Spectrally encoded coherence tomography and reflectometry: Simultaneous en face and cross-sectional imaging at 2 gigapixels per second

Hybrid spiral scanning in a double-clad fiber-based handheld confocal scanning light ophthalmoscope

High-speed, accessible, and robust in vivo imaging of the human retina is critical for screening of retinal pathologies, such as diabetic retinopathy, age-related macular degeneration, and others. Scanning light ophthalmoscopy (SLO) is a retinal imaging modality that produces digital, en face images of the human retina with superior image gradability rates when compared to the current standard of care in screening for these diseases, namely the flood-illumination handheld fundus camera (HFC). However, current-generation commercial SLO systems are mostly tabletop devices, limiting their accessibility and utility in screening applications. Moreover, most existing SLO systems use raster scan patterns, which are both inefficient and lead to undesired subject gaze drift when used with visible or pseudo-visible illumination. Non-raster scan patterns, especially spiral scanning as described herein, promise advantages in both scan efficiency and reduced subject eye motion. In this work, we introduce a novel "hybrid spiral" scan pattern and the associated hardware design and real-time image reconstruction techniques necessary for its implementation in an SLO system. Building upon this core hybrid spiral scanning SLO (HSS-SLO) technology, we go on to present a complete handheld HSS-SLO system, featuring a fiber-coupled portable patient interface which leverages a dual-clad fiber (DCF) to form a single-path optical topology, thus ensuring mechanically robust co-alignment of illumination and collection apertures, a necessity for a handheld system. The feasibility of HSS-SLO for handheld, in vivo imaging is demonstrated by imaging eight human volunteers.

Spectrally encoded dual-mode interferometry with orthogonal scanning

Spectrally encoded confocal microscopy (SECM) is a high-speed reflectance confocal microscopy technique. Here, we present a method to integrate optical coherence tomography (OCT) and SECM for complementary imaging by adding orthogonal scanning to the SECM configuration. The co-registration of SECM and OCT is automatic, as all system components are shared in the same order, eliminating the need for additional optical alignment. The proposed multimode imaging system is compact and cost-effective while providing the benefits of imaging aiming and guidance. Furthermore, speckle noise can be suppressed by averaging the speckles generated by shifting the spectral-encoded field in the direction of dispersion. Using a near infrared (NIR) card and a biological sample, we demonstrated the capability of the proposed system by showing SECM imaging at depths of interest guided by the OCT in real time and speckle noise reduction. Interfaced multimodal imaging of SECM and OCT was implemented at a speed of approximately 7 frames/s using fast-switching technology and GPU processing.

Real-time OCT image denoising using a self-fusion neural network

Optical coherence tomography (OCT) has become the gold standard for ophthalmic diagnostic imaging. However, clinical OCT image-quality is highly variable and limited visualization can introduce errors in the quantitative analysis of anatomic and pathologic features-of-interest. Frame-averaging is a standard method for improving image-quality, however, frame-averaging in the presence of bulk-motion can degrade lateral resolution and prolongs total acquisition time. We recently introduced a method called self-fusion, which reduces speckle noise and enhances OCT signal-to-noise ratio (SNR) by using similarity between from adjacent frames and is more robust to motion-artifacts than frame-averaging. However, since self-fusion is based on deformable registration, it is computationally expensive. In this study a convolutional neural network was implemented to offset the computational overhead of self-fusion and perform OCT denoising in real-time. The self-fusion network was pretrained to fuse 3 frames to achieve near video-rate frame-rates. Our results showed a clear gain in peak SNR in the self-fused images over both the raw and frame-averaged OCT B-scans. This approach delivers a fast and robust OCT denoising alternative to frame-averaging without the need for repeated image acquisition. Real-time self-fusion image enhancement will enable improved localization of OCT field-of-view relative to features-of-interest and improved sensitivity for anatomic features of disease.

