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In this paper, we design a multimodal visible/near-infrared photoacoustic microscopy and optical coherence tomography (VIS/NIR-PAM-OCT) system for imaging both retina and retinal pigment epithelium (RPE)/choroid complex layer. F127 and DSPE-PEG-cRGD encapsulated IR-1048 nanoparticles (FINPs) exhibiting absorption peak up to 1,064 nm were utilized as contrast agents to enhance NIR-PAM for in vivo imaging of fundus tissues. The fundus structure and vessels are clearly visualized by the multimodal imaging, and their parameters were quantitatively analyzed. NIR-PAM and OCT imaging of fundus were time-serially monitored over 60 min following the intravenous injection of FINPs into rats. The results indicated a 134 % increase in image signals in PAM at 1 min, along with an 8.23 % intensity enhancement in OCT. Moreover, laser-induced choroidal neovascularization (CNV) was specifically detected and accurately quantified using VIS/NIR-PAM-OCT. Lastly, FINPs demonstrated excellent biocompatibility in hematology analysis and pathology testing.

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly (over the age of 60 years) in western countries. In the early stages of the disease, structural changes may be subtle and cannot be detected. Recently it has been postulated that the mechanical properties of the retina may change with the onset of AMD. In this manuscript, we present a novel, non-invasive means that utilizes synchronized acoustic radiation force optical coherence elastography (ARF-OCE) to measure and estimate the elasticity of cadaver porcine retina. Both regions near the optic nerve and in the peripheral retina were studied. An acoustic force is exerted on the tissue for excitation and the resulting tissue vibrations, often in the nanometer scale, are detected with high-resolution optical methods. Segmentation has been performed to isolate individual layers and the Young's modulus has been estimated for each. The results have been successfully compared and mapped to corresponding histological results using H&E staining. Finally, 64 elastograms of the retina were analyzed, as well as the elastic properties, with stiffness ranging from 1.3 to 25.9 kPa in the ganglion to the photoreceptor sides respectively. ARF-OCE allows for the elasticity mapping of anatomical retinal layers. This imaging approach needs further evaluation but has the potential to allow physicians to gain a better understanding of the elasticity of retinal layers in retinal diseases such as AMD.

Cardiovascular diseases are the leading cause of fatalities in the United States. Atherosclerotic plaques are one of the primary complications that can lead to strokes and heart attacks if left untreated. It is essential to diagnose the disease early and distinguish vulnerable plaques from harmless ones. Many methods focus on the structural or molecular properties of plaques. Mechanical properties have been shown to change drastically when abnormalities develop in arterial tissue. We report the development of an acoustic radiation force optical coherence elastography (ARF-OCE) system that uses an integrated miniature ultrasound and optical coherence tomography (OCT) probe to map the relative elasticity of vascular tissues. We demonstrate the capability of the miniature probe to map the biomechanical properties in phantom and human cadaver carotid arteries.

Doppler optical coherence tomography (DOCT) is considered one of the most promising functional imaging modalities for neuro biology research and has demonstrated the ability to quantify cerebral blood flow velocity at a high accuracy. However, the measurement of total absolute blood flow velocity (BFV) of major cerebral arteries is still a difficult problem since it not only relates to the properties of the laser and the scattering particles, but also relates to the geometry of both directions of the laser beam and the flow. In this paper, focusing on the analysis of cerebral hemodynamics, we presents a method to quantify the total absolute blood flow velocity in middle cerebral artery (MCA) based on volumetric vessel reconstruction from pure DOCT images. A modified region growing segmentation method is first used to localize the MCA on successive DOCT B-scan images. Vessel skeletonization, followed by an averaging gradient angle calculation method, is then carried out to obtain Doppler angles along the entire MCA. Once the Doppler angles are determined, the absolute blood flow velocity of each position on the MCA is easily found. Given a seed point position on the MCA, our approach could achieve automatic quantification of the fully distributed absolute BFV. Based on experiments conducted using a swept-source optical coherence tomography system, our approach could achieve automatic quantification of the fully distributed absolute BFV across different vessel branches in the rodent brain.

Elastography provides a powerful tool for histopathological identification and clinical diagnosis based on information from tissue stiffness. Benefiting from high resolution, three-dimensional (3D), and noninvasive optical coherence tomography (OCT), optical micro-elastography has the ability to determine elastic properties with a resolution of ~10 μm in a 3D specimen. The shear wave velocity measurement can be used to quantify the elastic modulus. However, in current methods, shear waves are measured near the surface with an interference of surface waves. In this study, we developed acoustic radiation force (ARF) orthogonal excitation optical coherence elastography (ARFOE-OCE) to visualize shear waves in 3D. This method uses acoustic force perpendicular to the OCT beam to excite shear waves in internal specimens and uses Doppler variance method to visualize shear wave propagation in 3D. The measured propagation of shear waves agrees well with the simulation results obtained from finite element analysis (FEA). Orthogonal acoustic excitation allows this method to measure the shear modulus in a deeper specimen which extends the elasticity measurement range beyond the OCT imaging depth. The results show that the ARFOE-OCE system has the ability to noninvasively determine the 3D elastic map.

