Reconstruction of visible light optical coherence tomography images retrieved from discontinuous spectral data using a conditional generative adversarial network

Mid-infrared optical coherence tomography with a stabilized OP-GaP optical parametric oscillator

We demonstrate mid-infrared time-domain optical coherence tomography (OCT) with an orientation-patterned GaP optical parametric oscillator. Instantaneous broadband mid-infrared spectra provide reduced scattering for OCT applications including cultural heritage, quality assurance, and security. B-scan calibrations performed across the wavelength tuning range show depth resolutions of 67 µm at 5.1 µm and 88 µm at 10.5 µm. Volumetric imaging inside a plastic bank card is demonstrated at 5.1 µm, with a 1 Hz A-scan rate that indicates the potential of stable broadband OPO sources to contribute to mid-infrared OCT.

Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network

Optical coherence tomography (OCT) has stimulated a wide range of medical image-based diagnosis and treatment in fields such as cardiology and ophthalmology. Such applications can be further facilitated by deep learning-based super-resolution technology, which improves the capability of resolving morphological structures. However, existing deep learning-based method only focuses on spatial distribution and disregards frequency fidelity in image reconstruction, leading to a frequency bias. To overcome this limitation, we propose a frequency-aware super-resolution framework that integrates three critical frequency-based modules (i.e., frequency transformation, frequency skip connection, and frequency alignment) and frequency-based loss function into a conditional generative adversarial network (cGAN). We conducted a large-scale quantitative study from an existing coronary OCT dataset to demonstrate the superiority of our proposed framework over existing deep learning frameworks. In addition, we confirmed the generalizability of our framework by applying it to fish corneal images and rat retinal images, demonstrating its capability to super-resolve morphological details in eye imaging.

Imaging human macular pigments with visible light optical coherence tomography and superluminescent diodes

We demonstrate superluminescent diodes (SLDs) for visible light optical coherence tomography (OCT) of the human retina. SLDs are less costly than supercontinuum sources and have lower intrinsic excess noise, enabling imaging closer to the shot noise limit. While single SLDs are not broadband, they provide power concentrated at specific wavelengths relevant to retinal function. As a new, to the best of our knowledge, application, we image human macular pigments (MPs), which are thought to both aid vision and protect against advanced age-related macular degeneration. Using the unique depth-resolved capabilities of OCT, we localize MPs in depth to Henle's fibers beneath the foveal pit in the living human retina. Our approach reduces the cost of visible light OCT to nearly that of near-infrared (NIR) OCT while also providing information about clinically relevant MPs which cannot be measured in the NIR.

Computational 3D resolution enhancement for optical coherence tomography with a narrowband visible light source

Phase-preserving spectral estimation optical coherence tomography (SE-OCT) enables combining axial resolution improvement with computational depth of field (DOF) extension. We show that the combination of SE-OCT with interferometric synthetic aperture microscopy (ISAM) and computational adaptive optics (CAO) results in high 3D resolution over a large depth range for an OCT system with a narrow bandwidth visible light super-luminescent diode (SLD). SE-OCT results in up to five times axial resolution improvement from 8 µm to 1.5 µm. The combination with ISAM gives a sub-micron lateral resolution over a 400 µm axial range, which is at least 16 times the conventional depth of field. CAO can be successfully applied after SE and ISAM and removes residual aberrations, resulting in high quality images. The results show that phase-preserving SE-OCT is sufficiently accurate for coherent post-processing, enabling the use of cost-effective SLDs in the visible light range for high spatial resolution OCT.

Multi-Scale Reconstruction of Undersampled Spectral-Spatial OCT Data for Coronary Imaging Using Deep Learning

Coronary artery disease (CAD) is a cardiovascular condition with high morbidity and mortality. Intravascular optical coherence tomography (IVOCT) has been considered as an optimal imagining system for the diagnosis and treatment of CAD. Constrained by Nyquist theorem, dense sampling in IVOCT attains high resolving power to delineate cellular structures/features. There is a trade-off between high spatial resolution and fast scanning rate for coronary imaging. In this paper, we propose a viable spectral-spatial acquisition method that down-scales the sampling process in both spectral and spatial domain while maintaining high quality in image reconstruction. The down-scaling schedule boosts data acquisition speed without any hardware modifications. Additionally, we propose a unified multi-scale reconstruction framework, namely Multiscale-Spectral-Spatial-Magnification Network (MSSMN), to resolve highly down-scaled (compressed) OCT images with flexible magnification factors. We incorporate the proposed methods into Spectral Domain OCT (SD-OCT) imaging of human coronary samples with clinical features such as stent and calcified lesions. Our experimental results demonstrate that spectral-spatial down-scaled data can be better reconstructed than data that are down-scaled solely in either spectral or spatial domain. Moreover, we observe better reconstruction performance using MSSMN than using existing reconstruction methods. Our acquisition method and multi-scale reconstruction framework, in combination, may allow faster SD-OCT inspection with high resolution during coronary intervention.

Corneal imaging with blue-light optical coherence microscopy

Corneal imaging is important for the diagnostic and therapeutic evaluation of many eye diseases. Optical coherence tomography (OCT) is extensively used in ocular imaging due to its non-invasive and high-resolution volumetric imaging characteristics. Optical coherence microscopy (OCM) is a technical variation of OCT that can image the cornea with cellular resolution. Here, we demonstrate a blue-light OCM as a low-cost and easily reproducible system to visualize corneal cellular structures such as epithelial cells, endothelial cells, keratocytes, and collagen bundles within stromal lamellae. Our blue-light OCM system achieved an axial resolution of 12 µm in tissue over a 1.2 mm imaging depth, and a lateral resolution of 1.6 µm over a field of view of 750 µm × 750 µm.

Computational refocusing of Jones matrix polarization-sensitive optical coherence tomography and investigation of defocus-induced polarization artifacts

Here we demonstrate a long-depth-of-focus imaging method using polarization sensitive optical coherence tomography (PS-OCT). This method involves a combination of Fresnel-diffraction-model-based phase sensitive computational refocusing and Jones-matrix based PS-OCT (JM-OCT). JM-OCT measures four complex OCT images corresponding to four polarization channels. These OCT images are computationally refocused as preserving the mutual phase consistency. This method is validated using a static phantom, postmortem zebrafish, and ex vivo porcine muscle samples. All the samples demonstrated successful computationally-refocused birefringence and degree-of-polarization-uniformity (DOPU) images. We found that defocusing induces polarization artifacts, i.e., incorrectly high birefringence values and low DOPU values, which are substantially mitigated by computational refocusing.