用物理模型指导深度学习，同时还原OCT组织光学属性与清晰结构图。
End-to-end reconstruction of OCT optical properties and speckle-reduced structural intensity via physics-based learning
- 基于物理模型的端到端网络，联合重建光学参数与去斑结构图。
- 在合成角膜OCT数据上实现噪声下稳健参数恢复与分辨率提升。
- 适合做定量组织分析或想融合物理先验的医学成像研究者。
光学相干断层扫描（OCT）中的逆散射问题旨在恢复结构图像及组织固有光学属性，包括折射率、散射系数和各向异性。由于衰减、斑点噪声以及参数间的强耦合，该问题极具挑战性。我们提出一种正则化的端到端深度学习框架，可联合重建光学参数图与去斑OCT结构强度，用于分层可视化。网络通过蒙特卡洛模拟生成的真实数据训练，嵌入物理驱动的OCT前向模型，从估计参数生成预测信号，提供物理一致的监督以实现参数恢复与伪影抑制。在合成角膜OCT数据集上的实验表明，该方法在噪声环境下具备稳健的光学图恢复能力，分辨率更高，结构保真度增强。该方法实现了多参数定量组织表征，并凸显了物理信息建模与深度学习结合在计算OCT中的优势。
原文摘要 · Abstract (English)
Inverse scattering in optical coherence tomography (OCT) seeks to recover both structural images and intrinsic tissue optical properties, including refractive index, scattering coefficient, and anisotropy. This inverse problem is challenging due to attenuation, speckle noise, and strong coupling among parameters. We propose a regularized end-to-end deep learning framework that jointly reconstructs optical parameter maps and speckle-reduced OCT structural intensity for layer visualization. Trained with Monte Carlo-simulated ground truth, our network incorporates a physics-based OCT forward model that generates predicted signals from the estimated parameters, providing physics-consistent supervision for parameter recovery and artifact suppression. Experiments on the synthetic corneal OCT dataset demonstrate robust optical map recovery under noise, improved resolution, and enhanced structural fidelity. This approach enables quantitative multi-parameter tissue characterization and highlights the benefit of combining physics-informed modeling with deep learning for computational OCT.
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