Highly phase-stable 200 kHz swept-source optical coherence tomography based on KTN electro-optic deflector

OCTSharp: an open-source and real-time OCT imaging software based on C

Optical coherence tomography (OCT) demands massive data processing and real-time displaying during high-speed imaging. Current OCT imaging software is predominantly based on C++, aiming to maximize performance through low-level hardware management. However, the steep learning curve of C++ hinders agile prototyping, particularly for research purposes. Moreover, manual memory management poses challenges for novice developers and may lead to potential security issues. To address these limitations, OCTSharp is developed as an open-source OCT software based on the memory-safe language C#. Within the managed C# environment, OCTSharp offers synchronized hardware control, minimal memory management, and GPU-based parallel processing. The software has been thoroughly tested and proven capable of supporting real-time image acquisition, processing, and visualization with spectral-domain OCT systems equipped with the latest advanced hardware. With these enhancements, OCTSharp is positioned to serve as an open-source platform tailored for various applications.

Deep learning empowered highly compressive SS-OCT via learnable spectral-spatial sub-sampling

With the rapid advances of light source technology, the A-line imaging rate of swept-source optical coherence tomography (SS-OCT) has experienced a great increase in the past three decades. The bandwidths of data acquisition, data transfer, and data storage, which can easily reach several hundred megabytes per second, have now been considered major bottlenecks for modern SS-OCT system design. To address these issues, various compression schemes have been previously proposed. However, most of the current methods focus on enhancing the capability of the reconstruction algorithm and can only provide a data compression ratio (DCR) up to 4 without impairing the image quality. In this Letter, we proposed a novel design paradigm, in which the sub-sampling pattern for interferogram acquisition is jointly optimized with the reconstruction algorithm in an end-to-end manner. To validate the idea, we retrospectively apply the proposed method on an ex vivo human coronary optical coherence tomography (OCT) dataset. The proposed method could reach a maximum DCR of ∼62.5 with peak signal-to-noise ratio (PSNR) of 24.2 dB, while a DCR of ∼27.78 could yield a visually pleasant image with a PSNR of ∼24.6 dB. We believe the proposed system could be a viable remedy for the ever-growing data issue in SS-OCT.

Tissue-Specific Optical Mapping Models of Swine Atria Informed by Optical Coherence Tomography

Computational models and experimental optical mapping of cardiac electrophysiology serve as powerful tools to investigate the underlying mechanisms of arrhythmias. Modeling can also aid the interpretation of optical mapping signals, which may have different characteristics with respect to the underlying electrophysiological signals they represent. However, despite the prevalence of atrial arrhythmias such as atrial fibrillation, models of optical electrical mapping incorporating realistic structure of the atria are lacking. Therefore, we developed image-based models of atrial tissue using structural information extracted from optical coherence tomography (OCT), which can provide volumetric tissue characteristics in high resolution. OCT volumetric data of four swine atrial tissue samples were used to develop models incorporating tissue geometry, tissue-specific myofiber orientation, and ablation lesion regions. We demonstrated the use of these models through electrophysiology and photon scattering simulations. Changes in transmural electrical conduction were observed with the inclusion of OCT-derived, depth-resolved fiber orientation. Additionally, the amplitude of optical mapping signals were not found to correspond with lesion transmurality because of lesion geometry and electrical propagation occurring beyond excitation light penetration. This work established a framework for the development of tissue-specific models of atrial tissue derived from OCT imaging data, which can be useful in future investigations of electrophysiology and optical mapping signals with respect to realistic atrial tissue structure.

Compressed sensing-enabled phase-sensitive swept-source optical coherence tomography

Here we present a novel phase-sensitive swept-source optical coherence tomography (PhS-SS-OCT) system. The simultaneously recorded calibration signal, which is commonly used in SS-OCT to stabilize the phase, is randomly sub-sampled during the acquisition, and it is later reconstructed based on the Compressed Sensing (CS) theory. We first mathematically investigated the method, and verified it through computer simulations. We then conducted a vibrational frequency test and a flow velocity measurement in phantoms to demonstrate the system's capability of handling phase-sensitive tasks. The proposed scheme shows excellent phase stability with greatly discounted data bandwidth compared with conventional procedures. We further showcased the usefulness of the system in biological samples by detecting the blood flow in ex vivo swine left marginal artery. The proposed system is compatible with most of the existing SS-OCT systems and could be a preferred solution for future high-speed phase-sensitive applications.

Embedded deep learning in ophthalmology: making ophthalmic imaging smarter

Deep learning has recently gained high interest in ophthalmology due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. In this work, we will review the existing and future directions for 'active acquisition'-embedded deep learning, leading to as high-quality images with little intervention by the human operator. In clinical practice, the improved image quality should translate into more robust deep learning-based clinical diagnostics. Embedded deep learning will be enabled by the constantly improving hardware performance with low cost. We will briefly review possible computation methods in larger clinical systems. Briefly, they can be included in a three-layer framework composed of edge, fog, and cloud layers, the former being performed at a device level. Improved egde-layer performance via 'active acquisition' serves as an automatic data curation operator translating to better quality data in electronic health records, as well as on the cloud layer, for improved deep learning-based clinical data mining.

Feasibility of correlation mapping optical coherence tomography angiographic technique using a 200 kHz vertical-cavity surface-emitting laser source for in vivo microcirculation imaging applications

Optical coherence tomography (OCT) angiography is a well-established in vivo imaging technique to assess the overall vascular morphology of tissues and is an emerging field of research for the assessment of blood flow dynamics and functional parameters such as oxygen saturation. In this study, we present a modified scanning-based correlation mapping OCT using a 200 kHz high-speed swept-source OCT system operating at 1300 nm and demonstrate its wide field-imaging capability in ocular angiographic studies.

High-power and high-speed wavelength-swept amplified spontaneous emission source

In this work, we report the development of an external-cavity wavelength-swept amplified spontaneous emission (ASE) source with high output power and high tuning speed based on an efficient electro-optic effect of beam deflection. The wavelength-swept ASE source is capable of delivering stable output power with averaged intensity of 100 mW in a wide spectrum tuning range of over 80 nm around the wavelength of 1550 nm. The light source will have important applications in optical communications, biomedical imaging, spectral analysis and sensing.

Frequency-constrained robust principal component analysis: a sparse representation approach to segmentation of dynamic features in optical coherence tomography imaging

Sparse representation theory is an exciting area of research with recent applications in medical imaging and detection, segmentation, and quantitative analysis of biological processes. We present a variant on the robust-principal component analysis (RPCA) algorithm, called frequency constrained RPCA (FC-RPCA), for selectively segmenting dynamic phenomena that exhibit spectra within a user-defined range of frequencies. The algorithm lacks subjective parameter tuning and demonstrates robust segmentation in datasets containing multiple motion sources and high amplitude noise. When tested on 17 ex-vivo, time lapse optical coherence tomography (OCT) B-scans of human ciliated epithelium, segmentation accuracies ranged between 91-99% and consistently out-performed traditional RPCA.