Endoscopic Optical Coherence Tomography Research Articles

Quantification of attenuation and speckle features from endoscopic OCT images for the diagnosis of human brain glioma

Application of optical coherence tomography (OCT) in neurosurgery mostly includes the discrimination between intact and malignant tissues aimed at the detection of brain tumor margins. For particular tissue types, the existing approaches demonstrate low performance, which stimulates the further research for their improvement. The analysis of speckle patterns of brain OCT images is proposed to be taken into account for the discrimination between human brain glioma tissue and intact cortex and white matter. The speckle properties provide additional information of tissue structure, which could help to increase the efficiency of tissue differentiation. The wavelet analysis of OCT speckle patterns was applied to extract the power of local brightness fluctuations in speckle and its standard deviation. The speckle properties are analysed together with attenuation ones using a set of ex vivo brain tissue samples, including glioma of different grades. Various combinations of these features are considered to perform linear discriminant analysis for tissue differentiation. The results reveal that it is reasonable to include the local brightness fluctuations at first two wavelet decomposition levels in the analysis of OCT brain images aimed at neurosurgical diagnosis.

ATN-Res2Unet: an advanced deep learning network for the elimination of saturation artifacts in endoscopy optical coherence tomography

Endoscopic optical coherence tomography (OCT) possesses the capability to non-invasively image internal lumens; however, it is susceptible to saturation artifacts arising from robust reflective structures. In this study, we introduce an innovative deep learning network, ATN-Res2Unet, designed to mitigate saturation artifacts in endoscopic OCT images. This is achieved through the integration of multi-scale perception, multi-attention mechanisms, and frequency domain filters. To address the challenge of obtaining ground truth in endoscopic OCT, we propose a method for constructing training data pairs. Experimental in vivo data substantiates the effectiveness of ATN-Res2Unet in reducing diverse artifacts while preserving structural information. Comparative analysis with prior studies reveals a notable enhancement, with average quantitative indicators increasing by 45.4–83.8%. Significantly, this study marks the inaugural exploration of leveraging deep learning to eradicate artifacts from endoscopic OCT images, presenting considerable potential for clinical applications.

Design of an endoscopic OCT probe based on piezoelectric tube with quartered outside electrodes.

Introduction: Optical coherence tomography (OCT) is a pivotal imaging modality in ophthalmology for real-time, in vivo visualization of retinal structures. To enhance the capability and safety of OCT, this study focuses on the development of a micro intraocular OCT probe. The demand for minimal invasiveness and precise imaging drives the need for advanced probe designs that can access tight and sensitive areas, such as the ocular sclera. Methods: A novel OCT probe was engineered using a piezoelectric tube with quartered electrodes to drive Lissajous scanning movements at the end of a single-mode fiber. This design allows the probe to enter the eyeball through a scleral opening. Structural innovation enables the outer diameter of the endoscopic OCT probe to be adjusted from 13G (2.41mm) to 25G (0.51mm), accommodating various imaging field sizes and ensuring compatibility with different scleral incisions. Results: The fabricated micro intraocular OCT probe successfully performed preliminary imaging experiments on in vivo fingers. The Lissajous scanning facilitated comprehensive coverage of the target area, enhancing the imaging capabilities. Discussion: The integration of a piezoelectric tube with quartered outside electrodes into the OCT probe design proved effective for achieving precise control over scanning movements and adaptability to different surgical needs. The design characteristics and practical applications demonstrated the probe's potential in clinical settings.

In Vivo Thickness of the Healthy Tympanic Membrane Determined by Optical Coherence Tomography.

