Optical coherence tomography

Bichromatic tetraphasic full-field optical coherence microscopy

Significance

Full-field optical coherence microscopy (FF-OCM) is a prevalent technique for backscattering and phase imaging with epi-detection. Traditional methods have two limitations: suboptimal utilization of functional information about the sample and complicated optical design with several moving parts for phase contrast.

Aim

We report an OCM setup capable of generating dynamic intensity, phase, and pseudo-spectroscopic contrast with single-shot full-field video-rate imaging called bichromatic tetraphasic (BiTe) full-field OCM with no moving parts.

Approach

BiTe OCM resourcefully uses the phase-shifting properties of anti-reflection (AR) coatings outside the rated bandwidths to create four unique phase shifts, which are detected with two emission filters for spectroscopic contrast.

Results

BiTe OCM overcomes the disadvantages of previous FF-OCM setup techniques by capturing both the intensity and phase profiles without any artifacts or speckle noise for imaging scattering samples in three-dimensional (3D). BiTe OCM also utilizes the raw data effectively to generate three complementary contrasts: intensity, phase, and color. We demonstrate BiTe OCM to observe cellular dynamics, image live, and moving micro-animals in 3D, capture the spectroscopic hemodynamics of scattering tissues along with dynamic intensity and phase profiles, and image the microstructure of fall foliage with two different colors.

Conclusions

BiTe OCM can maximize the information efficiency of FF-OCM while maintaining overall simplicity in design for quantitative, dynamic, and spectroscopic characterization of biological samples.

The impact of statins treatments for plaque characteristics in stable angina pectoris patients with very low and high low-density lipoprotein cholesterol levels: an intracoronary optical coherence tomography study

Low-density lipoprotein cholesterol (LDL-C) levels are recommended according to the patient's risk factors based on guidelines. In patients achieving low LDL-C levels, the need for statins is uncertain, and the plaque characteristics of patients not treated with statins are unclear. In addition, the difference in plaque characteristics with and without statins is unclear in similarly high LDL levels. We evaluate the impact of statins on plaque characteristics on optical coherence tomography (OCT) in patients with very low LDL-C levels and high LDL-C levels. A total of 173 stable angina pectoris patients with 173 lesions undergoing OCT before percutaneous coronary intervention were evaluated. We divided the LDL-C levels into three groups: < 70 mg/dL (n = 48), 70 mg/dL ≤ LDL-C < 100 mg/dL (n = 71), and ≥ 100 mg/dL (n = 54). Among patients with LDL-C < 70 mg/dL, patients not treated with statins showed a significantly higher C-reactive protein level (0.27 ± 0.22 mg/dL vs. 0.15 ± 0.19 mg/dL, p = 0.049), and higher incidence of thin-cap fibroatheromas (TCFAs; 44% [7/16] vs. 13% [4/32], p = 0.021) than those treated with statins. Among patients with LDL-C level ≥ 100 mg/dL, patients treated with statins showed a significantly higher prevalence of familial hypercholesterolemia (FH) (38% [6/16] vs. 5% [2/38], p = 0.004), lower incidence of TCFAs (6% [1/16] vs. 39% [15/38], p = 0.013), healed plaques (13% [2/16] vs. 47% [18/38], p = 0.015), and higher incidence of fibrous plaques (75% [12/16] vs. 42% [16/38], p = 0.027) than patients not treated with statins. While patients achieved a low LDL-C, patients not treated with statins had high plaque vulnerability and high systemic inflammation. While patients had a high LDL-C level with a high prevalence of FH, patients treated with statins had stable plaque characteristics.

Choroidal Layer Analysis in OCT images via Ambiguous Boundary-aware Attention

Optical Coherence Tomography (OCT) is a commonly used retina imaging technique, and it is capable of revealing the morphology of the choroid. However, the segmentation and quantitative analysis of the sublayers and vessels in choroid are rarely explored, primarily due to the indistinct boundaries of choroidal sublayers, and imbalanced distribution of vessels observed in OCT imagery. In this paper, we propose a novel two-stage architecture called Choroidal Layer Analysis network (CLA), that may be considered the first attempt in this research community for joint segmentation of choroidal sublayers and choroidal vessels in OCT images. CLA employs the encoder-decoder network with the residual U-shape module as the backbone. In order to empower the ability of the segmentation model to identify the inconspicuous boundaries of choroidal sublayers, we introduce an Ambiguous Boundary Attention block (ABA) into the bottleneck of the encoder-decoder network in the first stage. For more accurate segmentation of large choroidal vessels with ambiguous contours and imbalanced spatial distribution, the second stage introduces an active contour-based loss to refine the contours of choroidal vessels simultaneously with precise identification of each vessel via contextual modeling. To train, test and validate the proposed model, we conducted a choroidal segmentation dataset containing 800 OCT images, with their sublayers and large choroidal vessels manually annotated. Experimental results demonstrate the superiority of the proposed approach compared with other state-of-the-art segmentation networks in large margins. It is worth noting that we also reconstructed the large choroidal vessels in three-dimensional (3D) based on the segmentation results, and multiple 3D morphological parameters were calculated. The statistical analysis of these parameters demonstrates significant differences between the healthy control and high myopia group, and this further confirms the proposed work may facilitate subsequent disease understanding and clinical decision-making.

Coaxial Bright and Dark Field Optical Coherence Tomography

To improve the signal collection efficiency of Optical Coherence Tomography (OCT) for biomedical applications. A novel coaxial optical design was implemented, utilizing a wavefront-division beam splitter in the sample arm with a 45-degree rod mirror. This design allowed for the simultaneous collection of bright and dark field signals. The bright field signal was detected within its circular aperture in a manner similar to standard OCT, while the dark field signal passed through an annular-shaped aperture and was collected by the same spectrometer via a fiber array. This new configuration improved the signal collection efficiency by ∼3 dB for typical biological tissues. Dark-field OCT images were found to provide higher resolution, contrast and distinct information compared to standard bright-field OCT. By compounding bright and dark field images, speckle noise was suppressed by ∼ √2 . These advantages were validated using Teflon phantoms, chicken breast ex vivo, and human skin in vivo. This new OCT configuration significantly enhances signal collection efficiency and image quality, offering great potential for improving OCT technology with better depth, contrast, resolution, speckles, and signal-to-noise ratio. We believe that the bright and dark field signals will enable more comprehensive tissue characterization with the angled scattered light. This advancement will greatly promote the OCT technology in various clinical and biomedical research applications.

Automatic exudate and aneurysm segmentation in OCT images using UNET++ and hyperreflective-foci feature based bagged tree ensemble

Diabetic retinopathy's signs, such as exudates (EXs) and aneurysms (ANs), initially develop from under the retinal surface detectable from optical coherence tomography (OCT) images. Detecting these signs helps ophthalmologists diagnose DR sooner. Detecting and segmenting exudates (EXs) and aneurysms (ANs) in medical images is challenging due to their small size, similarity to other hyperreflective regions, noise presence, and low background contrast. Furthermore, the scarcity of public OCT images featuring these abnormalities has limited the number of studies related to the automatic segmentation of EXs and ANs, and the reported performance of such studies has not been satisfactory. This work proposes an efficient algorithm that can automatically segment these anomalies by improving key steps in the process. The potential area where these hyper-reflective EXs and ANs occur was scoped by our method using a deep-learning U-Net++ program. From this area, the candidates for EX-AN were segmented using the adaptive thresholding method. Nine features based on appearances, locations, and shadow markers were extracted from these candidates. They were trained and tested using bagged tree ensemble classifiers to obtain only EX-AN blobs. The proposed method was tested on a collection of a public dataset comprising 80 images with hand-drawn ground truths. The experimental results showed that our method could segment EX-AN blobs with average recall, precision, and F1-measure as 87.9%, 86.1%, and 87.0%, respectively. Its F1-measure drastically outperformed two comparative methods, binary thresholding and watershed (BT-WS) and adaptive thresholding with shadow tracking (AT-ST), by 78.0% and 82.1%, respectively.

Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning

Evaluation of skin recovery is an important step in the treatment of burns. However, conventional methods only observe the surface of the skin and cannot quantify the injury volume. Optical coherence tomography (OCT) is a non-invasive, non-contact, real-time technique. Swept source OCT uses near infrared light and analyzes the intensity of light echo at different depths to generate images from optical interference signals. To quantify the dynamic recovery of skin burns over time, laser induced skin burns in mice were evaluated using deep learning of Swept source OCT images. A laser-induced mouse skin thermal injury model was established in thirty Kunming mice, and OCT images of normal and burned areas of mouse skin were acquired at day 0, day 1, day 3, day 7, and day 14 after laser irradiation. This resulted in 7000 normal and 1400 burn B-scan images which were divided into training, validation, and test sets at 8:1.5:0.5 ratio for the normal data and 8:1:1 for the burn data. Normal images were manually annotated, and the deep learning U-Net model (verified with PSPNe and HRNet models) was used to segment the skin into three layers: the dermal epidermal layer, subcutaneous fat layer, and muscle layer. For the burn images, the models were trained to segment just the damaged area. Three-dimensional reconstruction technology was then used to reconstruct the damaged tissue and calculate the damaged tissue volume. The average IoU value and f-score of the normal tissue layer U-Net segmentation model were 0.876 and 0.934 respectively. The IoU value of the burn area segmentation model reached 0.907 and f-score value reached 0.951. Compared with manual labeling, the U-Net model was faster with higher accuracy for skin stratification. OCT and U-Net segmentation can provide rapid and accurate analysis of tissue changes and clinical guidance in the treatment of burns.

Inferior alveolar nerve injury resulting from different implant rotary instruments: An ex vivo comparative study in human cadaveric mandibles

The present study aimed to evaluate the degree of nerve injury on inferior alveolar nerve (IAN) by different implant drills resulting from direct canal intrusion into inferior alveolar canal (IAC). A cadaveric study involving 7 human mandibles was performed to evaluate mechanical injury of canal enclosed IAN resulting from different drills. In group 1, osteotomies were created using different drills with 1 mm of intracanal intrusion, simulating accidental drill intrusion into canal. In group 2, drilling was stopped when the tip has breached into IAC, limited by tactile feedback of operator. The depth and width of nerve defects were assessed using optical coherence tomography. A significant difference in defect depth was found (p < 0.001) in group 1. A sinus lift reamer inflicted the least damage (0.068 ± 0.022 mm). It was also found that the mean defect depth was significantly different when a twist drill was used (p = 0.016). Sinus lift reamer can be used safely for osteotomy preparation in mandible when bone height is limited or when radiographic visualization of canal is poor. Bone corticalization around IAC does not provide adequate protection for IAN in the event of accidental intracanal intrusion.

Tissue Elasticity as a Diagnostic Marker of Molecular Mutations in Morphologically Heterogeneous Colorectal Cancer

The presence of molecular mutations in colorectal cancer (CRC) is a decisive factor in selecting the most effective first-line therapy. However, molecular analysis is routinely performed only in a limited number of patients with remote metastases. We propose to use tissue stiffness as a marker of the presence of molecular mutations in CRC samples. For this purpose, we applied compression optical coherence elastography (C-OCE) to calculate stiffness values in regions corresponding to specific CRC morphological patterns (n = 54). In parallel to estimating stiffness, molecular analysis from the same zones was performed to establish their relationships. As a result, a high correlation between the presence of KRAS/NRAS/BRAF driver mutations and high stiffness values was revealed regardless of CRC morphological pattern type. Further, we proposed threshold stiffness values for label-free targeted detection of molecular alterations in CRC tissues: for KRAS, NRAS, or BRAF driver mutation-above 803 kPa (sensitivity-91%; specificity-80%; diagnostic accuracy-85%), and only for KRAS driver mutation-above 850 kPa (sensitivity-90%; specificity-88%; diagnostic accuracy-89%). To conclude, C-OCE estimation of tissue stiffness can be used as a clinical diagnostic tool for preliminary screening of genetic burden in CRC tissues.

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.

Analysis of 3D pathology samples using weakly supervised AI

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Glaucoma detection using non-perfused areas in OCTA

Multiple ophthalmic diseases lead to decreased capillary perfusion that can be visualized using optical coherence tomography angiography images. To quantify the decrease in perfusion, past studies have often used the vessel density, which is the percentage of vessel pixels in the image. However, this method is often not sensitive enough to detect subtle changes in early pathology. More recent methods are based on quantifying non-perfused or intercapillary areas between the vessels. These methods rely upon the accuracy of vessel segmentation, which is a challenging task and therefore a limiting factor for reliability. Intercapillary areas computed from perfusion-distance measures are less sensitive to errors in the vessel segmentation since the distance to the next vessel is only slightly changing if gaps are present in the segmentation. We present a novel method for distinguishing between glaucoma patients and healthy controls based on features computed from the probability density function of these perfusion-distance areas. The proposed approach is evaluated on different capillary plexuses and outperforms previously proposed methods that use handcrafted features for classification. Moreover the results of the proposed method are in the same range as the ones of convolutional neural networks trained on the raw input images and is therefore a computationally efficient, simple to implement and explainable alternative to deep learning-based approaches.

K-Space Approach in Optical Coherence Tomography: Rigorous Digital Transformation of Arbitrary-Shape Beams, Aberration Elimination and Super-Refocusing beyond Conventional Phase Correction Procedures

For the most popular method of scan formation in Optical Coherence Tomography (OCT) based on plane-parallel scanning of the illuminating beam, we present a compact but rigorous K-space description in which the spectral representation is used to describe both the axial and lateral structure of the illuminating/received OCT signals. Along with the majority of descriptions of OCT-image formation, the discussed approach relies on the basic principle of OCT operation, in which ballistic backscattering of the illuminating light is assumed. This single-scattering assumption is the main limitation, whereas in other aspects, the presented approach is rather general. In particular, it is applicable to arbitrary beam shapes without the need for paraxial approximation or the assumption of Gaussian beams. The main result of this study is the use of the proposed K-space description to analytically derive a filtering function that allows one to digitally transform the initial 3D set of complex-valued OCT data into a desired (target) dataset of a rather general form. An essential feature of the proposed filtering procedures is the utilization of both phase and amplitude transformations, unlike conventionally discussed phase-only transformations. To illustrate the efficiency and generality of the proposed filtering function, the latter is applied to the mutual transformation of non-Gaussian beams and to the digital elimination of arbitrary aberrations at the illuminating/receiving aperture. As another example, in addition to the conventionally discussed digital refocusing enabling depth-independent lateral resolution the same as in the physical focus, we use the derived filtering function to perform digital "super-refocusing." The latter does not yet overcome the diffraction limit but readily enables lateral resolution several times better than in the initial physical focus.

Optical coherence tomography choroidal enhancement using generative deep learning

Spectral-domain optical coherence tomography (SDOCT) is the gold standard of imaging the eye in clinics. Penetration depth with such devices is, however, limited and visualization of the choroid, which is essential for diagnosing chorioretinal disease, remains limited. Whereas swept-source OCT (SSOCT) devices allow for visualization of the choroid these instruments are expensive and availability in praxis is limited. We present an artificial intelligence (AI)-based solution to enhance the visualization of the choroid in OCT scans and allow for quantitative measurements of choroidal metrics using generative deep learning (DL). Synthetically enhanced SDOCT B-scans with improved choroidal visibility were generated, leveraging matching images to learn deep anatomical features during the training. Using a single-center tertiary eye care institution cohort comprising a total of 362 SDOCT-SSOCT paired subjects, we trained our model with 150,784 images from 410 healthy, 192 glaucoma, and 133 diabetic retinopathy eyes. An independent external test dataset of 37,376 images from 146 eyes was deployed to assess the authenticity and quality of the synthetically enhanced SDOCT images. Experts' ability to differentiate real versus synthetic images was poor (47.5% accuracy). Measurements of choroidal thickness, area, volume, and vascularity index, from the reference SSOCT and synthetically enhanced SDOCT, showed high Pearson's correlations of 0.97 [95% CI: 0.96-0.98], 0.97 [0.95-0.98], 0.95 [0.92-0.98], and 0.87 [0.83-0.91], with intra-class correlation values of 0.99 [0.98-0.99], 0.98 [0.98-0.99], and 0.95 [0.96-0.98], 0.93 [0.91-0.95], respectively. Thus, our DL generative model successfully generated realistic enhanced SDOCT data that is indistinguishable from SSOCT images providing improved visualization of the choroid. This technology enabled accurate measurements of choroidal metrics previously limited by the imaging depth constraints of SDOCT. The findings open new possibilities for utilizing affordable SDOCT devices in studying the choroid in both healthy and pathological conditions.

