XCloud-HyperLRF: Fast Hypercomplex NMR Spectroscopy with Cloud-based Low Rank Hankel Matrix Reconstruction

作者：Di Guo, Jiaying Zhan, Zhangren Tu, Yi Guo, Yirong Zhou, Jianfan Wu, Qing Hong, Vladislav Orekhov, Xiaobo Qu · 发表于：Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 年份：2023 · DOI：10.58530/2022/3176 · 研究领域：Advanced MRI Techniques and Applications、NMR spectroscopy and applications、Atomic and Subatomic Physics Research

Nuclear magnetic resonance (NMR) serves as an indispensable tool in revealing physical, chemical and structural information about molecules. We present a hypercomplex low rank approach to reconstruct hypercomplex NMR spectrum reconstruction. We first introduce an adjoint matrix operation to convert the hypercomplex signal into complex matrix and then propose a low-rank model and algorithm to reconstruct hypercomplex signal. The experiment results demonstrate that the proposed method provides a fast and high-fidelity reconstruction of hypercomplex NMR data. Furthermore, we made the method available at an open-access and easy-to-use cloud computing platform.