Tympanic membrane (TM) thickness is an important parameter for differentiation between a healthy and a pathologic TM. Furthermore, it is needed for modeling the middle ear function. Endoscopic optical coherence tomography (eOCT) provides the opportunity to measure the TM thickness of the entire TM in vivo. A total of 27 healthy ears were examined by eOCT. The system uses a light source with a central wavelength of 1,300 nm. The endoscope with an outer diameter of 3.5 mm provides a field of view of 10 mm and a working distance of 10 mm. Thickness measurements were carried out at 8 points on the TM. Additionally, the existing literature was analyzed, and a mean TM thickness value was determined. The mean thickness of the TM over all measurement points of the pars tensa was 120.2 μm, and the pars flaccida was significantly thicker with a mean thickness of 177.9 μm. Beyond that, there were no significant differences between the single quadrants. The mean TM thickness in the literature was 88.8 μm. EOCT provides the possibility for in vivo thickness determination of the TM. The mean thickness seems to be higher than in the previous studies, which were mostly carried out ex vivo. Our study takes the three-dimensional refraction into account and provides a method for the refraction correction.

The Dresden in vivo OCT dataset for automatic middle ear segmentation

Endoscopic optical coherence tomography (OCT) offers a non-invasive approach to perform the morphological and functional assessment of the middle ear in vivo. However, interpreting such OCT images is challenging and time-consuming due to the shadowing of preceding structures. Deep neural networks have emerged as a promising tool to enhance this process in multiple aspects, including segmentation, classification, and registration. Nevertheless, the scarcity of annotated datasets of OCT middle ear images poses a significant hurdle to the performance of neural networks. We introduce the Dresden in vivo OCT Dataset of the Middle Ear (DIOME) featuring 43 OCT volumes from both healthy and pathological middle ears of 29 subjects. DIOME provides semantic segmentations of five crucial anatomical structures (tympanic membrane, malleus, incus, stapes and promontory), and sparse landmarks delineating the salient features of the structures. The availability of these data facilitates the training and evaluation of algorithms regarding various analysis tasks with middle ear OCT images, e.g. diagnostics.

Distal planar rotary scanner for endoscopic optical coherence tomography.

Optical coherence tomography (OCT) is becoming a more common endoscopic imaging modality for detecting and treating disease given its high resolution and image quality. To use OCT for 3-dimensional imaging of small lumen, embedding an optical scanner at the distal end of an endoscopic probe for circumferential scanning the probing light is a promising way to implement high-quality imaging unachievable with the conventional method of revolving an entire probe. To this end, the present work proposes a hollow and planar micro rotary actuator for its use as an endoscopic distal scanner. A miniaturized design of this ferrofluid-assisted electromagnetic actuator is prototyped to act as a full 360° optical scanner, which is integrated at the tip of a fiber-optic probe together with a gradient-index lens for use with OCT. The scanner is revealed to achieve a notably improved dynamic performance that shows a maximum speed of 6500rpm, representing 325% of the same reported with the preceding design, while staying below the thermal limit for safe in-vivo use. The scanner is demonstrated to perform real-time OCT using human fingers as live tissue samples for the imaging tests. The acquired images display no shadows from the electrical wires to the scanner, given its hollow architecture that allows the probing light to pass through the actuator body, as well as the quality high enough to differentiate the dermis from the epidermis while resolving individual sweat glands, proving the effectiveness of the prototyped scanner design for endoscopic OCT application.

Virtual Hall sensor triggered multi-MHz endoscopic OCT imaging for stable real-time visualization.

Circumferential scanning in endoscopic imaging is crucial across various disciplines, and optical coherence tomography (OCT) is often the preferred choice due to its high-speed, high-resolution, and micron-scale imaging capabilities. Moreover, real-time and high-speed 3D endoscopy is a pivotal technology for medical screening and precise surgical guidance, among other applications. However, challenges such as image jitter and non-uniform rotational distortion (NURD) are persistent obstacles that hinder real-time visualization during high-speed OCT procedures. To address this issue, we developed an innovative, low-cost endoscope that employs a brushless DC motor for scanning, and a sensorless technique for triggering and synchronizing OCT imaging with the scanning motor. This sensorless approach uses the motor's electrical feedback (back electromotive force, BEMF) as a virtual Hall sensor to initiate OCT image acquisition and synchronize it with a Fourier Domain Mode-Locked (FDML)-based Megahertz OCT system. Notably, the implementation of BEMF-triggered OCT has led to a substantial reduction in image jitter and NURD (<4 mrad), thereby opening up a new window for real-time visualization capabilities. This approach suggests potential benefits across various applications, aiming to provide a more accurate, deployable, and cost-effective solution. Subsequent studies can explore the adaptability of this system to specific clinical scenarios and its performance under practical endoscopic conditions.