Creating realistic anterior segment optical coherence tomography images using generative adversarial networks

AIMS

To develop a generative adversarial network (GAN) capable of generating realistic high-resolution anterior segment optical coherence tomography (AS-OCT) images.

METHODS

This study included 142 628 AS-OCT B-scans from the American University of Beirut Medical Center. The Style and WAvelet based GAN architecture was trained to generate realistic AS-OCT images and was evaluated through the Fréchet Inception Distance (FID) Score and a blinded assessment by three refractive surgeons who were asked to distinguish between real and generated images. To assess the suitability of the generated images for machine learning tasks, a convolutional neural network (CNN) was trained using a dataset of real and generated images over a classification task. The generated AS-OCT images were then upsampled using an enhanced super-resolution GAN (ESRGAN) to achieve high resolution.

RESULTS

The generated images exhibited visual and quantitative similarity to real AS-OCT images. Quantitative similarity assessed using FID scored an average of 6.32. Surgeons scored 51.7% in identifying real versus generated images which was not significantly better than chance (p value >0.3). The CNN accuracy improved from 78% to 100% when synthetic images were added to the dataset. The ESRGAN upsampled images were objectively more realistic and accurate compared with traditional upsampling techniques by scoring a lower Learned Perceptual Image Patch Similarity of 0.0905 compared with 0.4244 of bicubic interpolation.

CONCLUSIONS

This study successfully developed and leveraged GANs capable of generating high-definition synthetic AS-OCT images that are realistic and suitable for machine learning and image analysis tasks.

Study of dispersion extraction for optical coherence tomography imaging

Dispersion mismatch of optical coherence tomography (OCT), caused by inherent dispersion of biological sample, leads to axial resolution degradation and image blurring. However, the dispersion of biological samples may carry characteristic information of itself. Therefore, it is useful and important to extract dispersion as an additional parameter of biological samples. Here, we study and compare five categories of conventional OCT dispersion extraction methods first. The results show that the iterative method has the best accuracy and stability. Furthermore, we propose an improved iterative method, which achieves higher accuracy and stability in simulation compared with conventional methods. Then, the dispersion values of optical glasses are extracted experimentally utilizing the proposed method. The extracted values are consistent well with reference values. Furthermore, we apply the proposed method for extracting the dispersion characteristics of zebrafish eye. With the proposed method, OCT-based analysis of dispersion characteristics could be a powerful tool in clinical practice.

A depth-resolved quantitative evaluation method for non-carious cervical lesions treatment with optical coherence tomography

OBJECTIVES

The aim of this study is prognostic assessment of surface smoothness and the presence of internal bubbles after treatment of non-cancerous cervical lesions (NCCLs) using optical coherence tomography (OCT).

METHODS

After treatment with NCCLs, cross-sectional images of the lesion parts of the sample were non-invasively acquired and analyzed. The surface smoothness between tooth and resin, resin and cemento-enamel junction, and the presence bubble inside resin was confirmed. In addition, using an algorithm that distinguishes between resin and dental structure based on OCT cross-sectional images, we quantitatively analyzed the amount of resin used in treating NCCLs and acquired 3D images.

RESULTS

The inner structure of the resin in each sample was checked, and the presence of bubbles was confirmed. In addition, the resin sections were separated from the tomographic images acquired by OCT to visualize 3D images. The volume of resin used in the treatment part of each NCCLs samples was quantitatively analyzed as 3.7216 ∼ 14.889 mm3.

CONCLUSIONS

OCT is able to measure not only the surface abrasion provided by existing intraoral scanner, but also the size and depth location of interal bubbles, which is distinctive advantage of our method. Based on our results, OCT is a significant tool for qualitative and quantitative analysis of dental NCCLs treatment before and after treatment.

CLINICAL SIGNIFICANCE

The study used OCT, a non-destructive diagnostic, to reveal the structure of the resin and the location and size of bubbles after NCCLs treatment. These findings could be golden standard in determining the prognosis of NCCLs treatment.

Glucose tolerance and insulin resistance/sensitivity associate with retinal layer characteristics: the LIFE-Adult-Study

AIMS/HYPOTHESIS

As the prevalence of insulin resistance and glucose intolerance is increasing throughout the world, diabetes-induced eye diseases are a global health burden. We aim to identify distinct optical bands which are closely related to insulin and glucose metabolism, using non-invasive, high-resolution spectral domain optical coherence tomography (SD-OCT) in a large, population-based dataset.

METHODS

The LIFE-Adult-Study randomly selected 10,000 participants from the population registry of Leipzig, Germany. Cross-sectional, standardised phenotyping included the assessment of various metabolic risk markers and ocular imaging, such as SD-OCT-derived thicknesses of ten optical bands of the retina. Global and Early Treatment Diabetic Retinopathy Study (ETDRS) subfield-specific optical retinal layer thicknesses were investigated in 7384 healthy eyes of 7384 participants from the LIFE-Adult-Study stratified by normal glucose tolerance, prediabetes (impaired fasting glucose and/or impaired glucose tolerance and/or HbA1c 5.7-6.4% [39-47 mmol/mol]) and diabetes. The association of optical retinal band characteristics with different indices of glucose tolerance (e.g. fasting glucose, area under the glucose curve), insulin resistance (e.g. HOMA2-IR, triglyceride glucose index), or insulin sensitivity (e.g. estimated glucose disposal rate [eGDR], Stumvoll metabolic clearance rate) was determined using multivariable linear regression analyses for the individual markers adjusted for age, sex and refraction. Various sensitivity analyses were performed to validate the observed findings.

RESULTS

In the study cohort, nine out of ten optical bands of the retina showed significant sex- and glucose tolerance-dependent differences in band thicknesses. Multivariable linear regression analyses revealed a significant, independent, and inverse association between markers of glucose intolerance and insulin resistance (e.g. HOMA2-IR) with the thickness of the optical bands representing the anatomical retinal outer nuclear layer (ONL, standardised β=-0.096; p<0.001 for HOMA2-IR) and myoid zone (MZ; β=-0.096; p<0.001 for HOMA2-IR) of the photoreceptors. Conversely, markers of insulin sensitivity (e.g. eGDR) positively and independently associated with ONL (β=0.090; p<0.001 for eGDR) and MZ (β=0.133; p<0.001 for eGDR) band thicknesses. These global associations were confirmed in ETDRS subfield-specific analyses. Sensitivity analyses further validated our findings when physical activity, neuroanatomical cell/tissue types and ETDRS subfield categories were investigated after stratifying the cohort by glucose homeostasis.

CONCLUSIONS/INTERPRETATION

An impaired glucose homeostasis associates with a thinning of the optical bands of retinal ONL and photoreceptor MZ. Changes in ONL and MZ thicknesses might predict early metabolic retinal alterations in diabetes.

