SmartOCT: smartphone-integrated optical coherence tomography.

Smartphone devices have seen unprecedented technical innovation in computational power and optical imaging capabilities, making them potentially invaluable tools in scientific imaging applications. The smartphone's compact form-factor and broad accessibility has motivated researchers to develop smartphone-integrated imaging systems for a wide array of applications. Optical coherence tomography (OCT) is one such technique that could benefit from smartphone-integration. Here, we demonstrate smartOCT, a smartphone-integrated OCT system that leverages built-in components of a smartphone for detection, processing and display of OCT data. SmartOCT uses a broadband visible-light source and line-field OCT design that enables snapshot 2D cross-sectional imaging. Furthermore, we describe methods for processing smartphone data acquired in a RAW data format for scientific applications that improves the quality of OCT images. The results presented here demonstrate the potential of smartphone-integrated OCT systems for low-resource environments.

MURIN: Multimodal Retinal Imaging and Navigated-laser-delivery for dynamic and longitudinal tracking of photodamage in murine models.

Laser-induced photodamage is a robust method for investigating retinal pathologies in small animals. However, aiming of the photocoagulation laser is often limited by manual alignment and lacks real-time feedback on lesion location and severity. Here, we demonstrate a multimodality OCT and SLO ophthalmic imaging system with an image-guided scanning laser lesioning module optimized for the murine retina. The proposed system enables targeting of focal and extended area lesions under OCT guidance to benefit visualization of photodamage response and the precision and repeatability of laser lesion models of retinal injury.

DiffuserSpec: spectroscopy with Scotch tape.

Computational spectroscopy breaks the inherent one-to-one spatial-to-spectral pixel mapping of traditional spectrometers by multiplexing spectral data over a given sensor region. Most computational spectrometers require components that are complex to design, fabricate, or both. DiffuserSpec is a simple computational spectrometer that uses the inherent spectral dispersion of commercially available diffusers to generate speckle patterns that are unique to each wavelength. Using Scotch tape as a diffuser, we demonstrate narrowband and broadband spectral reconstructions with 2-nm spectral resolution over an 85-nm bandwidth in the near-infrared, limited only by the bandwidth of the calibration dataset. We also investigate the effect of spatial sub-sampling of the 2D speckle pattern on resolution performance.

A multi-channel smartphone-based spectroscopic system for high-throughput biosensing in low-resource settings.

Primary healthcare centers (PHC) are the first point of contact for people in low-resource settings, and laboratory services play a critical role in early diagnosis of any disease. In recent years, several smartphone-based spectroscopic systems have been demonstrated to translate lab-confined healthcare applications into point-of-care environments to improve their accessibility. Due to constraints, such as the low availability of skilled personnel and consumables in a PHC, batch processing would be ideal for a large number of samples. Therefore, high-throughput and multi-channel detection is equally critical as affordability and portability. To date, most point-of-care systems are designed to perform a single type of analysis at a time. Herein, we introduce a smartphone-based spectroscopic system based on the use of line-beam illumination to achieve high-throughput sensing (15 channels simultaneously) within a 3d-printed microfluidic device. We also developed a smartphone application to process the spectral data and provide the results in real-time. Bland-Altman analysis revealed that the proposed device performs similarly to a laboratory spectrophotometer. The availability of the developed system will enable detection of multiple samples rapidly in low-resource settings with the existing limited manpower and infrastructures. The fast turnaround time may eventually help in timely diagnosis of patients during situations of high sample load, such as during disease outbreaks.

Retinal OCT Denoising with Pseudo-Multimodal Fusion Network.

