A comparison of denoising methods in dynamic MRS using pseudo-synthetic data
A comparison of denoising methods in dynamic MRS using pseudo-synthetic data
复制标题
使用伪合成数据的动态 MRS 去噪方法的比较
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
A. P. Lin
中科院分区：
文献类型：
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作者：
B. Rowland;L. Sreepada;A. P. Lin
目的：动态系统的磁共振波谱受到低信噪比的限制。沿着一系列采集的光谱进行去噪，利用它们的时间相关性来提高单个光谱的质量，并减少拟合代谢物峰时的误差。在本研究中，我们比较了几种去噪方法的性能。方法：考虑了六种不同的去噪方法：SIFT（傅里叶阈值频谱改进）、HSVD（汉克尔奇异值分解）、样条、小波、滑动窗口和滑动高斯。构建伪合成数据来模拟锻炼肌肉时的 31 磷光谱。对于每种方法，使用蒙特卡罗方法确定信噪比为 2、5、10 和 20 的最佳调整参数。然后使用 AMARES 算法拟合每种方法的去噪数据，并将结果与伪合成地面实况进行比较。结果：与未经处理的噪声数据相比，所有六种方法在拟合精度和与真实情况的一致性方面都有所提高。效率最低的方法 SIFT 和 HSVD 可将 RMS 误差降低约 10-20%，而最有效的样条方法可将 RMS 误差降低 70%。对于较低 SNR 数据，去噪的改进通常更大。结论：动态磁共振波谱数据的间接时域去噪可以显着改善后续代谢物拟合。基于样条的去噪被认为是最灵活、最有效的技术。
Purpose: MR spectroscopy of dynamic systems is limited by low signal to noise. Denoising along a series of acquired spec- tra exploits their temporal correlation to improve the quality of individual spectra, and reduce errors in fitting metabolite peaks. In this study we compare the performance of several denoising methods. Methods: Six different denoising methods were con- sidered: SIFT (Spectral Improvement by Fourier Threshold- ing), HSVD (Hankel Singular Value Decomposition), spline, wavelet, sliding window and sliding Gaussian. Pseudo-synthetic data was constructed to mimic 31Phosphorus spectra from exercising muscle. For each method the optimal tuning pa- rameters were determined for SNRs of 2, 5, 10 and 20 using a Monte Carlo approach. Denoised data from each method was then fitted using the AMARES algorithm and the results compared to the pseudo-synthetic ground truth. Results: All six methods produced improvements in both fitting accuracy and agreement with the ground truth, com- pared to unprocessed noisy data. The least effective meth- ods, SIFT and HSVD, achieved around 10-20% reduction in RMS error, while the most effective, Spline, reduced RMS er- ror by 70%. The improvement from denoising was typically greater for lower SNR data. Conclusions: Indirect time domain denoising of dynamic MR spectroscopy data can substantially improve subsequent metabolite fitting. Spline-based denoising was found to be the most flexible and effective technique.