CylinGCN: Cylindrical structures segmentation in 3D biomedical optical imaging by a contour-based graph convolutional network

Cylindrical organs, e.g., blood vessels, airways, and intestines, are ubiquitous structures in biomedical optical imaging analysis. Image segmentation of these structures serves as a vital step in tissue physiology analysis. Traditional model-driven segmentation methods seek to fit the structure by constructing a corresponding topological geometry based on domain knowledge. Classification-based deep learning methods neglect the geometric features of the cylindrical structure and therefore cannot ensure the continuity of the segmentation surface. In this paper, by treating the cylindrical structures as a 3D graph, we introduce a novel contour-based graph neural network for 3D cylindrical structure segmentation in biomedical optical imaging. Our proposed method, which we named CylinGCN, adopts a novel learnable framework that extracts semantic features and complex topological relationships in the 3D volumetric data to achieve continuous and effective 3D segmentation. Our CylinGCN consists of a multiscale 3D semantic feature extractor for extracting inter-frame multiscale semantic features, and a residual graph convolutional network (GCN) contour generator that combines the semantic features and cylindrical topological priors to generate segmentation contours. We tested the CylinGCN framework on two types of optical tomographic imaging data, small animal whole body photoacoustic tomography (PAT) and endoscopic airway optical coherence tomography (OCT), and the results show that CylinGCN achieves state-of-the-art performance. Code will be released at https://github.com/lzc-smu/CylinGCN.git.

Cross-attention learning enables real-time nonuniform rotational distortion correction in OCT.

Nonuniform rotational distortion (NURD) correction is vital for endoscopic optical coherence tomography (OCT) imaging and its functional extensions, such as angiography and elastography. Current NURD correction methods require time-consuming feature tracking/registration or cross-correlation calculations and thus sacrifice temporal resolution. Here we propose a cross-attention learning method for the NURD correction in OCT. Our method is inspired by the recent success of the self-attention mechanism in natural language processing and computer vision. By leveraging its ability to model long-range dependencies, we can directly obtain the spatial correlation between OCT A-lines at any distance, thus accelerating the NURD correction. We develop an end-to-end stacked cross-attention network and design three types of optimization constraints. We compare our method with two traditional feature-based methods and a CNN-based method on two publicly-available endoscopic OCT datasets. We further verify the NURD correction performance of our method on 3D stent reconstruction using a home-built endoscopic OCT system. Our method achieves a ∼3 × speedup to real time (26 ± 3 fps), and superior correction performance.

Multipath artifacts enable angular contrast in multimodal endoscopic optical coherence tomography.

Multipath artifacts are inherent to double-clad fiber based optical coherence tomography (OCT), appearing as ghost images blurred in the A-line direction. They result from the excitation of higher-order inner-cladding modes in the OCT sample arm which cross-couple into the fundamental mode at discontinuities and thus are detected in single-mode fiber-based interferometers. Historically, multipath artifacts have been regarded as a drawback in single fiber endoscopic multimodal OCT systems as they degrade OCT quality. In this work, we reveal that multipath artifacts can be projected into high-quality two-dimensional en face images which encode high angle backscattering features. Using a combination of experiment and simulation, we characterize the coupling of Mie-range scatterers into the fundamental image (LP01 mode) and higher-order image (multipath artifact). This is validated experimentally through imaging of microspheres with an endoscopic multimodal OCT system. The angular dependence of the fundamental image and higher order image generated by the multipath artifact lays the basis for multipath contrast, a ratiometric measurement of differential coupling which provides information regarding the angular diversity of a sample. Multipath contrast images can be generated from OCT data where multipath artifacts are present, meaning that a wealth of clinical data can be retrospectively examined.

