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首页> 外文期刊>Medical Physics >SU‐F‐I‐23: 1H Magnetic Resonance Spectroscopy Baseline Correction WithSingular Value Decomposition Method
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SU‐F‐I‐23: 1H Magnetic Resonance Spectroscopy Baseline Correction WithSingular Value Decomposition Method

机译:SU-F-I-23:1H磁共振光谱基线校正具有静脉分解方法

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摘要

Purpose: Baseline distortions in NMR spectra are caused by the corruption of the first few data points in Free Induction Decay (FID) or originated from macromolecules and lipids. To study the Singular Value Decomposition (SVD) based baseline correction method. Methods: In‐house Singular Value Decomposition (SVD) program was developed with Matlab version 7.6 (Mathworks.com). Hankel SVD method (singular value decomposition of the acquired FID signal arranged in a Hankel matrix) is used to compute the signal poles and amplitude, and from them the signal frequencies and damping factors. The estimated FID signals are constructed from the quantification parameters; residue error spectrum was calculated by FFT of the difference of original FID and estimated FID. In order to study the baseline correction effect, known simulated FID with added noise to mimic known metabolites concentration was analyzed with SVD, quantification results were compared with known reference; then baseline distortion was introduced by removing the first 5 data points, after baseline corrected with SVD method, the quantification results were compared with original reference without baseline distortion.Known patient FID acquired with Siemens Verio 3T scanner was introduced baseline distortion by removing the first 3 data points, after baseline corrected with SVD method, corrected FID were compared with original results without baseline distortion. Results: Quantification results for simulated data were compared with reference, all within 10% deviation with SNR 20.For FID acquired with Siemens Verio 3T scanner, distorted FID and calculated baseline with SVD method was compared, the baseline matches the distortion well. Corrected FID were used for quantification, the quantification results match the corrected FID well. Conclusion: As demonstrated, SVD based baseline correction method can be used to correct the FID data point missing type of distortion, results are satisfactory for both simulated data and acquired patient data.
机译:目的:NMR光谱中的基线扭曲是由自由诱导衰变(FID)中的前几个数据点的损坏引起的,或来自大分子和脂质。研究基于奇异值分解(SVD)基线校正方法。方法:采用MATLAB版本7.6(MathWorks.com)开发了内部奇异值分解(SVD)程序。 Hankel SVD方法(在Hankel矩阵中排列的所获取的FID信号的奇异值分解)用于计算信号磁极和幅度,以及信号频率和阻尼因子。估计的FID信号由定量参数构建;残留误差谱通过FFT的原始FID和估计FID的差异计算。为了研究基线校正效果,用SVD分析具有添加到模拟的噪声的已知模拟FID,以SVD分析了SVD,将定量结果与已知的参考进行比较;然后通过使用SVD方法校正的基线校正的前5个数据点来引入基线失真,将定量结果与原始参考进行比较,而无基线失真。通过删除前3,使用Siemens Verio 3T扫描仪获得的已知患者FID被引入基线失真使用SVD方法校正基线后的数据点,将校正的FID与原始结果进行比较,无基线失真。结果:将模拟数据的定量结果与参考值相比,所有与SNR 20的偏差范围内。与SIEMENS Verio 3T扫描仪获得的FID相比,比较了与SVD方法的扭曲FID和计算的基线,基线良好地匹配失真。校正的FID用于量化,定量结果与校正的FID阱匹配。结论:如所示,基于SVD基线校正方法可用于校正FID数据点缺失类型的失真,结果是模拟数据和获取的患者数据的令人满意。

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