Low-cost, ultracompact handheld optical coherence tomography probe for in vivo oral maxillofacial tissue imaging

A Novel Small Form-Factor Handheld Optical Coherence Tomography Probe for Oral Soft Tissue Imaging

Tissue imaging is crucial in oral cancer diagnostics. Imaging techniques such as X-ray imaging, magnetic resonance imaging, optical coherence tomography (OCT) and computed tomography (CT) enable the visualization and analysis of tissues, aiding in the detection and diagnosis of cancers. A significant amount of research has been conducted on designing OCT probes for tissue imaging, but most probes are either heavy, bulky and require external mounting or are lightweight but straight. This study addresses these challenges, resulting in a curved lightweight, low-voltage and compact handheld imaging probe for oral soft tissue examination. To the best of our knowledge, this is the first curved handheld OCT probe with its shape optimized for oral applications. This probe features highly compact all-fiber optics with a diameter of 125 μm and utilizes innovative central deflection magnetic actuation for controlled beam scanning. To ensure vertical stability while scanning oral soft tissues, the fiber was secured through multiple narrow slits at the probe's distal end. This apparatus was encased in a 3D-printed angular cylinder tube (15 mm outer diameter, 12 mm inner diameter and 160 mm in length, weighing < 20 g). An angle of 115° makes the probe easy to hold and suitable for scanning in space-limited locations. To validate the feasibility of this probe, we conducted assessments on a multi-layered imaging phantom and human tissues, visualizing microstructural features with high contrast.

Next-generation endoscopic probe for detection of esophageal dysplasia using combined OCT and angle-resolved low-coherence interferometry

Angle-resolved low-coherence interferometry (a/LCI) is an optical technique that enables depth-specific measurements of nuclear morphology, with applications to detecting epithelial cancers in various organs. Previous a/LCI setups have been limited by costly fiber-optic components and large footprints. Here, we present a novel a/LCI instrument incorporating a channel for optical coherence tomography (OCT) to provide real-time image guidance. We showcase the system's capabilities by acquiring imaging data from in vivo Barrett's esophagus patients. The main innovation in this geometry lies in implementing a pathlength-matched single-mode fiber array, offering substantial cost savings while preserving signal fidelity. A further innovation is the introduction of a specialized side-viewing probe tailored for esophageal imaging, featuring miniature optics housed in a custom 3D-printed enclosure attached to the tip of the endoscope. The integration of OCT guidance enhances the precision of tissue targeting by providing real-time morphology imaging. This novel device represents a significant advancement in clinical translation of an enhanced screening approach for esophageal precancer, paving the way for more effective early-stage detection and intervention strategies.

Wearable optical coherence tomography angiography probe for freely moving mice

Optical coherence tomography (OCT) is an emerging optical imaging technology that holds great potential in medical and biological applications. Apart from its conventional ophthalmic uses, it has found extensive applications in studying various brain activities and disorders in anesthetized/restricted rodents, with a particular focus on visualizing brain blood vessel morphology and function. However, developing a compact wearable OCT probe for studying the brain activity/disorders in freely moving rodents is challenging due to the requirements for stability and lightweight design. Here, we report a robust wearable OCT probe, which, to the best of our knowledge, is the first wearable OCT angiography probe capable of long-term monitoring of mouse brain blood flow. This wearable imaging probe has a maximum scanning speed of 76 kHz, with a 12 µm axial resolution, 5.5 µm lateral resolution, and a large field of view (FOV) of 4 mm × 4 mm. It offers easy assembly and stable imaging, enabling it to capture brain vessels in freely moving rodents. We tested this probe to monitor cerebral hemodynamics for up to 4 hours during the acute ischemic phase after photothrombotic stroke in mice, highlighting the reliability and long-term stability of our probe. This work contributes to the advancement of wearable biomedical imaging.

Large Area Kidney Imaging for Pre-transplant Evaluation using Real-Time Robotic Optical Coherence Tomography

Optical coherence tomography (OCT) is a high-resolution imaging modality that can be used to image microstructures of human kidneys. These images can be analyzed to evaluate the viability of the organ for transplantation. However, current OCT devices suffer from insufficient field-of-view, leading to biased examination outcomes when only small portions of the kidney can be assessed. Here we present a robotic OCT system where an OCT probe is integrated with a robotic manipulator, enabling wider area spatially-resolved imaging. With the proposed system, it becomes possible to comprehensively scan the kidney surface and provide large area parameterization of the microstructures. We verified the probe tracking accuracy with a phantom as 0.0762±0.0727 mm and demonstrated its clinical feasibility by scanning ex vivo kidneys. The parametric map exhibits fine vasculatures beneath the kidney surface. Quantitative analysis on the proximal convoluted tubule from the ex vivo human kidney yields highly clinical-relevant information.

Potential use of optical coherence tomography in oral potentially malignant disorders: in-vivo case series study

BACKGROUND

Evidence confirms that the use of Optical Coherence Tomography (OCT) in oral medicine can be a reliable aid for the diagnosis and management of Oral Potentially Malignant Disorders (OPMDs). Several authors described the ability of this system to detect the structural changes of the epithelia involved by the OPMDs. The purpose of this case series is to provide a suggestion for interpretation of OCT images from different OPMDs, compared to OCT images of healthy tissues.