Optical Coherence Tomography as a Tool for Imaging the Sinonasal Mucosa in Patients: A Systematic Review.

Objectives: To summarize the current applications and potential uses of optical coherence tomography (OCT), a noninvasive imaging modality that uses near-infrared light to produce cross-sectional, high-resolution images of biologic tissues, for evaluating the sinonasal mucosa in patients. Methods: Original articles utilizing OCT to image the sinonasal mucosa in patients were identified from the PubMed, Embase, Cochrane Library, Web of Science, and Scopus databases using the search phrase: "Optical Coherence Tomography" AND (sinonasal OR intranasal OR sinus OR nose OR sinusitis OR rhinitis OR olfactory). Strength of evidence, quality of evidence, and risk of bias were measured using validated scales. Study results were qualitatively assessed. Results: Out of 1662 original records identified through database searching, 9 studies were included in the systematic review. Levels of evidence ranged from III to IV and quality of evidence ranged from moderate to very low. Endoscopic OCT systems as well as OCT systems integrated with surgical microscopes were described in the literature. Applications of OCT for imaging the sinonasal mucosa included identifying morphological patterns unique to individual diseases, detecting mucosal structural changes after medical therapies and procedures, and evaluating mucociliary clearance. Conclusions: Most studies investigating OCT imaging of the sinonasal mucosa featured small sample sizes and lacked control groups. While OCT imaging could be a useful adjunct for diagnosing sinonasal disorders and monitoring response to treatment in the future, additional high-quality studies are necessary to determine if the use of OCT imaging can lead to improved diagnostic accuracy and health outcomes for patients with sinonasal pathologies.

Automatic 3D reconstruction of an anatomically correct upper airway from endoscopic long range OCT images.

Endoscopic airway optical coherence tomography (OCT) is a non-invasive and high resolution imaging modality for the diagnosis and analysis of airway-related diseases. During OCT imaging of the upper airway, in order to reliably characterize its 3D structure, there is a need to automatically detect the airway lumen contour, correct rotational distortion and perform 3D airway reconstruction. Based on a long-range endoscopic OCT imaging system equipped with a magnetic tracker, we present a fully automatic framework to reconstruct the 3D upper airway model with correct bending anatomy. Our method includes an automatic segmentation method for the upper airway based on dynamic programming algorithm, an automatic initial rotation angle error correction method for the detected 2D airway lumen contour, and an anatomic bending method combined with the centerline detected from the magnetically tracked imaging probe. The proposed automatic reconstruction framework is validated on experimental datasets acquired from two healthy adults. The result shows that the proposed framework allows the full automation of 3D airway reconstruction from OCT images and thus reveals its potential to improve analysis efficiency of endoscopic OCT images.

Non-rigid point cloud registration for middle ear diagnostics with endoscopic optical coherence tomography

PurposeMiddle ear infection is the most prevalent inflammatory disease, especially among the pediatric population. Current diagnostic methods are subjective and depend on visual cues from an otoscope, which is limited for otologists to identify pathology. To address this shortcoming, endoscopic optical coherence tomography (OCT) provides both morphological and functional in vivo measurements of the middle ear. However, due to the shadow of prior structures, interpretation of OCT images is challenging and time-consuming. To facilitate fast diagnosis and measurement, improvement in the readability of OCT data is achieved by merging morphological knowledge from ex vivo middle ear models with OCT volumetric data, so that OCT applications can be further promoted in daily clinical settings.MethodsWe propose C2P-Net: a two-staged non-rigid registration pipeline for complete to partial point clouds, which are sampled from ex vivo and in vivo OCT models, respectively. To overcome the lack of labeled training data, a fast and effective generation pipeline in Blender3D is designed to simulate middle ear shapes and extract in vivo noisy and partial point clouds.ResultsWe evaluate the performance of C2P-Net through experiments on both synthetic and real OCT datasets. The results demonstrate that C2P-Net is generalized to unseen middle ear point clouds and capable of handling realistic noise and incompleteness in synthetic and real OCT data.ConclusionsIn this work, we aim to enable diagnosis of middle ear structures with the assistance of OCT images. We propose C2P-Net: a two-staged non-rigid registration pipeline for point clouds to support the interpretation of in vivo noisy and partial OCT images for the first time. Code is available at: https://gitlab.com/nct_tso_public/c2p-net.

