Wu Jianfan, Xu Runmin, Huang Yihui, Zhan Jiaying, Tu Zhangren, Qu Xiaobo, Guo Di
School of Computer and Information Engineering, Fujian Engineering Research Center for Medical Data Mining and Application, Xiamen University of Technology, Xiamen 361024, China.
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, Xiamen University, Xiamen 361005, China.
J Magn Reson. 2022 Sep;342:107283. doi: 10.1016/j.jmr.2022.107283. Epub 2022 Aug 6.
Nuclear Magnetic Resonance (NMR) spectroscopy is one of the most promising analytical chemistry techniques, although it takes a long time to acquire data. Non-uniform sampling (NUS) is an effective way to reduce the sampling time, but faithful reconstruction methods are needed. The low rank Hankel matrix (LRHM) approach uses the low rank constraint to obtain high-quality spectra from NUS signals, but the reconstruction has a considerable time overhead. In this work, we propose a sliding window based low rank Hankel matrix approach to speed up the spectra reconstruction from NUS signals. Using the sliding window to construct a matrix can effectively reduce the size of the Hankel matrix for faster reconstructions. To further decrease the reconstruction time, parallel computation is applied in the proposed approach. The experiments on both synthetic data and realistic data demonstrate that the reconstruction speed of the proposed method is the fastest among compared methods without sacrificing the quality of spectra.
核磁共振（NMR）光谱法是最有前途的分析化学技术之一，尽管采集数据需要很长时间。非均匀采样（NUS）是减少采样时间的有效方法，但需要可靠的重建方法。低秩汉克尔矩阵（LRHM）方法利用低秩约束从NUS信号中获得高质量光谱，但重建过程存在相当大的时间开销。在这项工作中，我们提出了一种基于滑动窗口的低秩汉克尔矩阵方法，以加速从NUS信号中重建光谱。使用滑动窗口构建矩阵可以有效减小汉克尔矩阵的大小，从而实现更快的重建。为了进一步减少重建时间，在所提出的方法中应用了并行计算。对合成数据和实际数据的实验表明，在所比较的方法中，该方法的重建速度最快，且不牺牲光谱质量。