METHODS

A sample of 11 OPMDs patients was recruited and analyzed with OCT. The images obtained were then compared with an OCT repertoire image. In this work the reflectance degree was considered, together with the analysis of the increased/decreased thicknesses of the various layers. Keratin Layer (KL), Epithelial Layer (EP), Lamina Propria (LP), Basal Membrane (BM) assessment, for each lesion, was performed.

RESULTS

OCT measurements of KL, EP and LP layers, together with BM assessing, should aid the physicians to recognize and describe different oral lesions, relating them to the corresponding oral pathology.

CONCLUSION

More studies like this, on larger samples, are needed to validate the results and provide, in the future, a kind of manual that could guide clinicians to correctly interpret the OCT images in relation to the causing pathologies.

TRIAL REGISTRATION

The present trial has been registered with ISRCTN (#17,893,224).

Deep-Learning-Based Automated Identification and Visualization of Oral Cancer in Optical Coherence Tomography Images

Early detection and diagnosis of oral cancer are critical for a better prognosis, but accurate and automatic identification is difficult using the available technologies. Optical coherence tomography (OCT) can be used as diagnostic aid due to the advantages of high resolution and non-invasion. We aim to evaluate deep-learning-based algorithms for OCT images to assist clinicians in oral cancer screening and diagnosis. An OCT data set was first established, including normal mucosa, precancerous lesion, and oral squamous cell carcinoma. Then, three kinds of convolutional neural networks (CNNs) were trained and evaluated by using four metrics (accuracy, precision, sensitivity, and specificity). Moreover, the CNN-based methods were compared against machine learning approaches through the same dataset. The results show the performance of CNNs, with a classification accuracy of up to 96.76%, is better than the machine-learning-based method with an accuracy of 92.52%. Moreover, visualization of lesions in OCT images was performed and the rationality and interpretability of the model for distinguishing different oral tissues were evaluated. It is proved that the automatic identification algorithm of OCT images based on deep learning has the potential to provide decision support for the effective screening and diagnosis of oral cancer.

Intraoral optical coherence tomography and angiography combined with autofluorescence for dental assessment

There remains a clinical need for an accurate and non-invasive imaging tool for intraoral evaluation of dental conditions. Optical coherence tomography (OCT) is a potential candidate to meet this need, but the design of current OCT systems limits their utility in the intraoral examinations. The inclusion of light-induced autofluorescence (LIAF) can expedite the image collection process and provides a large field of view for viewing the condition of oral tissues. This study describes a novel LIAF-OCT system equipped with a handheld probe designed for intraoral examination of microstructural (via OCT) and microvascular information (via OCT angiography, OCTA). The handheld probe is optimized for use in clinical studies, maintaining the ability to detect and image changes in the condition of oral tissue (e.g., hard tissue damage, presence of dental restorations, plaque, and tooth stains). The real-time LIAF provides guidance for OCT imaging to achieve a field of view of approximately 6.9 mm × 7.8 mm, and a penetration depth of 1.5 mm to 3 mm depending on the scattering property of the target oral tissue. We demonstrate that the proposed system is successful in capturing reliable depth-resolved images from occlusal and palatal surfaces and offers added design features that can enhance its usability in clinical settings.

A handheld fiber-optic probe to enable optical coherence tomography of oral soft tissue

This study presents a highly miniaturized, handheld probe developed for rapid assessment of soft tissue using optical coherence tomography (OCT). OCT is a non-invasive optical technology capable of visualizing the sub-surface structural changes that occur in soft tissue disease such as oral lichen planus. However, usage of OCT in the oral cavity has been limited, as the requirements for high-quality optical scanning have often resulted in probes that are heavy, unwieldy and clinically impractical. In this paper, we present a novel probe that combines an all-fiber optical design with a light-weight magnetic scanning mechanism to provide easy access to the oral cavity. The resulting probe is approximately the size of a pen (10 mm 140 mm) and weighs only 10 grams. To demonstrate the feasibility and high image quality achieved with the probe, imaging is performed on the buccal mucosa and alveolar mucosa during routine clinical assessment of six patients diagnosed with oral lichen planus. Results show the loss of normal tissue structure within the lesion, and contrast this with the clear delineation of tissue layers in adjacent inconspicuous regions. The results also demonstrate the ability of the probe to acquire a three-dimensional data volume by manually sweeping across the surface of the mucosa. The findings of this study show the feasibility of using a small, lightweight probe to identify pathological features in oral soft tissue.

Identification of oral precancerous and cancerous tissue by swept source optical coherence tomography

BACKGROUND AND OBJECTIVES

Distinguishing cancer from precancerous lesions is critical and challenging in oral medicine. As a noninvasive method, optical coherence tomography (OCT) has the advantages of real-time, in vivo, and large-depth imaging. Texture information hidden in OCT images can provide an important auxiliary effect for improving diagnostic accuracy. The aim of this study is to explore a reliable and accurate OCT-based method for the screening and diagnosis of human oral diseases, especially oral cancer.

