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.

Four-dimensional microscope-integrated optical coherence tomography enables volumetric imaging of tissue structures and tool-tissue interactions in ophthalmic surgery at interactive update rates. This enables surgeons to undertake particular surgical steps under four-dimensional optical coherence tomography (4D OCT) guidance. However, current 4D OCT systems are limited by their field of view and signal quality. Both are attributable to the emphasis on high volume acquisition rates, which is critical for smooth visual perception by the surgeon. Existing 3D volume mosaicing methods are developed in the context of diagnostic imaging and do not take dynamic surgical interactions and real-time processing into account. In this paper, we propose a novel volume mosaicing and visualization methodology that not only aims at leveraging the temporal information to overcome some of the current limitations and imaging artifacts of 4D OCT, but also is aware of the surgical context and dynamic instrument motion implicitly during registration and explicitly for visualization. We propose a rapid 4-degrees of freedom volume registration, integrating an innovative approach for volume mosaicing that takes temporal recency and semantic information into account for enhanced surgical visualization. Our experiments on 4D OCT datasets demonstrate high registration accuracy and illustrate the benefits for visualization by reducing imaging artifacts and dynamically expanding the surgical view.

The injection of therapeutic agents into the sub-retinal space might allow improved treatment of age-related macular degeneration. Various robotic systems have been developed to achieve the required precision and, in combination with intraoperative Optical Coherence Tomography (iOCT) imaging, methods for autonomous robotic guidance have been proposed. In such systems, the robot’s cognition is often governed by machine learning algorithms, such as convolutional neural networks (CNNs), which provide semantic scene information from iOCT images. Although the robot performs a surgical task autonomously, human supervision is critical to monitor the robot’s execution and, if necessary, stop the robot or take control to avoid trauma to the patient. In this paper, we propose a novel visualization concept for improved human supervision of autonomous robotic subretinal injection that integrates uncertainty information of the data provided to the robot. We design a focus and context visualization that renders an automatically identified instrument-aligned B-scan in the context of the 3D OCT volume. Our visualization is enriched by augmenting the uncertainty information on the instrument-aligned B-scan. To dynamically model task-specific uncertainty, we introduce a weighting scheme to assign an importance factor to each pair of classes, controlling the impact of their confusion on the overall uncertainty. We demonstrate our visualization concept on iOCT volumes acquired at different stages during subretinal injection on ex-vivo porcine eyes. We show that our processing pipeline achieves sufficient update rates for surgical display and discuss the impact of our visualization concept on the acceptance of robotic task autonomy for subretinal injection procedures.

Optical Coherence Tomography Angiography (OCTA), a functional extension of OCT, has the potential to replace most invasive fluorescein angiography (FA) exams in ophthalmology. So far, OCTA's field of view is however still lacking behind fluorescence fundus photography techniques. This is problematic, because many retinal diseases manifest at an early stage by changes of the peripheral retinal capillary network. It is therefore desirable to expand OCTA's field of view to match that of ultra-widefield fundus cameras. We present a custom developed clinical high-speed swept-source OCT (SS-OCT) system operating at an acquisition rate 8-16 times faster than today's state-of-the-art commercially available OCTA devices. Its speed allows us to capture ultra-wide fields of view of up to 90 degrees with an unprecedented sampling density and hence extraordinary resolution by merging two single shot scans with 60 degrees in diameter. To further enhance the visual appearance of the angiograms, we developed for the first time a three-dimensional deep learning based algorithm for denoising volumetric OCTA data sets. We showcase its imaging performance and clinical usability by presenting images of patients suffering from diabetic retinopathy.