用深度学习提升核磁共振谱分辨率，精准定位信号峰位置。
Towards Ultimate NMR Resolution with Deep Learning
- 构建概率谱 $P^3$，为每个频点赋峰值出现概率。
- 在稀疏采样下显著提升谱图质量，对Tau等蛋白有效。
- 支持多谱图联合处理，实现数据间信息互通。
在多维核磁共振波谱中，实际分辨率指在重叠峰、热噪声和谱图伪影背景下区分并精确测定信号位置的能力。为实现极限分辨率，我们提出峰值概率表示法（$P^3$）——一种统计谱表示，为每个谱点分配一个概率，反映该位置存在峰极大值的可能性。通过基于物理启发的深度学习架构MR-Ai，实现谱图到$P^3$的映射，该网络专为多维NMR谱设计。此外，我们证明MR-Ai可实现多谱图协同处理，促进数据集间直接信息交换，显著提升谱图质量，尤其在高度稀疏采样条件下表现优异。MR-Ai与$P^3$的价值在合成数据及Tau、MATL1、钙调蛋白等蛋白质的谱图上得到验证。
原文摘要 · Abstract (English)
In multidimensional NMR spectroscopy, practical resolution is defined as the ability to distinguish and accurately determine signal positions against a background of overlapping peaks, thermal noise, and spectral artifacts. In the pursuit of ultimate resolution, we introduce Peak Probability Presentations ($P^3$)- a statistical spectral representation that assigns a probability to each spectral point, indicating the likelihood of a peak maximum occurring at that location. The mapping between the spectrum and $P^3$ is achieved using MR-Ai, a physics-inspired deep learning neural network architecture, designed to handle multidimensional NMR spectra. Furthermore, we demonstrate that MR-Ai enables coprocessing of multiple spectra, facilitating direct information exchange between datasets. This feature significantly enhances spectral quality, particularly in cases of highly sparse sampling. Performance of MR-Ai and high value of the $P^3$ are demonstrated on the synthetic data and spectra of Tau, MATL1, Calmodulin, and several other proteins.
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