Deep-learning visualization enhancement method for optical coherence tomography angiography in dermatology

Sub-Second Optical Coherence Tomography Angiography Protocol for Intraoral Imaging Using an Efficient Super-Resolution Network

This study introduces a 200 kHz swept-source optical coherence tomography system-based fast optical coherence tomography angiography (OCTA) protocol for intraoral imaging by integrating an efficient Intraoral Micro-Angiography Super-Resolution Transformer (IMAST) model. This protocol reduces acquisition time to ~0.3 s by reducing the spatial sampling resolution, thereby minimizing motion artifacts while maintaining a field of view and image quality. The IMAST model utilizes a transformer-based architecture combined with convolutional operations to reconstruct high-resolution intraoral OCTA images from reduced-resolution scans. Experimental results from various intraoral sites and conditions show the model's robustness and high performance in enhancing image quality compared to existing deep-learning methods. Besides, IMAST shows advantages in model complexity, inference time, and computational cost, underscoring its suitability for clinical environments. These findings support the potential of our approach for noninvasive oral disease diagnosis, reducing patient discomfort and facilitating early detection of malignancies, thus serving as a valuable tool for oral assessment.

VET: Vasculature Extraction Transformer for Single-Scan Optical Coherence Tomography Angiography

Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality for analyzing skin microvasculature, enabling non-invasive diagnosis and treatment monitoring. Traditional OCTA algorithms necessitate at least two-repeated scans to generate microvasculature images, while image quality is highly dependent on the repetitions of scans (e.g., 4-8). Nevertheless, a higher repetition count increases data acquisition time, causing patient discomfort and more unpredictable motion artifacts, which can result in potential misdiagnosis. To address these limitations, we proposed a vasculature extraction pipeline based on the novelty vasculature extraction transformer (VET) to generate OCTA images by using a single OCT scan. Distinct from the vision Transformer, VET utilizes convolutional projection to better learn the spatial relationships between image patches. This study recruited 15 healthy participants. The OCT scans were performed in five various skin sites, i.e., palm, arm, face, neck, and lip. Our results show that in comparison to OCTA images obtained by the speckle variance OCTA (peak-signal-to-noise ratio (PSNR): 16.13) and eigen-decomposition OCTA (PSNR: 17.08) using four repeated OCT scans, OCTA images extracted by the proposed pipeline exhibit a better PSNR (18.03) performance while reducing the data acquisition time by 75%. Visual comparisons show that the proposed pipeline outperformed traditional OCTA algorithms, particularly in the imaging of lip and face areas, where artifacts are commonly encountered. This study is the first to demonstrate that the VET can efficiently extract high-quality vasculature images from a single, rapid OCT scan. This capability significantly enhances diagnostic accuracy for patients and streamlines the imaging process.

Deep-learning segmentation method for optical coherence tomography angiography in ophthalmology

PURPOSE

The optic disc and the macular are two major anatomical structures in the human eye. Optic discs are associated with the optic nerve. Macular mainly involves degeneration and impaired function of the macular region. Reliable optic disc and macular segmentation are necessary for the automated screening of retinal diseases.

METHODS

A swept-source OCTA system was designed to capture OCTA images of human eyes. To address these segmentation tasks, first, we constructed a new Optic Disc and Macula in fundus Image with optical coherence tomography angiography (OCTA) dataset (ODMI). Second, we proposed a Coarse and Fine Attention-Based Network (CFANet).

RESULTS

The five metrics of our methods on ODMI are 98.91 % , 98.47 % , 89.77 % , 98.49 % , and 89.77 % , respectively.

CONCLUSIONS

Experimental results show that our CFANet has achieved good performance on segmentation for the optic disc and macula in OCTA.