Development of an intraoral handheld optical coherence tomography-based angiography probe for multi-site oral imaging

Critical review of OCT in clinical practice for the assessment of oral lesions

Background

Optical Coherence Tomography (OCT) is an advanced imaging technique that is widely used in ophthalmology and is increasingly being applied in other fields of medicine. In oral oncology, OCT offers high-resolution, non-invasive (uses non-ionizing light), label-free, real-time imaging, providing detailed insights into tissue microanatomy and cellular structures, thus having the potential to improve early detection, monitoring and cost-effective screening of high-risk populations. However, significant challenges remain in applying OCT to OSCC and OPMDs, particularly in clinical practice.

Methods

A comprehensive search of PUBMED, SCOPUS, and Web of Science databases was performed up to October 2024. Additional manual searches were conducted by screening article bibliographies. Inclusion criteria encompassed studies published in English involving human subjects and evaluating the role of OCT in OSCC and OPMD assessment, OCT utilization for margin resection, and artificial intelligence (AI)-assisted interpretation of OCT images. After removal of duplicates and screening of titles and abstracts, full-text analysis was conducted on eligible studies.

Results

The technique has been investigated for its accuracy in identifying malignant changes in tissues before surgery and/or evaluating resection margins during surgery. Although early studies, primarily in animal models, have been extended to humans and have demonstrated the potential of OCT to accurately assess resection margins and identify precancerous lesions, significant limitations persist. The high cost of OCT equipment reduces its accessibility, availability and widespread use as a common investigation methodology in non-experimental settings. In addition, there is significant heterogeneity in the methodologies used to interpret OCT data, which is strictly operator dependent and may affect standardization and reproducibility of results. This is further complicated by the introduction and increased trend to adopt artificial intelligence (AI) algorithms in imaging evaluation. Machine learning and deep learning algorithms have shown superior diagnostic sensitivity and accuracy compared to clinician judgment. However, especially when used to assess resection margins, these algorithms may be significantly affected by sample extension and preparation, which remains a barrier to the routine clinical application of OCT systems.

Conclusion

Addressing the advantages and challenges of this emerging technique may help focus future research on standardizing application protocols and enhancing AI-assisted analysis to improve diagnostic performance and facilitate clinical translation.

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.

Semi-supervised assisted multi-task learning for oral optical coherence tomography image segmentation and denoising

Optical coherence tomography (OCT) is promising to become an essential imaging tool for non-invasive oral mucosal tissue assessment, but it faces challenges like speckle noise and motion artifacts. In addition, it is difficult to distinguish different layers of oral mucosal tissues from gray level OCT images due to the similarity of optical properties between different layers. We introduce the Efficient Segmentation-Denoising Model (ESDM), a multi-task deep learning framework designed to enhance OCT imaging by reducing scan time from ∼8s to ∼2s and improving oral epithelium layer segmentation. ESDM integrates the local feature extraction capabilities of the convolution layer and the long-term information processing advantages of the transformer, achieving better denoising and segmentation performance compared to existing models. Our evaluation shows that ESDM outperforms state-of-the-art models with a PSNR of 26.272, SSIM of 0.737, mDice of 0.972, and mIoU of 0.948. Ablation studies confirm the effectiveness of our design, such as the feature fusion methods, which enhance performance with minimal model complexity increase. ESDM also presents high accuracy in quantifying oral epithelium thickness, achieving mean absolute errors as low as 5 µm compared to manual measurements. This research shows that ESDM can notably improve OCT imaging and reduce the cost of accurate oral epithermal segmentation, improving diagnostic capabilities in clinical settings.

Quantitative assessment of the oral microvasculature using optical coherence tomography angiography

Introduction

Early diagnosis of oral squamous cell carcinoma can greatly improve treatment success rate and patient survival. Although Optical Coherence Tomography (OCT) based Angiography (OCTA) is a promising in vivo technique in oral imaging, there is a need for objective assessment of oral microvasculature.

Methods

This study aimed to demonstrate a comprehensive methodology of quantitative assessing OCTA intraoral scanning results to provide measurable, reproducible data and to avoid subjective visual interpretations. Data were collected from 37 healthy subjects in total across four intraoral sites-buccal mucosa (n = 32), labial mucosa (n = 24), floor of the mouth (n = 13), and hard palate (n = 8)-using a non-invasive swept-source OCT system. Four quantitative metrics-vessel area density, vessel skeleton density, vessel diameter index, and a newly proposed weighted Tortuosity Index-were used to assess OCTA images in oral applications.

Results

The quadruple quantitative assessment's repeatability was evaluated to be reliable. Analysis of a benign ulcer case revealed differences in these metrics compared to healthy cases.

Discussion/Conclusion

In conclusion, we demonstrated a comprehensive method to quantify microvasculature in the oral cavity, showing considerable promise for early diagnosis and clinical management of oral diseases.

