Abstract：
BACKGROUND: Symmetrical NMR spectroscopy, such as Total Correlation Spectroscopy (TOCSY) and other homonuclear spectroscopy, displays symmetry in chemical shift but are generally not symmetrical in terms of intensity, which constitutes a pivotal branch of multidimensional NMR spectroscopy and offers a robust tool for elucidating the structures and dynamics of complex samples, particularly in the context of biological macromolecules. Non-Uniform Sampling (NUS) stands as a critical technique for accelerating multidimensional NMR experiments. However, symmetrical NMR spectroscopy inherently presents dynamic peak intensities, where cross peaks tend to be substantially weaker compared to diagonal peaks. Recovering these weaker cross peaks from NUS data poses a significant challenge, often resulting in compromised data quality.
RESULTS: We enhance the reconstruction quality of NUS symmetrical NMR spectroscopy based on the assumption that the asymmetry in intensity is mild. Regarding the sampling schedule, we employ the symmetrical sampling structure integrated with Poisson sampling schedule to enhance the efficiency of data acquisition. In term of the reconstruction algorithm, we propose the new method by incorporating hard and soft symmetrical constraints into our recently developed L1-norm-based Compressed Sensing (CS) method known as Sparse Complex-valued REconstruction Enabled by Newton method (SCREEN). Additionally, we propose a two-step reconstruction strategy that separately addresses diagonal and cross peaks. In this two-step strategy, cross peaks are effectively reconstructed by excluding the stronger diagonal peaks. Extensive experimental results validate the effectiveness of our proposed methodology.
CONCLUSIONS: This method enhances the overall quality of the reconstructed NUS symmetrical NMR spectra, especially in terms of cross peaks, thereby enriching the interpretation of spectral information. Furthermore, it boosts the robustness towards regularization parameters, facilitating a user-friendly experience.
RESULTS: We enhance the reconstruction quality of NUS symmetrical NMR spectroscopy based on the assumption that the asymmetry in intensity is mild. Regarding the sampling schedule, we employ the symmetrical sampling structure integrated with Poisson sampling schedule to enhance the efficiency of data acquisition. In term of the reconstruction algorithm, we propose the new method by incorporating hard and soft symmetrical constraints into our recently developed L1-norm-based Compressed Sensing (CS) method known as Sparse Complex-valued REconstruction Enabled by Newton method (SCREEN). Additionally, we propose a two-step reconstruction strategy that separately addresses diagonal and cross peaks. In this two-step strategy, cross peaks are effectively reconstructed by excluding the stronger diagonal peaks. Extensive experimental results validate the effectiveness of our proposed methodology.
CONCLUSIONS: This method enhances the overall quality of the reconstructed NUS symmetrical NMR spectra, especially in terms of cross peaks, thereby enriching the interpretation of spectral information. Furthermore, it boosts the robustness towards regularization parameters, facilitating a user-friendly experience.
摘要：
背景：对称NMR光谱，如全相关光谱(TOCSY)和其他同核光谱，在化学位移中显示对称，但在强度方面通常不对称，这构成了多维核磁共振波谱的一个关键分支，并提供了一个强大的工具来阐明复杂样品的结构和动力学，特别是在生物大分子的背景下。非均匀采样（NUS）是加速多维NMR实验的关键技术。然而,对称NMR光谱固有地呈现动态峰强度，与对角峰相比,其中交叉峰倾向于显著更弱。从NUS数据中恢复这些较弱的交叉峰构成了重大挑战，经常导致数据质量受损。
结果：我们基于强度不对称性轻度的假设来增强NUS对称NMR光谱的重建质量。关于抽样时间表，我们采用对称采样结构与泊松采样计划集成，以提高数据采集的效率。就重建算法而言，我们通过将硬和软对称约束纳入我们最近开发的基于L1范数的压缩感知（CS）方法提出了新方法，该方法被称为稀疏复值重建牛顿法（SCREEN）。此外,我们提出了一个两步重建策略，分别解决对角和交叉峰。在这两步走的战略中,交叉峰通过排除较强的对角峰有效地重建。大量的实验结果验证了我们提出的方法的有效性。
结论：该方法提高了重建的NUS对称NMR光谱的整体质量，特别是在交叉峰方面，从而丰富了光谱信息的解释。此外，它提高了对正则化参数的鲁棒性，促进用户友好的体验。
结果：我们基于强度不对称性轻度的假设来增强NUS对称NMR光谱的重建质量。关于抽样时间表，我们采用对称采样结构与泊松采样计划集成，以提高数据采集的效率。就重建算法而言，我们通过将硬和软对称约束纳入我们最近开发的基于L1范数的压缩感知（CS）方法提出了新方法，该方法被称为稀疏复值重建牛顿法（SCREEN）。此外,我们提出了一个两步重建策略，分别解决对角和交叉峰。在这两步走的战略中,交叉峰通过排除较强的对角峰有效地重建。大量的实验结果验证了我们提出的方法的有效性。
结论：该方法提高了重建的NUS对称NMR光谱的整体质量，特别是在交叉峰方面，从而丰富了光谱信息的解释。此外，它提高了对正则化参数的鲁棒性，促进用户友好的体验。