Endoscopic optical coherence tomography angiography using an externally driving catheter.

Endoscopic optical coherence tomography (OCT) is an imaging modality that enables cross-sectional subsurface imaging of tubular organs and cavities. Recently, endoscopic OCT angiography (OCTA) was successfully achieved in distal scanning systems using an internal-motor-driving catheter. In conventional OCT systems using externally driving catheters, the mechanical instability in the proximal actuation causes difficulties for differentiating capillaries in tissues. In this study, OCTA in an endoscopic OCT system using an external-motor-driving catheter was proposed. Blood vessels were visualized by using a high-stability inter-A-scan scheme and the spatiotemporal singular value decomposition algorithm. It is not limited by nonuniform rotation distortion caused by the catheter and physiological motion artifacts. Results show that microvasculature in a custom-made microfluidic phantom and the submucosal capillaries in the mouse rectum are successfully visualized. Furthermore, OCTA using a catheter with a small size (outer diameter less than 1 mm) makes it possible for early diagnosis of narrow lumens, such as pancreatic and bile duct cancers.

In vivo microstructural investigation of the human tympanic membrane by endoscopic polarization-sensitive optical coherence tomography.

Endoscopic optical coherence tomography (OCT) is of growing interest for in vivo diagnostics of the tympanic membrane (TM) and the middle ear but generally lacks a tissue-specific contrast. To assess the collagen fiber layer within the in vivo TM, an endoscopic imaging method utilizing the polarization changes induced by the birefringent connective tissue was developed. An endoscopic swept-source OCT setup was redesigned and extended by a polarization-diverse balanced detection unit. Polarization-sensitive OCT (PS-OCT) data were visualized by a differential Stokes-based processing and the derived local retardation. The left and right ears of a healthy volunteer were examined. Distinct retardation signals in the annulus region of the TM and near the umbo revealed the layered structure of the TM. Due to the TM's conical shape and orientation in the ear canal, high incident angles onto the TM's surface, and low thicknesses compared to the axial resolution limit of the system, other regions of the TM were more difficult to evaluate. The use of endoscopic PS-OCT is feasible to differentiate birefringent and nonbirefringent tissue of the human TM in vivo. Further investigations on healthy as well as pathologically altered TMs are required to validate the diagnostic potential of this technique.

Towards OCT-Guided Endoscopic Laser Surgery—A Review

Optical Coherence Tomography (OCT) is an optical imaging technology occupying a unique position in the resolution vs. imaging depth spectrum. It is already well established in the field of ophthalmology, and its application in other fields of medicine is growing. This is motivated by the fact that OCT is a real-time sensing technology with high sensitivity to precancerous lesions in epithelial tissues, which can be exploited to provide valuable information to clinicians. In the prospective case of OCT-guided endoscopic laser surgery, these real-time data will be used to assist surgeons in challenging endoscopic procedures in which high-power lasers are used to eradicate diseases. The combination of OCT and laser is expected to enhance the detection of tumors, the identification of tumor margins, and ensure total disease eradication while avoiding damage to healthy tissue and critical anatomical structures. Therefore, OCT-guided endoscopic laser surgery is an important nascent research area. This paper aims to contribute to this field with a comprehensive review of state-of-the-art technologies that may be exploited as the building blocks for achieving such a system. The paper begins with a review of the principles and technical details of endoscopic OCT, highlighting challenges and proposed solutions. Then, once the state of the art of the base imaging technology is outlined, the new OCT-guided endoscopic laser surgery frontier is reviewed. Finally, the paper concludes with a discussion on the constraints, benefits and open challenges associated with this new type of surgical technology.

Triple-clad W-type fiber mitigates multipath artifacts in multimodal optical coherence tomography.