Acute alcohol consumption modulates corneal biomechanical properties as revealed by optical coherence elastography

Acute alcohol ingestion has been found to impact visual functions, including eye movement, but its effects on corneal biomechanical properties remain unclear. This study aimed to investigate the influence of acute alcohol consumption on corneal biomechanical properties using optical coherence elastography (OCE). An air-coupled ultrasound transducer induced elastic waves in mice corneas in vivo, and a high-resolution phase-sensitive optical coherence tomography (OCT) system tracked the mechanical waves to quantify the elastic wave speed. In vivo measurements were performed on three groups of age- and gender-matched mice: control, placebo (administered saline), and alcohol (administered ethanol) groups. Longitudinal measurements were conducted over a one-hour period to assess acute temporal changes in wave speeds, which are associated with inherent biomechanical properties of the cornea. The results showed a significant decrease in wave speed for the alcohol group after 10 min of ingestion in comparison to pre-ingestion values (p = 0.0096), whereas the temporal wave speed changes for the placebo group were statistically insignificant (p = 0.076). In contrast, the control group showed no significant changes in elastic wave speed and corneal thickness. Furthermore, a significant difference was observed between the wave speeds of the placebo and alcohol groups at each measurement time point between 10 and 50 min (p < 0.05), though both groups exhibited a similar trend in corneal thickness change. The findings of this study have important implications for clinical assessments and research in corneal disorders, highlighting the potential of OCE as a valuable tool for evaluating such changes.

Retinal changes in generalized anxiety disorder patients

OBJECTIVE

Optical coherence tomography (OCT) is a method that allows high-resolution cross-sectional imaging of biological tissues. It was suggested that changes in the cranial structure or functions would be reflected in the retina. OCT has been an important method in the diagnosis and follow-up of diseases via morphometric or quantitative retinal measurements. Free radicals, inflammatory processes, and neurotransmission disorders play a role in the etiology of generalized anxiety disorder (GAD). The study aimed to demonstrate the retinal changes in GAD patients due to neurodegeneration based on the comparison of the OCT data of the GAD patients and controls, and the differences between OCT findings of GAD patients and those of controls.

METHODS

The study group included 21 GAD patients. The control group included 21 individuals without any known psychiatric or organic disease, including eye diseases.

RESULTS

There was a statistically significant difference between the macular volumes (MV) of the GAD and control groups, the macular volume was lower in the GAD group. There were positive correlations between BDI scores and MV, GCLT, RNFLT-i, RNFLT-n, between BAE scores and (RNFLT-n), and between the CGI severity scale scores and MV, RNFLT-n, and RNFLT-t.

CONCLUSION

OCT analysis of the GAD patients demonstrated that MV values were lower when compared to the control group. Patients with GAD should be screened for these retinal changes. OCT, a simple, non-invasive, and relatively inexpensive method could be employed as a supplementary method in the follow-up of GAD patients.

Artificial Intelligence for Multiple Sclerosis Management Using Retinal Images: Pearl, Peaks, and Pitfalls

Multiple sclerosis (MS) is a complex autoimmune disease characterized by inflammatory processes, demyelination, neurodegeneration, and axonal damage within the central nervous system (CNS). Retinal imaging, particularly Optical coherence tomography (OCT), has emerged as a crucial tool for investigating MS-related retinal injury. The integration of artificial intelligence(AI) has shown promise in enhancing OCT analysis for MS. Researchers are actively utilizing AI algorithms to accurately detect and classify MS-related abnormalities, leading to improved efficiency in diagnosis, monitoring, and personalized treatment planning. The prognostic value of AI in predicting MS disease progression has garnered substantial attention. Machine learning (ML) and deep learning (DL) algorithms can analyze longitudinal OCT data to forecast the course of the disease, providing critical information for personalized treatment planning and improved patient outcomes. Early detection of high-risk patients allows for targeted interventions to mitigate disability progression effectively. As such, AI-driven approaches yielded remarkable abilities in classifying distinct MS subtypes based on retinal features, aiding in disease characterization and guiding tailored therapeutic strategies. Additionally, these algorithms have enhanced the accuracy and efficiency of OCT image segmentation, streamlined diagnostic processes, and reduced human error. This study reviews the current research studies on the integration of AI,including ML and DL algorithms, with OCT in the context of MS. It examines the advancements, challenges, potential prospects, and ethical concerns of AI-powered techniques in enhancing MS diagnosis, monitoring disease progression, revolutionizing patient care, the development of patient screening tools, and supported clinical decision-making based on OCT images.

Diagnosis and therapy of actinic keratosis

Actinic keratosis (AK) is considered a chronic and recurring in situ skin neoplasia, with a possible transformation into invasive squamous cell carcinoma (SCC). Among others, predominant risk factors for development of AK are UV-light exposure and immunosuppression. Basal epidermal keratinocyte atypia (AK I) and proliferation (PRO score) seem to drive malignant transformation, rather than clinical appearance of AK (Olsen I-III). Due to the invasiveness of punch biopsy, those histological criteria are not regularly assessed. Non-invasive imaging techniques, such as optical coherence tomography (OCT), reflectance confocal microscopy (RCM) and line-field confocal OCT (LC-OCT) are helpful to distinguish complex cases of AK, Bowen's disease, and SCC. Moreover, LC-OCT can visualize the epidermis and the papillary dermis at cellular resolution, allowing real-time PRO score assessment. The decision-making for implementation of therapy is still based on clinical risk factors, ranging from lesion- to field-targeted and ablative to non-ablative regimens, but in approximately 85% of the cases a recurrence of AK can be observed after a 1-year follow-up. The possible beneficial use of imaging techniques for a non-invasive follow-up of AK to detect recurrence or invasive progression early on should be subject to critical evaluation in further studies.

Diagnose und Therapie aktinischer Keratosen

Actinic keratosis (AK) is considered a chronic and recurring in situ skin neoplasia, with a possible transformation into invasive squamous cell carcinoma (SCC). Among others, predominant risk factors for development of AK are UV-light exposure and immunosuppression. Basal epidermal keratinocyte atypia (AK I) and proliferation (PRO Score) seem to drive malignant turnover, rather than clinical appearance of AK (Olsen I-III). Due to the invasiveness of punch biopsy, those histological criteria are not regularly assessed. Non-invasive imaging techniques, such as optical coherence tomography (OCT), reflectance confocal microscopy (RCM) and line-field confocal OCT (LC-OCT) are helpful to distinguish complex cases of AK, Bowen's disease and SCC. Moreover, LC-OCT can visualize the epidermis and the papillary dermis at cellular resolution, allowing real-time PRO Score assessment. The decision-making for implementation of therapy is still based on clinical risk factors, ranging from lesion- to field-targeted and ablative to non-ablative regimes, but in approximately 85% of the cases a recurrence of AK can be observed after a 1-year follow-up. The possible beneficial use of imaging techniques for a non-invasive follow-up of AK to detect recurrence or invasive progression early on should be subject to critical evaluation in further studies.

Coronary plaque phenotype associated with positive remodeling

BACKGROUND

Positive remodeling is an integral part of the vascular adaptation process during the development of atherosclerosis, which can be detected by coronary computed tomography angiography (CTA).

METHODS

A total of 426 patients who underwent both coronary CTA and optical coherence tomography (OCT) were included. Four machine learning (ML) models, gradient boosting machine (GBM), random forest (RF), deep learning (DL), and support vector machine (SVM), were employed to detect specific plaque features. A total of 15 plaque features assessed by OCT were analyzed. The variable importance ranking was used to identify the features most closely associated with positive remodeling.

RESULTS

In the variable importance ranking, lipid index and maximal calcification arc were consistently ranked high across all four ML models. Lipid index and maximal calcification arc were correlated with positive remodeling, showing pronounced influence at the lower range and diminishing influence at the higher range. Patients with more plaques with positive remodeling throughout their entire coronary trees had higher low-density lipoprotein cholesterol levels and were associated with a higher incidence of cardiovascular events during 5-year follow-up (Hazard ratio 2.10 [1.26-3.48], P ​= ​0.004).

CONCLUSION

Greater lipid accumulation and less calcium burden were important features associated with positive remodeling in the coronary arteries. The number of coronary plaques with positive remodeling was associated with a higher incidence of cardiovascular events.

