Photonics
Physics-Guided and Data-Driven Conditional GAN Data Augmentation for Depth-Resolved Spatial Frequency Domain Imaging Optical Property Estimation: A Simulation-Based Comparative Study
Von Ghazala Arshad, Dongyuan Liu und Feng Gao.
Inhalt auf Englisch
Abstract
Background
Spatial frequency domain imaging (SFDI) is a non-contact, wide-field optical imaging technique that quantifies tissue absorption (μₐ) and reduced scattering coefficients (μs′). Conventional SFDI provides depth-averaged 2D maps.
Methods
This study presents a simulation-based framework for depth-resolved optical property estimation over five depth layers, including discrete 3D inclusions. The framework also includes a comparison of two conditional GAN (cGAN) designs for synthetic data augmentation. Monte Carlo simulation generated 1000 heterogeneous tissue samples, with μₐ and μs′ varied independently. Two cGANs with identical architectures were trained. The first was a physics-guided (PG-cGAN), with six physics-based loss terms; the second was a data-driven (DD-cGAN) with no physics losses. Each cGAN generated 5000 synthetic samples. A multi-output U-Net was trained on each synthetic dataset separately and evaluated on the same held-out set of 200 real MCX samples to assess downstream optical property estimation accuracy.