Multimodal endoscopic optical coherence tomography (OCT) can be implemented with double-clad fiber by using the presumed single-mode core for OCT and the higher numerical aperture cladding for a secondary modality. However, the quality of OCT in double-clad fiber (DCF) based systems is compromised by the introduction of multipath artifacts that are nt present in single-mode fiber OCT systems. Herein, the mechanisms for multipath artifacts in DCF are linked to its modal contents using a commercial software package and experimental measurement. A triple-clad W-type fiber is proposed as a method for achieving multimodal imaging with single-mode quality OCT in an endoscopic system. Simulations of the modal contents of a W-type fiber are compared to DCF and single-mode fiber. Finally, a W-Type fiber rotary catheter is used in a DCF-based endoscopic OCT and autofluorescence imaging (AFI) system to demonstrate multipath artifact free OCT and AFI of a human fingertip.

Automatic intraluminal scanning with a steerable endoscopic optical coherence tomography catheter for gastroenterology applications

SignificanceEndoscopic optical coherence tomography (OCT) enables real-time optical biopsy of human organs. Endoscopic probes require miniaturization of optics, which in turn limits field of view. When larger imaging areas are needed such as in the gastrointestinal tract, the operator must manually scan the probe over the tissue to extend the field of view, often resulting in an imperfect scanning pattern and increased risk of missing lesions. Automatic scanning has the potential to extend the field of view of OCT, allowing the user to focus on image interpretation during real-time observations.AimThis work proposes an automatic scanning using a steerable OCT catheter integrated with a robotized interventional flexible endoscope. The aim is to extend the field of view of a low-profile OCT probe while improving scanning accuracy and maintaining a stable endoscope’s position during minimally invasive treatment of colorectal lesions.ApproachA geometrical model of the steerable OCT catheter was developed for estimating the volume of the accessible workspace. Experimental validation was done using electromagnetic tracking of the catheter’s positions. An exemplary scanning path was then selected within the available workspace to evaluate motion performance with the robotized steerable OCT catheter. Automatic scanning is compared to a teleoperated one and a manual scanning with a nonrobotized flexible endoscope. Spectral arc length, scanning area, spacing between scan trajectories, and time are metrics used to quantify performance.ResultsThe available scanning workspace was experimentally estimated to be 255 cm3. The automatic scanning mode provided the highest accuracy and smoothness of motion with spectral arc length of −3.18, covered area of 10.11 cm2, 1.54 mm spacing between 15 sweep trajectories, maximum translation of 27.99 mm, and time to finish of 3.11s.ConclusionsAutomatic modality improved the accuracy of scanning within a large workspace. The robotic capability provided better control to the user to define spacing resolution of scanning patterns.

Research Progress and Applications of Endoscopic Optical Coherence Tomography

Research Progress and Applications of Endoscopic Optical Coherence Tomography

Direct Visualization and Quantitative Imaging of Small Airway Anatomy In Vivo Using Deep Learning Assisted Diffractive OCT.

In vivo imaging and quantification of the microstructures of small airways in three dimensions (3D) allows a better understanding and management of airway diseases, such as asthma and chronic obstructive pulmonary disease (COPD). At present, the resolution and contrast of the currently available conventional optical coherence tomography (OCT) imaging technologies operating at 1300 nm remain challenging to directly visualize the fine microstructures of small airways in vivo. We developed an ultrahigh-resolution diffractive endoscopic OCT at 800 nm to afford a resolving power of 1.7 µm (in tissue) with an improved contrast and a custom deep residual learning based image segmentation framework to perform accurate and automated 3D quantification of airway anatomy. The 800-nm diffractive OCT enabled the direct delineation of the structural components in the small airway wall in vivo. We further first demonstrated the 3D anatomic quantification of critical tissue compartments of small airways in sheep using the automated segmentation method. The deep learning assisted diffractive OCT provides a unique ability to access the small airways, directly visualize and quantify the important tissue compartments, such as airway smooth muscle, in the airway wall in vivo in 3D. These pilot results suggest a potential technology for calculating volumetric measurements of small airways in patients in vivo.