Growing Trend to Adopt Speckle Variance Optical Coherence Tomography for Biological Tissue Assessments in Pre-Clinical Applications

Speckle patterns are a generic feature in coherent imaging techniques like optical coherence tomography (OCT). Although speckles are granular like noise texture, which degrades the image, they carry information that can be benefited by processing and thereby furnishing crucial information of sample structures, which can serve to provide significant important structural details of samples in in vivo longitudinal pre-clinical monitoring and assessments. Since the motions of tissue molecules are indicated through speckle patterns, speckle variance OCT (SV-OCT) can be well-utilized for quantitative assessments of speckle variance (SV) in biological tissues. SV-OCT has been acknowledged as a promising method for mapping microvasculature in transverse-directional blood vessels with high resolution in micrometers in both the transverse and depth directions. The fundamental scope of this article reviews the state-of-the-art and clinical benefits of SV-OCT to assess biological tissues for pre-clinical applications. In particular, focus on precise quantifications of in vivo vascular response, therapy assessments, and real-time temporal vascular effects of SV-OCT are primarily emphasized. Finally, SV-OCT-incorporating pre-clinical techniques with high potential are presented for future biomedical applications.

Optical Coherence Tomography in Inherited Macular Dystrophies: A Review

Macular dystrophies (MDs) constitute a collection of hereditary retina disorders leading to notable visual impairment, primarily due to progressive macular atrophy. These conditions are distinguished by bilateral and relatively symmetrical abnormalities in the macula that significantly impair central visual function. Recent strides in fundus imaging, especially optical coherence tomography (OCT), have enhanced our comprehension and diagnostic capabilities for MD. OCT enables the identification of neurosensory retinal disorganization patterns and the extent of damage to retinal pigment epithelium (RPE) and photoreceptor cells in the dystrophies before visible macular pathology appears on fundus examinations. It not only helps us in diagnostic retinal and choroidal pathologies but also guides us in monitoring the progression of, staging of, and response to treatment. In this review, we summarize the key findings on OCT in some of the most common MD.

Quantitative Characterization of Retinal Features in Translated OCTA

Purpose

This study explores the feasibility of using generative machine learning (ML) to translate Optical Coherence Tomography (OCT) images into Optical Coherence Tomography Angiography (OCTA) images, potentially bypassing the need for specialized OCTA hardware.

Methods

The method involved implementing a generative adversarial network framework that includes a 2D vascular segmentation model and a 2D OCTA image translation model. The study utilizes a public dataset of 500 patients, divided into subsets based on resolution and disease status, to validate the quality of TR-OCTA images. The validation employs several quality and quantitative metrics to compare the translated images with ground truth OCTAs (GT-OCTA). We then quantitatively characterize vascular features generated in TR-OCTAs with GT-OCTAs to assess the feasibility of using TR-OCTA for objective disease diagnosis.

Result

TR-OCTAs showed high image quality in both 3 and 6 mm datasets (high-resolution, moderate structural similarity and contrast quality compared to GT-OCTAs). There were slight discrepancies in vascular metrics, especially in diseased patients. Blood vessel features like tortuosity and vessel perimeter index showed a better trend compared to density features which are affected by local vascular distortions.

Conclusion

This study presents a promising solution to the limitations of OCTA adoption in clinical practice by using vascular features from TR-OCTA for disease detection.

Translation relevance

This study has the potential to significantly enhance the diagnostic process for retinal diseases by making detailed vascular imaging more widely available and reducing dependency on costly OCTA equipment.

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 (2.41 mm) to 25G (0.51 mm), 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.

Deep learning automatically assesses 2-µm laser-induced skin damage OCT images

The present study proposed a noninvasive, automated, in vivo assessment method based on optical coherence tomography (OCT) and deep learning techniques to qualitatively and quantitatively analyze the biological effects of 2-µm laser-induced skin damage at different irradiation doses. Different doses of 2-µm laser irradiation established a mouse skin damage model, after which the skin-damaged tissues were imaged non-invasively in vivo using OCT. The acquired images were preprocessed to construct the dataset required for deep learning. The deep learning models used were U-Net, DeepLabV3+, PSP-Net, and HR-Net, and the trained models were used to segment the damage images and further quantify the damage volume of mouse skin under different irradiation doses. The comparison of the qualitative and quantitative results of the four network models showed that HR-Net had the best performance, the highest agreement between the segmentation results and real values, and the smallest error in the quantitative assessment of the damage volume. Based on HR-Net to segment the damage image and quantify the damage volume, the irradiation doses 5.41, 9.55, 13.05, 20.85, 32.71, 52.92, 76.71, and 97.24 J/cm² corresponded to a damage volume of 4.58, 12.56, 16.74, 20.88, 24.52, 30.75, 34.13, and 37.32 mm³. The damage volume increased in a radiation dose-dependent manner.

Machine learning and optical coherence tomography-derived radiomics analysis to predict persistent diabetic macular edema in patients undergoing anti-VEGF intravitreal therapy

BACKGROUND

Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. This study aimed to develop and evaluate an OCT-omics prediction model for assessing anti-vascular endothelial growth factor (VEGF) treatment response in patients with DME.

METHODS

A retrospective analysis of 113 eyes from 82 patients with DME was conducted. Comprehensive feature engineering was applied to clinical and optical coherence tomography (OCT) data. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained using a training set of 79 eyes, and evaluated on a test set of 34 eyes. Clinical implications of the OCT-omics prediction model were assessed by decision curve analysis. Performance metrics (sensitivity, specificity, F1 score, and AUC) were calculated.

RESULTS

The logistic, SVM, and BPNN classifiers demonstrated robust discriminative abilities in both the training and test sets. In the training set, the logistic classifier achieved a sensitivity of 0.904, specificity of 0.741, F1 score of 0.887, and AUC of 0.910. The SVM classifier showed a sensitivity of 0.923, specificity of 0.667, F1 score of 0.881, and AUC of 0.897. The BPNN classifier exhibited a sensitivity of 0.962, specificity of 0.926, F1 score of 0.962, and AUC of 0.982. Similar discriminative capabilities were maintained in the test set. The OCT-omics scores were significantly higher in the non-persistent DME group than in the persistent DME group (p < 0.001). OCT-omics scores were also positively correlated with the rate of decline in central subfield thickness after treatment (Pearson's R = 0.44, p < 0.001).

CONCLUSION

The developed OCT-omics model accurately assesses anti-VEGF treatment response in DME patients. The model's robust performance and clinical implications highlight its utility as a non-invasive tool for personalized treatment prediction and retinal pathology assessment.

Using optical coherence tomography and optical coherence tomography angiography to delineate neurovascular homeostasis in migraine: a review

Migraine is one of the world's most debilitating disorders, and it has recently been shown that changes in the retina can be a potential biomarker for the disease. These changes can be detected by optical coherence tomography (OCT), which measures retinal thickness, and optical coherence tomography angiography (OCTA), which measures vessel density. We searched the databases Google Scholar, ProQuest, Scopus, and Web of Science for studies in English using OCT and OCTA in migraineurs, using the search terms "optical coherence tomography," "OCT," "optical coherence tomography angiography," "OCTA" and "migraine." We found 73 primary studies, 11 reviews, and 8 meta-analyses pertaining to OCT and OCTA findings in migraineurs. They showed that migraineurs had reduced retinal thickness (via OCT), retinal vessel density, and greater foveal avascular zone area (via OCTA) than controls. OCTA changes reflect a perfusion compromise occurring in migraineurs as opposed to in healthy controls. OCT and OCTA deficits were worse in migraine-with-aura and chronic migraine than in migraine-without-aura and episodic migraine. Certain areas of the eye, such as the fovea, may be more vulnerable to these perfusion changes than other parts. Direct comparison between study findings is difficult because of the heterogeneity between the studies in terms of both methodology and analysis. Moreover, as almost all case-control studies were cross-sectional, more longitudinal cohort studies are needed to determine cause and effect between migraine pathophysiology and OCT/OCTA findings. Current evidence suggests both OCT and OCTA may serve as retinal markers for migraineurs, and further research in this field will hopefully enable us to better understand the vascular changes associated with migraine, perhaps also providing a new diagnostic and therapeutic biomarker.

