.png){kind=icon}
返回
HERO: A Memory-Efficient and Fast Structured Low-Rank Reconstruction Algorithm for Accelerated Hypercomplex NMR
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.analchem.5c05054.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Non-uniform sampling is essential in NMR spectroscopy to accelerate data acquisition without compromising spectral information. Among the various NUS reconstruction techniques, such as compressive sensing and maximum entropy, structured low-rank methods are particularly effective in preserving spectral fidelity by exploiting the inherent redundancies in NMR data. However, their application to high-dimensional NMR remains limited due to the high memory and computational cost associated with nuclear norm minimization or matrix factorization methods, both of which rely on large intermediate variables. In this work, we propose a memory-efficient structured low-rank reconstruction algorithm that avoids explicit construction of block Hankel matrices and reduces the dependence on large matrix multiplications through a reformulated Alternating Direction Method of Multipliers scheme. In contrast to existing matrix factorization approaches, our algorithm significantly lowers both computational and memory complexity, achieving four-dimensional (4D) NMR reconstructions with more than a 90% reduction in memory usage while maintaining accuracy. This advancement enables practical and scalable applications of structured low-rank reconstruction to high-dimensional NMR, greatly enhancing the utility of NUS in real-world scenarios. The code is available at https://github.com/EricLin1993/HERO.
Keyword:
HANKEL MATRIX
FACTORIZATION
SPECTROSCOPY
ROBUST
期刊
IF:
6.7
论文数:
4.7W
被引数:
15.9W
机构
引用论文
Non-uniform sampling reconstruction for symmetrical NMR spectroscopy by exploiting inherent symmetry利用固有对称性进行对称NMR波谱学的非均匀采样重建.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏
分享收藏SCREENES: Enhancing non-uniform sampling reconstruction for symmetrical NMR spectroscopySCREENES: 增强对称NMR光谱的非均匀采样重建
PREAI
分享收藏Quantification of CH and NH/π-Stacking Interactions in Cells Using Nuclear Magnetic Resonance SpectroscopyANALYTICAL CHEMISTRY
ANALYTICAL CHEMISTRYIF6.7
PREAI
分享收藏WPR-Net: A Deep Learning Protocol for Highly Accelerated NMR Spectroscopy with Faithful Weak Peak ReconstructionWPR-Net：一种用于高速加速NMR波谱学的深度学习协议，可实现忠实弱峰重建ANALYTICAL CHEMISTRY
ANALYTICAL CHEMISTRYIF6.7
PREAI
分享收藏Lineshapes and artifacts in Multidimensional Fourier Transform of arbitrary sampled NMR data sets
PREAI
分享收藏
分享收藏
查看更多内容