波谱学杂志 ›› 2024, Vol. 41 ›› Issue (2): 191-208.doi: 10.11938/cjmr20233079
收稿日期:
2023-08-29
出版日期:
2024-06-05
在线发表日期:
2023-10-10
通讯作者:
*Tel: +86 18250756791, E-mail: yuyang15@xmu.edu.cn.
基金资助:
YANG Yu*(), CHEN Bo, WU Liubin, LIN Enping, HUANG Yuqing, CHEN Zhong
Received:
2023-08-29
Published:
2024-06-05
Online:
2023-10-10
Contact:
*Tel: +86 18250756791, E-mail: yuyang15@xmu.edu.cn.
摘要:
拉普拉斯核磁共振(Laplace NMR)可以提供待测样品的扩散系数或弛豫时间等物理参数信息,是用于研究分子化学结构、动力学和相互作用的强大工具.Laplace NMR的适用性很大程度上取决于拉普拉斯逆变换相关的信号处理算法的性能.在本文中,我们首先讨论了Laplace NMR谱图重建问题的不适定性,接着回顾了经典的基于正则化约束的重建算法,并介绍了目前前沿的深度学习算法在处理Laplace反演问题方面的应用,最后总结了这些算法的优缺点,并对Laplace NMR信号处理方法未来改进方向进行了展望.
中图分类号:
杨钰, 陈博, 吴柳滨, 林恩平, 黄玉清, 陈忠. Laplace NMR谱图重建——从经典正则化到深度学习[J]. 波谱学杂志, 2024, 41(2): 191-208.
YANG Yu, CHEN Bo, WU Liubin, LIN Enping, HUANG Yuqing, CHEN Zhong. Spectrum Reconstruction for Laplace NMR: From Handcraft Regularization to Deep Learning[J]. Chinese Journal of Magnetic Resonance, 2024, 41(2): 191-208.
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