NaYF4:Ho3+/Yb3+@NaGdF4 core@shell upconversion nanoparticles for contrast enhancement in bimodal in-vitro OCT imaging

Contrast enhancement is explored in optical coherence tomography images using core NaYF4:Ho3+/Yb3+ and core@shell NaYF4:Ho3+/Yb3+@NaGdF4 nanoparticles. Under 980 nm excitation, core@shell nanoparticles exhibited 2.8 and 3.3 times enhancement at 541 nm and 646 nm emission wavelengths of Ho3+ ions compared to core nanoparticles. Photo-thermal conversion efficiencies were 32% and 20% for core and core@shell nanoparticles. In swept-source optical coherence tomography (SSOCT), core@shell nanoparticles have shown superior contrast, while in photo-thermal optical coherence tomography (PTOCT) core nanoparticles have excelled due to their higher photo-thermal conversion efficiency. The enhancement in contrast to noise ratio obtained is 58 dB. Comparative assessments of scattering coefficients and contrast-to-noise ratios were conducted, providing insights into nanoparticle performance for contrast enhancement in optical coherence tomography.

OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods

Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset.

FNeXter: A Multi-Scale Feature Fusion Network Based on ConvNeXt and Transformer for Retinal OCT Fluid Segmentation

The accurate segmentation and quantification of retinal fluid in Optical Coherence Tomography (OCT) images are crucial for the diagnosis and treatment of ophthalmic diseases such as age-related macular degeneration. However, the accurate segmentation of retinal fluid is challenging due to significant variations in the size, position, and shape of fluid, as well as their complex, curved boundaries. To address these challenges, we propose a novel multi-scale feature fusion attention network (FNeXter), based on ConvNeXt and Transformer, for OCT fluid segmentation. In FNeXter, we introduce a novel global multi-scale hybrid encoder module that integrates ConvNeXt, Transformer, and region-aware spatial attention. This module can capture long-range dependencies and non-local similarities while also focusing on local features. Moreover, this module possesses the spatial region-aware capabilities, enabling it to adaptively focus on the lesions regions. Additionally, we propose a novel self-adaptive multi-scale feature fusion attention module to enhance the skip connections between the encoder and the decoder. The inclusion of this module elevates the model's capacity to learn global features and multi-scale contextual information effectively. Finally, we conduct comprehensive experiments to evaluate the performance of the proposed FNeXter. Experimental results demonstrate that our proposed approach outperforms other state-of-the-art methods in the task of fluid segmentation.

Detection sensitivity of fluorescence lifetime imaging ophthalmoscopy for laser-induced selective damage of retinal pigment epithelium

PURPOSE

To investigate the sensitivity of fluorescence lifetime imaging ophthalmoscopy (FLIO) to detect retinal laser spots by comparative analysis with other imaging modalities.

METHODS

A diode laser with a wavelength of 514 nm was applied with pulse durations of 5.2, 12, 20, and 50 µs. The laser pulse energy was increased so that the visibility of the laser spot by slit-lamp fundus examination (SL) under the irradiator's observation covers from the subvisible to visible range immediately after irradiation. The irradiated areas were then examined by fundus color photography (FC), optical coherence tomography (OCT), fundus autofluorescence (AF), FLIO, and fluorescein angiography (FA). The visibility of a total of over 2200 laser spots was evaluated by two independent researchers, and effective dose (ED) 50 laser pulse energy values were calculated for each imaging modality and compared.

RESULTS

Among examined modalities, FA showed the lowest mean of ED50 energy value and SL the highest, that is, they had the highest and lowest sensitivity to detect retinal pigment epithalium (RPE)-selective laser spots, respectively. FLIO also detected spots significantly more sensitively than SL at most laser pulse durations and was not significantly inferior to FA. AF was also often more sensitive than SL, but the difference was slightly less significant than FLIO.

CONCLUSION

Considering its high sensitivity in detecting laser spots and previously reported potential of indicating local wound healing and metabolic changes around laser spots, FLIO may be useful as a non-invasive monitoring tool during and after minimally invasive retinal laser treatment.

Optical coherence tomography for multicellular tumor spheroid category recognition and drug screening classification via multi-spatial-superficial-parameter and machine learning

Optical coherence tomography (OCT) is an ideal imaging technique for noninvasive and longitudinal monitoring of multicellular tumor spheroids (MCTS). However, the internal structure features within MCTS from OCT images are still not fully utilized. In this study, we developed cross-statistical, cross-screening, and composite-hyperparameter feature processing methods in conjunction with 12 machine learning models to assess changes within the MCTS internal structure. Our results indicated that the effective features combined with supervised learning models successfully classify OVCAR-8 MCTS culturing with 5,000 and 50,000 cell numbers, MCTS with pancreatic tumor cells (Panc02-H7) culturing with the ratio of 0%, 33%, 50%, and 67% of fibroblasts, and OVCAR-4 MCTS treated by 2-methoxyestradiol, AZD1208, and R-ketorolac with concentrations of 1, 10, and 25 µM. This approach holds promise for obtaining multi-dimensional physiological and functional evaluations for using OCT and MCTS in anticancer studies.

Study of ischemic progression in different intestinal tissue layers during acute intestinal ischemia using swept-source optical coherence tomography angiography

In acute intestinal ischemia, the progression of ischemia varies across different layers of intestinal tissue. We established a mouse model and used swept-source optical coherence tomography (OCT) to observe the intestinal ischemic process longitudinally in different tissue layers. Employing a method that combines asymmetric gradient filtering with adaptive weighting, we eliminated the vessel trailing phenomenon in OCT angiograms, reducing the confounding effects of superficial vessels on the imaging of deeper vasculature. We quantitatively assessed changes in vascular perfusion density (VPD), vessel length, and vessel average diameter across various intestinal layers. Our results showed a significant reduction in VPD in all layers during ischemia. The mucosa layer experienced the most significant impact, primarily due to disrupted capillary blood flow, followed by the submucosa layer, where vascular constriction or decreased velocity was the primary factor.

Deep Multimodal Fusion of Data With Heterogeneous Dimensionality via Projective Networks

The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases. Similar to clinical practice, some works have demonstrated the benefits of multimodal fusion for automatic segmentation and classification using deep learning-based methods. However, current segmentation methods are limited to fusion of modalities with the same dimensionality (e.g., 3D + 3D, 2D + 2D), which is not always possible, and the fusion strategies implemented by classification methods are incompatible with localization tasks. In this work, we propose a novel deep learning-based framework for the fusion of multimodal data with heterogeneous dimensionality (e.g., 3D + 2D) that is compatible with localization tasks. The proposed framework extracts the features of the different modalities and projects them into the common feature subspace. The projected features are then fused and further processed to obtain the final prediction. The framework was validated on the following tasks: segmentation of geographic atrophy (GA), a late-stage manifestation of age-related macular degeneration, and segmentation of retinal blood vessels (RBV) in multimodal retinal imaging. Our results show that the proposed method outperforms the state-of-the-art monomodal methods on GA and RBV segmentation by up to 3.10% and 4.64% Dice, respectively.