Optical coherence tomography (OCT) is a prevalent imaging technique for retina. However, it is affected by multiplicative speckle noise that can degrade the visibility of essential anatomical structures, including blood vessels and tissue layers. Although averaging repeated B-scan frames can significantly improve the signal-to-noise-ratio (SNR), this requires longer acquisition time, which can introduce motion artifacts and cause discomfort to patients. In this study, we propose a learning-based method that exploits information from the single-frame noisy B-scan and a pseudo-modality that is created with the aid of the self-fusion method. The pseudo-modality provides good SNR for layers that are barely perceptible in the noisy B-scan but can over-smooth fine features such as small vessels. By using a fusion network, desired features from each modality can be combined, and the weight of their contribution is adjustable. Evaluated by intensity-based and structural metrics, the result shows that our method can effectively suppress the speckle noise and enhance the contrast between retina layers while the overall structure and small blood vessels are preserved. Compared to the single modality network, our method improves the structural similarity with low noise B-scan from 0.559 ± 0.033 to 0.576 ± 0.031.

Self-fusion for OCT noise reduction.

Reducing speckle noise is an important task for improving visual and automated assessment of retinal OCT images. Traditional image/signal processing methods only offer moderate speckle reduction; deep learning methods can be more effective but require substantial training data, which may not be readily available. We present a novel self-fusion method that offers effective speckle reduction comparable to deep learning methods, but without any external training data. We present qualitative and quantitative results in a variety of datasets from fovea and optic nerve head regions, with varying SNR values for input images.

Handheld spectrally encoded coherence tomography and reflectometry for motion-corrected ophthalmic optical coherence tomography and optical coherence tomography angiography.

Optical coherence tomography (OCT) is the gold standard for quantitative ophthalmic imaging. The majority of commercial and research systems require patients to fixate and be imaged in a seated upright position, which limits the ability to perform ophthalmic imaging in bedridden or pediatric patients. Handheld OCT devices overcome this limitation, but image quality often suffers due to a lack of real-time aiming and patient eye and photographer motion. We describe a handheld spectrally encoded coherence tomography and reflectometry (SECTR) system that enables simultaneous en face reflectance and cross-sectional OCT imaging. The handheld probe utilizes a custom double-pass scan lens for fully telecentric OCT scanning with a compact optomechanical design and a rapid-prototyped enclosure to reduce the overall system size and weight. We also introduce a variable velocity scan waveform that allows for simultaneous acquisition of densely sampled OCT angiography (OCTA) volumes and widefield reflectance images, which enables high-resolution vascular imaging with precision motion-tracking for volumetric motion correction and multivolumetric mosaicking. Finally, we demonstrate in vivo human retinal OCT and OCT angiography (OCTA) imaging using handheld SECTR on a healthy volunteer. Clinical translation of handheld SECTR will allow for high-speed, motion-corrected widefield OCT and OCTA imaging in bedridden and pediatric patients who may benefit ophthalmic disease diagnosis and monitoring.

Simultaneous multimodal ophthalmic imaging using swept-source spectrally encoded scanning laser ophthalmoscopy and optical coherence tomography.

Scanning laser ophthalmoscopy (SLO) benefits diagnostic imaging and therapeutic guidance by allowing for high-speed en face imaging of retinal structures. When combined with optical coherence tomography (OCT), SLO enables real-time aiming and retinal tracking and provides complementary information for post-acquisition volumetric co-registration, bulk motion compensation, and averaging. However, multimodality SLO-OCT systems generally require dedicated light sources, scanners, relay optics, detectors, and additional digitization and synchronization electronics, which increase system complexity. Here, we present a multimodal ophthalmic imaging system using swept-source spectrally encoded scanning laser ophthalmoscopy and optical coherence tomography (SS-SESLO-OCT) for in vivo human retinal imaging. SESLO reduces the complexity of en face imaging systems by multiplexing spatial positions as a function of wavelength. SESLO image quality benefited from single-mode illumination and multimode collection through a prototype double-clad fiber coupler, which optimized scattered light throughput and reduce speckle contrast while maintaining lateral resolution. Using a shared 1060 nm swept-source, shared scanner and imaging optics, and a shared dual-channel high-speed digitizer, we acquired inherently co-registered en face retinal images and OCT cross-sections simultaneously at 200 frames-per-second.