High-Throughput Photonic Time-Stretch OCT Using Frequency-Division Multiplexing and Reservoir Computing

In recent years, there has been an increasing demand for real-time high-resolution optical imaging technology in fields such as biomedicine, material testing, and industrial imaging. Optical coherence tomography (OCT) has become an important tool for microstructure observation due to its micrometer level axial resolution and non-contact, non-invasive imaging capabilities. This thesis systematically investigates a novel PTS-OCT system based on an integrated architecture of Frequency Division Multiplexing (FDM) and Reservoir Computing (RC). The main contributions and innovations of this work are summarized as follows: 1. FDM-based parallel acquisition architecture for improved system throughput: Propose a frequency division multiplexing parallel acquisition architecture to improve system throughput: In response to the low lateral acquisition rate and high imaging redundancy of PTS-OCT, this paper introduces the frequency division multiplexing (FDM) mechanism to achieve simultaneous acquisition of multi-channel interference signals within a single sampling period, significantly improving the overall throughput and lateral resolution of the system. The experimental results show that architecture has good channel isolation and parallel imaging capabilities and can efficiently capture high-speed moving target image information. 2. Fourier-free signal processing model to overcome traditional limitations: To address dispersion-induced chirp and the limitations of Fourier-based algorithms, a Reservoir Computing (RC) framework is adopted. A novel time-domain signal processing scheme is designed, which eliminates the need for Fourier transform. By mapping nonlinear interference signals into high-dimensional temporal representations, this approach enables effective feature extraction and depth reconstruction, avoiding the computational overhead and accumulated errors associated with traditional resampling, interpolation, and polynomial fitting methods.