发表机构
Politecnico di Milano; University of Florence; University of Leicester; University of Nottingham; The Alan Turing Institute; Butterfly Decisions srl; University of Pisa; ISTI-CNR; Istituto Nazionale di Ricerca Metrologica (INRiM); Istituto di Fotonica e Nanotecnologie（米兰理工大学; 佛罗伦萨大学; 莱斯特大学; 诺丁汉大学; 艾伦·图灵研究所; Butterfly Decisions 有限公司; 比萨大学; 意大利国家研究委员会信息科学与技术研究所; 意大利国家计量研究院; 光子与纳米技术研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对双层生物介质光学特性重建难题，提出机器学习框架替代传统模型，利用蒙特卡洛模拟数据训练，实现更快更准的重建，无需层数先验。
AI 中文摘要
从时间域反射率测量中重建分层生物介质的光学特性（特别是吸收系数和散射系数）的逆问题，对传统解析模型而言仍是一个重大挑战。基于扩散方程的逆求解器往往难以应对结构异质性，在浅层吸收和深层散射方面经常产生较差的精度。在本工作中，我们提出一种机器学习框架作为替代方法，用于重建双层介质的光学特性，并将其效率与精度与基于模型的算法进行基准比较。为克服扩散理论及其逆重建的内在近似，我们利用精确的蒙特卡洛模拟在多个源-探测器距离下生成了一组稳健的正向DTOF合成数据集。随后，在此数据集上训练了一个机器学习流水线，并与最先进的基于模型的重建方法进行验证。除了显著的重建速度提升外，机器学习方法比基于模型的逆求解器实现了更高的精度，并且无需关于所研究几何结构中层数的任何先验信息即可提供参数空间维度的估计。通过在同一介质的多个DTOF曲线上训练该流水线，采用联合多距离重建方法，本工作的未来扩展可预期进一步提高重建精度。
英文摘要
The inverse problem of reconstructing optical properties, specifically absorption and scattering coefficients, in layered biological media from time-domain reflectance measurements remains a significant challenge for traditional analytical models. Inverse solvers based on the diffusion equation often struggle with structural heterogeneity, frequently yielding poor accuracy for superficial absorption and deep-layers scattering. In this work, we propose a machine learning framework as an alternative approach to reconstruct the optical properties of a bilayered medium, benchmarking its efficiency and accuracy against model-based algorithms. To overcome the intrinsic approximations of diffusion theory and inverse reconstruction, we generated a robust synthetic dataset of forward DTOF using exact Monte Carlo simulations at multiple source-detector distances. A machine learning pipeline was then trained on this dataset and validated against state-of-the-art model-based reconstruction methods. Besides the significant reconstruction speed-up, the machine learning approach achieves higher accuracy than model-based inverse solvers, further providing an estimate of the parameter space dimensionality without requiring any a priori information about the number of layers in the investigated geometry. Further enhancements in the reconstruction accuracy can be expected in future extensions of this work, by training the pipeline over multiple DTOF curves from the same medium, in a joint multi-distance reconstruction approach.
