Division of Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Max-Planck-Institute for Nuclear Physics, Heidelberg, Germany.
NMR Biomed. 2019 Nov;32(11):e4133. doi: 10.1002/nbm.4133. Epub 2019 Jul 30.
High image signal-to-noise ratio (SNR) is required to reliably detect the inherently small chemical exchange saturation transfer (CEST) effects in vivo. In this study, it was demonstrated that identifying spectral redundancies of CEST data by principal component analysis (PCA) in combination with an appropriate data-driven extraction of relevant information can be used for an effective and robust denoising of CEST spectra. The relationship between the number of relevant principal components and SNR was studied on fitted in vivo Z-spectra with artificially introduced noise. Three different data-driven criteria to automatically determine the optimal number of necessary components were investigated. In addition, these criteria facilitate straightforward assessment of data quality that could provide guidance for CEST MR protocols in terms of SNR. Insights were applied to achieve a robust denoising of highly sampled low power Z-spectra of the human brain at 3 and 7 T. The median criterion provided the best estimation for the optimal number of components consistently for all three investigated artificial noise levels. Application of the denoising technique to in vivo data revealed a considerable increase in image quality for the amide and rNOE contrast with a considerable SNR gain. At 7 T the denoising capability was quantified to be comparable or even superior to an averaging of six measurements. The proposed denoising algorithm enables an efficient and robust denoising of CEST data by combining PCA with appropriate data-driven truncation criteria. With this generally applicable technique at hand, small CEST effects can be reliably detected without the need for repeated measurements.
高图像信噪比（SNR）对于可靠地检测体内固有的小化学交换饱和转移（CEST）效应是必需的。在这项研究中，已经证明通过主成分分析（PCA）识别 CEST 数据的谱冗余，结合适当的数据驱动的相关信息提取，可以有效地对 CEST 谱进行稳健的去噪。研究了通过拟合人工引入噪声的体内 Z 谱，确定相关主成分数量与 SNR 之间的关系。研究了三种不同的数据驱动标准来自动确定所需成分的最佳数量。此外，这些标准还便于直接评估数据质量，从而为 CEST MR 协议提供 SNR 方面的指导。该方法应用于在 3T 和 7T 下对人脑进行高采样低功率 Z 谱的稳健去噪。中位数标准始终为所有三个研究的人工噪声水平提供了最佳成分数量的最佳估计。将去噪技术应用于体内数据，揭示了酰胺和 rNOE 对比度的图像质量有了显著提高，同时 SNR 也有了显著提高。在 7T 下，去噪能力被量化为与平均六个测量值相当，甚至更优。所提出的去噪算法通过将 PCA 与适当的数据驱动截断标准相结合，实现了 CEST 数据的高效稳健去噪。有了这种通用技术，就可以可靠地检测到小的 CEST 效应，而无需进行重复测量。
