不是完全分阶段进行的。但是，不正确的阶段化或 迭代方法的不完全收敛导致了 用主成分分析估计峰面积的系统误差。一个 本文提出了一种改进的主成分分析方法，该方法利用复数 奇异值分解(SVD)用于分析光谱数据集 光谱相位的任何数量的变化。新的方法被证明是 对光谱相位完全不敏感。在有噪音的情况下， 基于复奇异值分解的主成分分析方法具有较低的估计误差 峰面积约为传统主成分分析的2倍。 仿真数据验证了该方法的有效性。 人体骨骼肌的活体31P波谱。

Principal-component analysis (PCA) is a powerful method for quantitative analysis of NMR spectral data sets. It has the advantage of being model-independent, making it well suited for the analysis of spectra with complicated or unknown line shapes. Previous applications of PCA have required that all spectra in a data set be in-phase, or have implemented iterative methods to analyze spectra which are not perfectly phased. However, improper phasing or imperfect convergence of the iterative methods have resulted in systematic errors in the estimation of peak areas with PCA. A modified method of PCA is presented here which utilizes complex singular value decomposition (SVD) to analyze spectral data sets with any amount of variation in spectral phase. The new method is shown to completely insensitive to spectral phase. In the presence of noise, PCA with complex SVD yields a lower variation in the estimation of peak area than conventional PCA by a factor of approximately 2. The performance of the method is demonstrated with simulated data and in-vivo 31P spectra from human skeletal muscle.