Data-centric physics-inspired deep learning framework for saturation artifact removal in optical coherence tomography.

In Fourier-domain optical coherence tomography (FD-OCT) imaging, saturation of the data acquisition chain leads to loss of information by clipping the spectral interferograms. When reconstructed, affected depth profiles are degraded by bright axial lines or periodical patterns which mask the underlying information and thereby affect clinical utility. In this work, we propose an approach to faithfully recover the distorted information from saturated FD-OCT scans based on physical insights. For this purpose, genesis and appearance of saturation artifacts are analyzed, revealing a wavelength-dependency within individual interferograms. We propose to use this knowledge in a dual manner: Firstly, we present a simulation model that can dynamically generate realistically saturated interferograms from clean counterparts. This physics-based model allows training neural networks for artifact removal solely on synthesized image pairs. Secondly, to make the wavelength-dependency of artifacts directly accessible, we propose a multi-input, single-output (MISO) network framework. In addition to full B-scans, MISO receives images reconstructed from various spectral sub-windows. Our experiments confirm successful generalization of networks trained on simulated training data to real-world artifact removal in ophthalmic anterior segment imaging. This includes reflexes of various origin in diagnostic or intra-surgical imaging, such as different tissue structures or surgical instruments. Furthermore, a comparison of the proposed multi-input network to single-input baselines reveals consistent performance and reliability gains on both swept-source and spectral domain datasets.