Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.
United Imaging Research Institute of Intelligent Imaging, Beijing 100101, China.
Molecules. 2021 Jun 25;26(13):3896. doi: 10.3390/molecules26133896.
Magnetic resonance spectroscopy (MRS), as a noninvasive method for molecular structure determination and metabolite detection, has grown into a significant tool in clinical applications. However, the relatively low signal-to-noise ratio (SNR) limits its further development. Although the multichannel coil and repeated sampling are commonly used to alleviate this problem, there is still potential room for promotion. One possible improvement way is combining these two acquisition methods so that the complementary of them can be well utilized. In this paper, a novel coil-combination method, average smoothing singular value decomposition, is proposed to further improve the SNR by introducing repeatedly sampled signals into multichannel coil combination. Specifically, the sensitivity matrix of each sampling was pretreated by whitened singular value decomposition (WSVD), then the smoothing was performed along the repeated samplings' dimension. By comparing with three existing popular methods, Brown, WSVD, and generalized least squares, the proposed method showed better performance in one phantom and 20 in vivo spectra.
磁共振波谱（MRS）作为一种用于分子结构测定和代谢物检测的非侵入性方法，已成为临床应用中的重要工具。然而，相对较低的信噪比（SNR）限制了其进一步发展。尽管多通道线圈和重复采样通常用于缓解这个问题，但仍有进一步提高的潜力。一种可能的改进方法是将这两种采集方法结合起来，以便充分利用它们的互补性。在本文中，提出了一种新的线圈组合方法，平均平滑奇异值分解，通过将重复采样信号引入多通道线圈组合，进一步提高 SNR。具体来说，对每个采样的灵敏度矩阵进行白化奇异值分解（WSVD）预处理，然后沿重复采样的维度进行平滑处理。与三种现有的流行方法（Brown、WSVD 和广义最小二乘法）进行比较，所提出的方法在一个体模和 20 个体内谱中表现出更好的性能。