Quantitative Optimization of Handheld Probe External Pressure on Dermatological Microvasculature Using Optical Coherence Tomography-Based Angiography

Optical Coherence Tomography (OCT)-based angiography (OCTA) is a high-resolution, high-speed, and non-invasive imaging method that can provide vascular mapping of subcutaneous tissue up to approximately 2 mm. In dermatology applications of OCTA, handheld probes are always designed with a piece of transparent but solid contact window placed at the end of the probe to directly contact the skin for achieving better focusing between the light source and the tissue, reducing noise caused by minor movements. The pressure between the contact window and the skin is usually uncontrollable, and high external pressure affects the quality of microvascular imaging by compressing the vessels and obstructing the underlying blood flow. Therefore, it is necessary to determine a pressure range to ensure that the vessels can be fully imaged in high-quality images. In this paper, two pressure sensors were added to the existing handheld OCT probe, and the imaging probe was fixed to a metal stand and adjusted vertically to change the pressure between the probe and the tested skin site, a gradient of roughly 4 kPa (with 1-2 kPa error) increase was applied in each experiment, and the impact of pressure to the vessel was calculated. The experiment involved a total of five subjects, three areas of which were scanned (palm, back of the hand, and forearm). The vessel density was calculated to evaluate the impact of external pressure on angiography. In addition, PSNR was calculated to ensure that the quality of different tests was at a similar level. The angiography showed the highest density (about 10%) when the pressure between the contact window on the probe and the test area was between 3 and 5 kPa. As the pressure increased, the vascular density decreased, and the rate of decrease varied in different test areas. After fitting all the data points according to the different sites, the slope of the fitted line, i.e., the rate of decrease in density per unit value of pressure, was found to be 4.05% at the palm site, 6.93% at the back of the hand, and 4.55% at the forearm site. This experiment demonstrates that the pressure between the skin and contact window is a significant parameter that cannot be ignored. It is recommended that in future OCTA data collection processes and probe designs, the impact of pressure on the experiment be considered.

Optical coherence tomography: Promising imaging technique for the diagnosis of oral mucosal diseases

OBJECTIVE

This review aims to summarize the latest application of optical coherence tomography (OCT) in oral mucosal diseases, promoting an accurate and earlier diagnosis of such disorders, which are difficult to be differentiated.

SUBJECTIVE AND METHODS

References on the application of OCT in oral mucosal diseases were mainly obtained from PubMed, Embase, Web of Science and Scopus databases, using the keywords: "optical coherence tomography and 'oral mucosa/oral cancers/oral potentially malignant diseases/oral lichen planus/oral leukoplakia/oral erythroplakia/discoid lupus erythematosus/oral autoimmune bullous diseases/oral ulcers/erythema multiforme/oral mucositis'".

RESULTS

It is found that OCT is showing a promising application potential in the early detection, diagnosis, differential diagnosis, monitoring of oral cancer and oral dysplastic lesions, as well as the delineation of tumor margins. OCT is also playing an increasingly important role in the diagnosis of oral potentially malignant disorders, oral mucosal bullous diseases, oral ulcerative diseases, erythema multiforme, and the early detection of oral mucositis.

CONCLUSION

Optical coherence tomography, as a novel optical technique featured by real-time, noninvasive, dynamic and high-resolution imaging, is of great use to serve as an adjunct tool for the diagnosis, differential diagnosis, monitoring and therapy evaluation of oral mucosal diseases.

Robust Ultrafast Projection Pipeline for Structural and Angiography Imaging of Fourier-Domain Optical Coherence Tomography

The current methods to generate projections for structural and angiography imaging of Fourier-Domain optical coherence tomography (FD-OCT) are significantly slow for prediagnosis improvement, prognosis, real-time surgery guidance, treatments, and lesion boundary definition. This study introduced a robust ultrafast projection pipeline (RUPP) and aimed to develop and evaluate the efficacy of RUPP. RUPP processes raw interference signals to generate structural projections without the need for Fourier Transform. Various angiography reconstruction algorithms were utilized for efficient projections. Traditional methods were compared to RUPP using PSNR, SSIM, and processing time as evaluation metrics. The study used 22 datasets (hand skin: 9; labial mucosa: 13) from 8 volunteers, acquired with a swept-source optical coherence tomography system. RUPP significantly outperformed traditional methods in processing time, requiring only 0.040 s for structural projections, which is 27 times faster than traditional summation projections. For angiography projections, the best RUPP variation took 0.15 s, making it 7518 times faster than the windowed eigen decomposition method. However, PSNR decreased by 41-45% and SSIM saw reductions of 25-74%. RUPP demonstrated remarkable speed improvements over traditional methods, indicating its potential for real-time structural and angiography projections in FD-OCT, thereby enhancing clinical prediagnosis, prognosis, surgery guidance, and treatment efficacy.