Line-field confocal optical coherence tomography in dermato-oncology: A literature review towards harmonized histopathology-integrated terminology

Non-invasive diagnostics like line-field confocal optical coherence tomography (LC-OCT) are being implemented in dermato-oncology. However, unification of terminology in LC-OCT is lacking. By reviewing the LC-OCT literature in the field of dermato-oncology, this study aimed to develop a unified terminological glossary integrated with traditional histopathology. A PRISMA-guided literature-search was conducted for English-language publications on LC-OCT of actinic keratosis (AK), keratinocyte carcinoma (KC), and malignant melanoma (MM). Study characteristics and terminology were compiled. To harmonize LC-OCT terminology and integrate with histopathology, synonymous terms for image features of AK, KC, and MM were merged by two authors, organized by skin layer and lesion-type. A subset of key LC-OCT image-markers with histopathological correlates that in combination were typical of AK, squamous cell carcinoma in situ (SCCis), invasive squamous cell carcinoma (SCC), basal cell carcinoma (BCC), and MM in traditional histopathology, were selected from the glossary by an experienced dermatopathologist. Seventeen observational studies of AK (7 studies), KC (13 studies), MM (7 studies) utilizing LC-OCT were included, with 117 terms describing either AK, KC, or MM. These were merged to produce 45 merged-terms (61.5% reduction); 5 assigned to the stratum corneum (SC), 23 to the viable epidermis, 2 to dermo-epidermal junction (DEJ) and 15 to the dermis. For each lesion, mandatory key image-markers were a well-defined DEJ and presence of mild/moderate but not severe epidermal dysplasia for AK, severe epidermal dysplasia and well-defined DEJ for SCCis, interrupted DEJ and/or dermal broad infiltrative strands for invasive SCC, dermal lobules connected and/or unconnected to the epidermis for BCC, as well as single atypical melanocytes and/or nest of atypical melanocytes in the epidermis or dermis for MM. This review compiles evidence on LC-OCT in dermato-oncology, providing a harmonized histopathology-integrated terminology and key image-markers for each lesion. Further evaluation is required to determine the clinical value of these findings.

High-Risk Plaques on Coronary Computed Tomography Angiography: Correlation With Optical Coherence Tomography

BACKGROUND

Although patients with high-risk plaque (HRP) on coronary computed tomography angiography (CTA) are reportedly at increased risk for future cardiovascular events, individual HRP features have not been systematically validated against high-resolution intravascular imaging.

OBJECTIVES

The aim of this study was to correlate HRP features on CTA with plaque characteristics on optical coherence tomography (OCT).

METHODS

Patients who underwent both CTA and OCT before coronary intervention were enrolled. Plaques in culprit vessels identified by CTA were evaluated with the use of OCT at the corresponding sites. HRP was defined as a plaque with at least 2 of the following 4 features: positive remodeling (PR), low-attenuation plaque (LAP), napkin-ring sign (NRS), and spotty calcification (SC). Patients were followed for up to 3 years.

RESULTS

The study included 448 patients, with a median age of 67 years and of whom 357 (79.7%) were male, and 203 (45.3%) presented with acute coronary syndromes. A total of 1,075 lesions were analyzed. All 4 HRP features were associated with thin-cap fibroatheroma. PR was associated with all OCT features of plaque vulnerability, LAP was associated with lipid-rich plaque, macrophage, and cholesterol crystals, NRS was associated with cholesterol crystals, and SC was associated with microvessels. The cumulative incidence of the composite endpoint (target vessel nontarget lesion revascularization and cardiac death) was significantly higher in patients with HRP than in those without HRP (4.7% vs 0.5%; P = 0.010).

CONCLUSIONS

All 4 HRP features on CTA were associated with features of vulnerability on OCT. (Massachusetts General Hospital and Tsuchiura Kyodo General Hospital Coronary Imaging Collaboration; NCT04523194).

Scan-less 3D microscopy based on spatiotemporal encoding on a single-cavity dual-comb laser

Dual-comb microscopy enables high-speed and high-precision optical sampling by simultaneously extracting both amplitude and phase information from the interference signals with frequency division multiplexing. In this Letter, we introduce a spatiotemporal encoding approach for dual-comb microscopy that overcomes previous limitations such as mechanical scanning, low sampling efficiency, and system complexity. By employing free-space angular-chirp-enhanced delay (FACED) and a low-noise single-cavity dual-comb laser, we achieve scan-less 3D imaging with nanometer precision and a 3D distance-imaging rate of 330 Hz, restricted only by the repetition rate difference of the dual-comb laser. Specifically, the FACED unit linearly arranges the laser beam into an array. A grating subsequently disperses this array transversely into lines, facilitating ultrafast spectroscopic applications that are 1-2 orders of magnitude quicker than traditional dual-comb methods. This spatiotemporal encoding also eases the stringent conditions on various dual-comb laser parameters, such as repetition rates, coherence, and stability. Through carefully designed experiments, we demonstrate that our scan-less system can measure 3D profiles of microfabricated structures at a rate of 7 million pixels per second. Our method significantly enhances measurement speed while maintaining high precision, using a compact light source. This advancement has the potential for broad applications, including phase imaging, surface topography, distance ranging, and spectroscopy.

Atrophic Lesions Associated with Age-Related Macular Degeneration: High-Resolution versus Standard OCT

OBJECTIVE

The objective of this study was to determine whether high-resolution OCT (HR-OCT) could enhance the identification and classification of atrophic features in age-related macular degeneration (AMD) compared with standard resolution OCT.

DESIGN

Prospective, observational, cross-sectional study.

SUBJECTS

The study included 60 eyes from 60 patients > 60 years of age with a diagnosis of AMD.

METHODS

The participants underwent volume OCT scanning using HR-OCT and standard resolution OCT devices. Trained graders reviewed and graded the scans, identifying specific regions of interest for subsequent analysis.

MAIN OUTCOME MEASURES

The study focused on identifying and classifying complete retinal pigment epithelium (RPE) and outer retinal atrophy (cRORA), incomplete RORA (iRORA), and other nonatrophic AMD features. Additionally, qualitative and quantitative features associated with atrophy were assessed.

RESULTS

The agreement among readers for classifying atrophic lesions was substantial to perfect for both HR-OCT (0.88) and standard resolution OCT(0.82). However, HR-OCT showed a higher accuracy in identifying iRORA lesions compared with standard OCT. Qualitative assessment of features demonstrated higher agreement for HR-OCT, particularly in identifying external limiting membrane (ELM) (0.95) and ellipsoid zone (EZ) disruption (0.94). Quantitative measurements of features such as hypertransmission defects, RPE attenuation/disruption, EZ disruption width, and ELM disruption width showed excellent interreader agreement with HR-OCT (> 0.90 for all features) but only moderate agreement with standard OCT (0.51-0.60).

CONCLUSIONS

The study results suggest that HR-OCT improves the accuracy and reliability of classifying and quantifying atrophic lesions associated with AMD compared with standard resolution OCT. The quantitative findings in particular may have implications for future research and clinical practice, especially with the availability of therapeutic agents for treating geographic atrophy and the development of commercially available HR-OCT devices.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Clinical Utilisation of Wide-Field Optical Coherence Tomography and Angiography: A Narrative Review

Many important abnormalities of the vitreous, retina and choroid are predominantly located in the peripheral retina. In some retinal diseases with both central and peripheral manifestations, pathological structural or vascular changes can be apparent in the periphery before they are detectable in the central retina. Conventional optical coherence tomography (OCT) and optical coherence tomography angiography (OCT-A) imaging only cover the most posterior 30° of the retina. Wide-field OCT (WF-OCT), though offering detailed cross-sectional imaging of the peripheral retina, is not yet systematically used in clinical practice. This narrative review provides a presentation of the utilisation of WF-OCT and WF-OCT-A in the diagnosis and monitoring of a variety of ophthalmological diseases and discusses the advantages and limitations of the technology. With the rapidly developing technology, multiple WF-OCT and WF-OCT-A devices are now commercially available and enable the clinician to obtain scans within a field of view up to 200°. As detailed in this review, several studies have shown promising results in the application of WF-OCT and WF-OCT-A in diseases of the retina, choroid and vitreous, such as retinal vein occlusion, diabetic retinopathy, ocular oncology, paediatric ophthalmology, uveitis and lesions of the vitreo-retinal interface. In conclusion, WF-OCT and WF-OCT-A can reliably produce high-quality, non-invasive images of the vitreous, retinal, and choroidal structures and vascularity covering the posterior pole as well as the mid and far periphery. These methods can be a valuable part of a multimodal imaging approach in the management of a variety of ocular conditions. Future studies are warranted to investigate the patient outcome benefits of implementation of WF-OCT and WF-OCT-A imaging in a real-life clinical setting.

