Computational multi-angle optical coherence tomography using implicit neural representation

Multi-angle optical coherence tomography (OCT) has attracted increasing attention in recent years due to its higher near-isotropic resolution and enhanced penetration depth compared to conventional OCT, making it promising for biomedical applications. However, accurately reconstructing quantitative optical parameters from sparse multi-angle signals while maintaining high resolution remains a challenging task. In this paper, we introduce a novel framework for multi-angle OCT image reconstruction, termed Implicit REpresentation for OCT (IREO), enabling the recovery of structural images and optical parameter distributions of tissue from backscattered signals impacted by refraction distortion and noise. IREO regards optical parameters as a continuous function of spatial position, which is fitted by a simple neural network. Through the imaging model of OCT, the theoretical imaging intensities are calculated from optical parameters output by the network, and then compared with intensity values acquired by OCT. The neural network parameters are optimized to form an implicit representation of the imaged sample. Through extensive evaluation, we confirmed the outstanding reconstruction quality of the proposed method for two- and three-dimensional multi-angle OCT data even with sparsely-acquired data, and verified the accuracy and feasibility of quantitative optical parameter estimation with custom-built multi-angle imaging systems. The multi-angle imaging reconstruction technique introduced here achieves high-resolution, deep-penetration, distortion-corrected, and multi-contrast imaging, enhancing label-free mesoscopic visualization of biological tissues and showing promise for advancing OCT applications in biomedical research and clinical theranostics.