MATERIALS AND METHODS

Fresh ex vivo oral tissues including normal mucosa, leukoplakia with epithelial hyperplasia (LEH), and oral squamous cell carcinoma (OSCC) were imaged intraoperatively by a homemade OCT system, and 58 texture features were extracted to create computational models of these tissues. A principal component analysis algorithm was employed to optimize the combination of texture feature vectors. The identification based on artificial neural network (ANN) was proposed and the sensitivity/specificity was calculated statistically to evaluate the classification performance.

RESULTS

A total of 71 sites of three types of oral tissues were measured, and 5176 OCT images of three types of oral tissues were used in this study. The superior classification result based on ANN was obtained with an average accuracy of 98.17%. The sensitivity and specificity of normal mucosa, LEH, and OSCC are 98.17% / 98.38%, 93.81% / 98.54%, and 98.11% / 99.04%, respectively.

CONCLUSION

It is demonstrated from the high accuracies, sensitivities, and specificities that texture-based analysis can be used to identify oral precancerous and cancerous tissue in OCT images, and it has the potential to help surgeons in diseases screening and diagnosis effectively.

A review of low-cost and portable optical coherence tomography

Optical coherence tomography (OCT) is a powerful optical imaging technique capable of visualizing the internal structure of biological tissues at near cellular resolution. For years, OCT has been regarded as the standard of care in ophthalmology, acting as an invaluable tool for the assessment of retinal pathology. However, the costly nature of most current commercial OCT systems has limited its general accessibility, especially in low-resource environments. It is therefore timely to review the development of low-cost OCT systems as a route for applying this technology to population-scale disease screening. Low-cost, portable and easy to use OCT systems will be essential to facilitate widespread use at point of care settings while ensuring that they offer the necessary imaging performances needed for clinical detection of retinal pathology. The development of low-cost OCT also offers the potential to enable application in fields outside ophthalmology by lowering the barrier to entry. In this paper, we review the current development and applications of low-cost, portable and handheld OCT in both translational and research settings. Design and cost-reduction techniques are described for general low-cost OCT systems, including considerations regarding spectrometer-based detection, scanning optics, system control, signal processing, and the role of 3D printing technology. Lastly, a review of clinical applications enabled by low-cost OCT is presented, along with a detailed discussion of current limitations and outlook.

Classification of oral salivary gland tumors based on texture features in optical coherence tomography images

Currently, the diagnoses of oral diseases primarily depend on the visual recognition of experienced clinicians. It has been proven that automatic recognition based on images can support clinical decision-making by extracting and analyzing objective hidden information. In recent years, optical coherence tomography (OCT) has become a powerful optical imaging technique with the advantages of high resolution and non-invasion. In our study, a dataset composed of four kinds of oral salivary gland tumors (SGTs) was obtained from a homemade swept-source OCT, including two benign and two malignant tumors. Seventy-six texture features were extracted from OCT images to create computational models of diseases. It was demonstrated that the artificial neural network (ANN) based on principal component analysis (PCA) can obtain high diagnostic sensitivity and specificity (higher than 99%) for these four kinds of tumors. The classification accuracy of each tumor is larger than 99%. In addition, the performances of two classifiers (ANN and support vector machine) were quantitatively evaluated based on SGTs. It was proven that the texture features in OCT images provided objective information to classify oral tumors.

Classification of Salivary Gland Tumors Based on Quantitative Optical Coherence Tomography

BACKGROUND AND OBJECTIVES

Visual inspection is the primary diagnostic method for oral diseases, and its accuracy of diagnosis mainly depends on surgeons' experience. Histological examination is still the golden standard, but it is invasive and time-consuming. In order to address these issues, as a noninvasive imaging technique, optical coherence tomography (OCT) can differentiate oral tissue with advantages of real-time, in situ, and high resolution. The aim of this study is to explore optimal quantitative parameters in OCT images to distinguish different salivary gland tumors.

STUDY DESIGN/MATERIALS AND METHODS

OCT images of four salivary gland tumors were obtained from 14 patients, including mucoepidermoid carcinoma (MC), adenoid cystic carcinoma (ACC), basal cell adenoma (BCA), and pleomorphic adenoma (PA). Two parameters of optical attenuation coefficient (OAC) and standard deviation (SD) along the depth of OCT signal were combined to create a computational model of classification, and sensitivity/specificity of classification was calculated statistically to evaluate their results.

RESULTS

A total of 5,919 two-dimensional (2D) OCT images were used for quantitative analysis. The classification sensitivities of 89.6%, 95.0%, 89.5%, 97.8%, and specificities of 97.6%, 99.0%, 98.0%, 98.2%, respectively, were obtained for MC, ACC, BCA, and PA, with the thresholds of 3.6 mm^-1 based on OAC and 0.22/0.18 based on SD.

CONCLUSION

It was demonstrated that OAC and SD could be considered as important parameters in quantitative analysis of OCT images for salivary gland tissue characterization and intraoperative diagnosis. It is of great potential value in promoting the application of this method based on OCT in clinical practice. Lasers Surg. © 2020 Wiley Periodicals LLC.