用物理引导的专家模型提升散射介质中荧光激光雷达的深度与寿命检测精度
EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media
- 引入物理约束的专家网络，分别建模不同时间成分
- 深度估计NRMSE达0.030，荧光寿命估计NRMSE为0.074
- 适合医学成像、生物检测等需高精度深度感知的场景
荧光激光雷达（FLiDAR）是一种用于医疗、汽车等领域距离与深度估算的激光探测技术，在散射介质中面临显著计算挑战。由于获取信号复杂，尤其在该环境下，分离与目标深度相关的光子飞行时间及内在荧光寿命极为困难，制约了现有分析与计算方法的有效性。为此，我们提出一种面向多种时间成分专门建模的物理引导混合专家（Physics-Guided Mixture-of-Experts, MoE）框架。与传统MoE不同，本框架中的专家模型基于底层物理规律，如描述光子在散射介质中传播的辐射传输方程。核心是证据混合专家（EvidenceMoE），其集成基于证据的狄利克雷评判器（EDCs），通过提供各专家输出的质量评分与修正反馈，评估专家可靠性。决策网络据此动态融合专家预测，生成鲁棒最终估计。我们在基于组织中光子传输模型生成的真实模拟荧光激光雷达数据上验证该方法，实现非侵入式癌细胞深度检测。框架表现优异，深度估计的归一化均方根误差（NRMSE）为0.030，荧光寿命估计为0.074。
原文摘要 · Abstract (English)
Fluorescence LiDAR (FLiDAR), a Light Detection and Ranging (LiDAR) technology employed for distance and depth estimation across medical, automotive, and other fields, encounters significant computational challenges in scattering media. The complex nature of the acquired FLiDAR signal, particularly in such environments, makes isolating photon time-of-flight (related to target depth) and intrinsic fluorescence lifetime exceptionally difficult, thus limiting the effectiveness of current analytical and computational methodologies. To overcome this limitation, we present a Physics-Guided Mixture-of-Experts (MoE) framework tailored for specialized modeling of diverse temporal components. In contrast to the conventional MoE approaches our expert models are informed by underlying physics, such as the radiative transport equation governing photon propagation in scattering media. Central to our approach is EvidenceMoE, which integrates Evidence-Based Dirichlet Critics (EDCs). These critic models assess the reliability of each expert's output by providing per-expert quality scores and corrective feedback. A Decider Network then leverages this information to fuse expert predictions into a robust final estimate adaptively. We validate our method using realistically simulated Fluorescence LiDAR (FLiDAR) data for non-invasive cancer cell depth detection generated from photon transport models in tissue. Our framework demonstrates strong performance, achieving a normalized root mean squared error (NRMSE) of 0.030 for depth estimation and 0.074 for fluorescence lifetime.
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