Revealing the clinical potential of high-resolution organoids

The symbiotic interplay of organoid technology and advanced imaging strategies yields innovative breakthroughs in research and clinical applications. Organoids, intricate three-dimensional cell cultures derived from pluripotent or adult stem/progenitor cells, have emerged as potent tools for in vitro modeling, reflecting in vivo organs and advancing our grasp of tissue physiology and disease. Concurrently, advanced imaging technologies such as confocal, light-sheet, and two-photon microscopy ignite fresh explorations, uncovering rich organoid information. Combined with advanced imaging technologies and the power of artificial intelligence, organoids provide new insights that bridge experimental models and real-world clinical scenarios. This review explores exemplary research that embodies this technological synergy and how organoids reshape personalized medicine and therapeutics.

High-precision Drosophila heart segmentation and dynamic cardiac parameter measurement for optogenetics-OCT-based cardiac function research

Drosophila model has been widely used to study cardiac functions, especially combined with optogenetics and optical coherence tomography (OCT) that can continuously acquire mass cross-sectional images of the Drosophila heart in vivo over time. It's urgent to quickly and accurately obtain dynamic Drosophila cardiac parameters such as heartbeat rate for cardiac function quantitative analysis through these mass cross-sectional images of the Drosophila heart. Here we present a deep-learning method that integrates U-Net and generative adversarial network architectures while incorporating residually connected convolutions for high-precision OCT image segmentation of Drosophila heart and dynamic cardiac parameter measurements for optogenetics-OCT-based cardiac function research. We compared our proposed network with the previous approaches and our segmentation results achieved the accuracy of intersection over union and Dice similarity coefficient higher than 98%, which can be used to better quantify dynamic heart parameters and improve the efficiency of Drosophila-model-based cardiac research via the optogenetics-OCT-based platform.

Imaging Assessment of Peripapillary Vessel Diameters in Postmortem Eyes

PURPOSE

Proof of concept of ex vivo retinal vessel diameter measurements in human postmortem eyes.

METHODS

En face near-infrared (IR) images and optical coherence tomography (OCT) of the optic nerve head (ONH) were captured ex vivo with a Heidelberg Engineering Spectralis (Spectralis, version 7.0.4, Image Capture Module, version 1.2.4, Heidelberg Heidelberg, Germany) device, using a custom-made eye chamber holding and positioning the eyes during the image process. Thirty-two formaldehyde-fixated eyes of 16 patients were imaged. In the IR images, two independent graders measured retinal vessel diameters at the intersection of a drawn circle centered on the ONH with diameters of 2.0 mm and 3.4 mm, respectively. The anatomically corresponding measurements between both graders were statistically analyzed using a Wilcoxon signed-rank test.

RESULTS

A total of 246 matched measurements of both graders were analyzed across all 32 imaged eyes. Statistically significant differences between the graders were found for arterioles at 2 mm from the ONH. The other measurements did not show statistically significant intergrader differences. The mean values for arteriole diameters were 72.2 µm at 2.0 mm and 61.5 µm at 3.4 mm for grader 1, and 66.4 µm at 2.0 mm and 63.2 µm at 3.4 mm for grader 2. The mean diameter for venules were 75.5 µm at 2.0 mm and 79.3 µm at 3.4 mm for grader 1, and 67.4 µm at 2 mm and 79.1 µm at 3.4 mm for grader 2.

CONCLUSION

To the best of our knowledge, this is the first study to present IR image-based retinal vessel diameters in ex vivo postmortem eyes. Retinal IR/OCT imaging is possible, and measurements are reproducible in formaldehyde-fixated human eyes. Fixation artefacts result in lower image quality, and this can impose challenges in correctly detecting, classifying, and measuring retinal vessels.

Accuracy of an Artificial Intelligence Chatbot's Interpretation of Clinical Ophthalmic Images

Importance

Ophthalmology is reliant on effective interpretation of multimodal imaging to ensure diagnostic accuracy. The new ability of ChatGPT-4 (OpenAI) to interpret ophthalmic images has not yet been explored.

Objective

To evaluate the performance of the novel release of an artificial intelligence chatbot that is capable of processing imaging data.

Design, Setting, and Participants

This cross-sectional study used a publicly available dataset of ophthalmic cases from OCTCases, a medical education platform based out of the Department of Ophthalmology and Vision Sciences at the University of Toronto, with accompanying clinical multimodal imaging and multiple-choice questions. Across 137 available cases, 136 contained multiple-choice questions (99%).

Exposures

The chatbot answered questions requiring multimodal input from October 16 to October 23, 2023.

Main Outcomes and Measures

The primary outcome was the accuracy of the chatbot in answering multiple-choice questions pertaining to image recognition in ophthalmic cases, measured as the proportion of correct responses. χ2 Tests were conducted to compare the proportion of correct responses across different ophthalmic subspecialties.

Results

A total of 429 multiple-choice questions from 136 ophthalmic cases and 448 images were included in the analysis. The chatbot answered 299 of multiple-choice questions correctly across all cases (70%). The chatbot's performance was better on retina questions than neuro-ophthalmology questions (77% vs 58%; difference = 18%; 95% CI, 7.5%-29.4%; χ21 = 11.4; P < .001). The chatbot achieved a better performance on nonimage-based questions compared with image-based questions (82% vs 65%; difference = 17%; 95% CI, 7.8%-25.1%; χ21 = 12.2; P < .001).The chatbot performed best on questions in the retina category (77% correct) and poorest in the neuro-ophthalmology category (58% correct). The chatbot demonstrated intermediate performance on questions from the ocular oncology (72% correct), pediatric ophthalmology (68% correct), uveitis (67% correct), and glaucoma (61% correct) categories.

Conclusions and Relevance

In this study, the recent version of the chatbot accurately responded to approximately two-thirds of multiple-choice questions pertaining to ophthalmic cases based on imaging interpretation. The multimodal chatbot performed better on questions that did not rely on the interpretation of imaging modalities. As the use of multimodal chatbots becomes increasingly widespread, it is imperative to stress their appropriate integration within medical contexts.

OCTA-500: A retinal dataset for optical coherence tomography angiography study

Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age/gender/eye/disease) and seven types of segmentation labels (large vessel/capillary/artery/vein/2D FAZ/3D FAZ/retinal layers). Then, we propose a multi-object segmentation task called CAVF, which integrates capillary segmentation, artery segmentation, vein segmentation, and FAZ segmentation under a unified framework. In addition, we optimize the 3D-to-2D image projection network (IPN) to IPN-V2 to serve as one of the segmentation baselines. Experimental results demonstrate that IPN-V2 achieves an about 10% mIoU improvement over IPN on CAVF task. Finally, we further study the impact of several dataset characteristics: the training set size, the model input (OCT/OCTA, 3D volume/2D projection), the baseline networks, and the diseases. The dataset and code are publicly available at: https://ieee-dataport.org/open-access/octa-500.

Optical coherence tomography as a potential surrogate marker of dopaminergic modulation across the life span

The retina has been considered a "window to the brain" and shares similar innervation by the dopaminergic system with the cortex in terms of an unequal distribution of D1 and D2 receptors. Here, we provide a comprehensive overview that Optical Coherence Tomography (OCT), a non-invasive imaging technique, which provides an "in vivo" representation of the retina, shows promise to be used as a surrogate marker of dopaminergic neuromodulation in cognition. Overall, most evidence supports reduced retinal thickness in individuals with dopaminergic dysregulation (e.g., patients with Parkinson's Disease, non-demented older adults) and with poor cognitive functioning. By using the theoretical framework of metacontrol, we derive hypotheses that retinal thinning associated to decreased dopamine (DA) levels affecting D1 families, might lead to a decrease in the signal-to-noise ratio (SNR) affecting cognitive persistence (depending on D1-modulated DA activity) but not cognitive flexibility (depending on D2-modulated DA activity). We argue that the use of OCT parameters might not only be an insightful for cognitive neuroscience research, but also a potentially effective tool for individualized medicine with a focus on cognition. As our society progressively ages in the forthcoming years and decades, the preservation of cognitive abilities and promoting healthy aging will hold of crucial significance. OCT has the potential to function as a swift, non-invasive, and economical method for promptly recognizing individuals with a heightened vulnerability to cognitive deterioration throughout all stages of life.