用仿真数据训练神经网络，有效去除生物组织中强荧光干扰的拉曼光谱噪声
Simulation-Driven Deep Learning Framework for Raman Spectral Denoising Under Fluorescence-Dominant Conditions
- 基于物理模型生成真实感拉曼数据，训练级联神经网络
- 显著提升荧光主导条件下的拉曼信号信噪比，改善谱图质量
- 适合需要高精度拉曼分析的生物医学研究者使用
拉曼光谱可实现无损、无标记的分子分析，具有高特异性，是生物医学诊断的强大工具。然而，其在生物组织中的应用受限于本征弱拉曼散射和强荧光背景，严重降低信号质量。本文提出一种模拟驱动的去噪框架，结合统计学基础的噪声模型与深度学习，以增强荧光主导条件下获取的拉曼光谱。我们全面建模了主要噪声源，并基于该模型生成了生物上真实的拉曼光谱，用于训练一个级联深度神经网络，旨在联合抑制随机探测器噪声和荧光基线干扰。为评估方法性能，我们以真实实验数据为基础，模拟了人体皮肤光谱作为验证案例。结果表明，物理信息引导的学习能显著提升光谱质量，实现更快、更准确的拉曼组织分析。
原文摘要 · Abstract (English)
Raman spectroscopy enables non-destructive, label-free molecular analysis with high specificity, making it a powerful tool for biomedical diagnostics. However, its application to biological tissues is challenged by inherently weak Raman scattering and strong fluorescence background, which significantly degrade signal quality. In this study, we present a simulation-driven denoising framework that combines a statistically grounded noise model with deep learning to enhance Raman spectra acquired under fluorescence-dominated conditions. We comprehensively modeled major noise sources. Based on this model, we generated biologically realistic Raman spectra and used them to train a cascaded deep neural network designed to jointly suppress stochastic detector noise and fluorescence baseline interference. To evaluate the performance of our approach, we simulated human skin spectra derived from real experimental data as a validation case study. Our results demonstrate the potential of physics-informed learning to improve spectral quality and enable faster, more accurate Raman-based tissue analysis.
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