Automated instrument-tracking for 4D video-rate imaging of ophthalmic surgical maneuvers

Intraoperative image-guidance provides enhanced feedback that facilitates surgical decision-making in a wide variety of medical fields and is especially useful when haptic feedback is limited. In these cases, automated instrument-tracking and localization are essential to guide surgical maneuvers and prevent damage to underlying tissue. However, instrument-tracking is challenging and often confounded by variations in the surgical environment, resulting in a trade-off between accuracy and speed. Ophthalmic microsurgery presents additional challenges due to the nonrigid relationship between instrument motion and instrument deformation inside the eye, image field distortion, image artifacts, and bulk motion due to patient movement and physiological tremor. We present an automated instrument-tracking method by leveraging multimodal imaging and deep-learning to dynamically detect surgical instrument positions and re-center imaging fields for 4D video-rate visualization of ophthalmic surgical maneuvers. We are able to achieve resolution-limited tracking accuracy at varying instrument orientations as well as at extreme instrument speeds and image defocus beyond typical use cases. As proof-of-concept, we perform automated instrument-tracking and 4D imaging of a mock surgical task. Here, we apply our methods for specific applications in ophthalmic microsurgery, but the proposed technologies are broadly applicable for intraoperative image-guidance with high speed and accuracy.

Double-Clad Fiber-Based Multifunctional Biosensors and Multimodal Bioimaging Systems: Technology and Applications

Optical fibers have been used to probe various tissue properties such as temperature, pH, absorption, and scattering. Combining different sensing and imaging modalities within a single fiber allows for increased sensitivity without compromising the compactness of an optical fiber probe. A double-clad fiber (DCF) can sustain concurrent propagation modes (single-mode, through its core, and multimode, through an inner cladding), making DCFs ideally suited for multimodal approaches. This study provides a technological review of how DCFs are used to combine multiple sensing functionalities and imaging modalities. Specifically, we discuss the working principles of DCF-based sensors and relevant instrumentation as well as fiber probe designs and functionalization schemes. Secondly, we review different applications using a DCF-based probe to perform multifunctional sensing and multimodal bioimaging.

Modeling and optimization of galvanometric point-scanning temporal dynamics

Galvanometers are ubiquitous in point-scanning applications in optical imaging, display, ranging, manufacturing, and therapeutic technologies. However, galvanometer performance is constrained by finite response times related to mirror size and material properties. We present a model-driven approach for optimizing galvanometer response characteristics by tuning the parameters of the closed-loop galvanometer controller and demonstrate settling time reduction by over 50%. As an imaging proof-of-concept, we implement scan waveforms that take advantage of the optimized galvanometer frequency response to increase linear field-of-view, signal-to-noise ratio, contrast-to-noise ratio, and speed. The hardware methods presented may be directly implemented on galvanometer controllers without the need for specialized equipment and used in conjunction with customized scan waveforms to further optimize scanning performance.

LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation

Optical coherence tomography (OCT) is a non-invasive imaging technique widely used for ophthalmology. It can be extended to OCT angiography (OCT-A), which reveals the retinal vasculature with improved contrast. Recent deep learning algorithms produced promising vascular segmentation results; however, 3D retinal vessel segmentation remains difficult due to the lack of manually annotated training data. We propose a learning-based method that is only supervised by a self-synthesized modality named local intensity fusion (LIF). LIF is a capillary-enhanced volume computed directly from the input OCT-A. We then construct the local intensity fusion encoder (LIFE) to map a given OCT-A volume and its LIF counterpart to a shared latent space. The latent space of LIFE has the same dimensions as the input data and it contains features common to both modalities. By binarizing this latent space, we obtain a volumetric vessel segmentation. Our method is evaluated in a human fovea OCT-A and three zebrafish OCT-A volumes with manual labels. It yields a Dice score of 0.7736 on human data and 0.8594 ± 0.0275 on zebrafish data, a dramatic improvement over existing unsupervised algorithms.

Real-time corneal segmentation and 3D needle tracking in intrasurgical OCT

Ophthalmic procedures demand precise surgical instrument control in depth, yet standard operating microscopes supply limited depth perception. Current commercial microscope-integrated optical coherence tomography partially meets this need with manually-positioned cross-sectional images that offer qualitative estimates of depth. In this work, we present methods for automatic quantitative depth measurement using real-time, two-surface corneal segmentation and needle tracking in OCT volumes. We then demonstrate these methods for guidance of ex vivo deep anterior lamellar keratoplasty (DALK) needle insertions. Surgeons using the output of these methods improved their ability to reach a target depth, and decreased their incidence of corneal perforations, both with statistical significance. We believe these methods could increase the success rate of DALK and thereby improve patient outcomes.