Luo Yao, Chen Wenhan, Su Zhenhua, Shi Xiaoqi, Luo Jie, Qu Xiaobo, Chen Zhong, Lin Yanqin
Department of Electronic Science, Xiamen University, Xiamen, China.
Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China.
Nat Commun. 2025 Mar 8;16(1):2342. doi: 10.1038/s41467-025-57721-w.
High-quality nuclear magnetic resonance (NMR) spectra can be rapidly acquired by combining non-uniform sampling techniques (NUS) with reconstruction algorithms. However, current deep learning (DL) based reconstruction methods focus only on single-domain reconstruction (time or frequency domain), leading to drawbacks like peak loss and artifact peaks and ultimately failing to achieve optimal performance. Moreover, the lack of fully sampled spectra makes it difficult, even impossible, to determine the quality of reconstructed spectra, presenting challenges in the practical applications of NUS. In this study, a joint time-frequency domain deep learning network, referred to as JTF-Net, is proposed. It effectively combines time domain and frequency domain features, exhibiting better reconstruction performance on protein spectra across various dimensions compared to traditional algorithms and single-domain DL methods. In addition, the reference-free quality assessment metric, denoted as REconstruction QUalIty assuRancE Ratio (REQUIRER), is proposed base on an established quality space in the field of NMR spectral reconstruction. The metric is capable of evaluating the quality of reconstructed NMR spectra without the fully sampled spectra, making it more suitable for practical applications.
通过将非均匀采样技术（NUS）与重建算法相结合，可以快速获取高质量的核磁共振（NMR）光谱。然而，当前基于深度学习（DL）的重建方法仅专注于单域重建（时域或频域），导致出现峰损失和伪峰等缺点，最终无法实现最佳性能。此外，缺乏全采样光谱使得难以甚至无法确定重建光谱的质量，这给NUS的实际应用带来了挑战。在本研究中，提出了一种联合时域-频域深度学习网络，称为JTF-Net。它有效地结合了时域和频域特征，与传统算法和单域DL方法相比，在各个维度的蛋白质光谱上表现出更好的重建性能。此外，基于NMR光谱重建领域已建立的质量空间，提出了一种无参考质量评估指标，称为重建质量保证率（REQUIRER）。该指标能够在没有全采样光谱的情况下评估重建NMR光谱的质量，使其更适合实际应用。