We report on a real-time acoustic radiation force optical coherence elastography (ARF-OCE) system to map the relative elasticity of corneal tissue. A modulated ARF is used as excitation to vibrate the cornea while OCE serves as detection of tissue response. To show feasibility of detecting mechanical contrast using this method, we performed tissue-equivalent agarose phantom studies with inclusions of a different stiffness. We obtained 3-D elastograms of a healthy cornea and a highly cross-linked cornea. Finally we induced a stiffness change on a small portion of a cornea and observed the differences in displacement.

We present an automatic segmentation method for delineation and quantitative thickness measurement of multiple layers in endoscopic airway optical coherence tomography (OCT) images. The boundaries of the mucosa and the sub-mucosa layers were extracted using a graph-theory-based dynamic programming algorithm. The algorithm was tested with pig airway OCT images acquired with a custom built long range endoscopic OCT system. The performance of the algorithm was demonstrated by cross-validation between auto and manual segmentation experiments. Quantitative thicknesses changes in the mucosal layers are obtained automatically for smoke inhalation injury experiments.

Doppler optical coherence tomography (DOCT) is considered one of the most promising functional imaging modalities for neuro biology research and has demonstrated the ability to quantify cerebral blood flow velocity at a high accuracy. However, the measurement of total absolute blood flow velocity (BFV) of major cerebral arteries is still a difficult problem since it is related to vessel geometry. In this paper, we present a volumetric vessel reconstruction approach that is capable of measuring the absolute BFV distributed along the entire middle cerebral artery (MCA) within a large field-of-view. The Doppler angle at each point of the MCA, representing the vessel geometry, is derived analytically by localizing the artery from pure DOCT images through vessel segmentation and skeletonization. Our approach could achieve automatic quantification of the fully distributed absolute BFV across different vessel branches. Experiments on rodents using swept-source optical coherence tomography showed that our approach was able to reveal the consequences of permanent MCA occlusion with absolute BFV measurement.

We present an automatic segmentation method for the delineation and quantitative thickness measurement of multiple layers in endoscopic airway optical coherence tomography (OCT) images. The boundaries of the mucosa and the sub-mucosa layers are accurately extracted using a graph-theory-based dynamic programming algorithm. The algorithm was tested with sheep airway OCT images. Quantitative thicknesses of the mucosal layers are obtained automatically for smoke inhalation injury experiments.

Diabetic retinopathy (DR) is one of the major causes of visual impairment in adults with diabetes. Optical coherence tomography angiography (OCTA) is nowadays widely used as the golden criterion for diagnosing DR. Recently, wide-field OCTA (WF-OCTA) provided more abundant information including that of the peripheral retinal degenerative changes and it can contribute in accurately diagnosing DR. The need for an automatic DR diagnostic system based on WF-OCTA pictures attracts more and more attention due to the large diabetic population and the prevalence of retinopathy cases. In this study, automatic diagnosis of DR using vision transformer was performed using WF-OCTA images (12 mm × 12 mm single-scan) centered on the fovea as the dataset. WF-OCTA images were automatically classified into four classes: No DR, mild nonproliferative diabetic retinopathy (NPDR), moderate to severe NPDR, and proliferative diabetic retinopathy (PDR). The proposed method for detecting DR on the test set achieves accuracy of 99.55%, sensitivity of 99.49%, and specificity of 99.57%. The accuracy of the method for DR staging reaches up to 99.20%, which has been proven to be higher than that attained by classical convolutional neural network models. Results show that the automatic diagnosis of DR based on vision transformer and WF-OCTA pictures is more effective for detecting and staging DR. [ABSTRACT FROM AUTHOR]

The effect of dopant composition ratio on nonvolatile holographic recording in LiNb[O.sub.3]:Cu:Ce crystals is investigated experimentally. The results show that the dopant composition ratio affects the recording sensitivity and fixed diffraction efficiency by altering the UV light absorption characteristics of the crystals during nonvolatile, holographic recording. Increasing the dopant composition ratio of Cu and Ce leads to an increase in the absorption of UV light and further to an increase in the recording sensitivity and fixed diffraction efficiency. The UV light absorption characteristics of LiNb[O.sub.3]:Cu:Ce crystals and their roles in nonvolatile holographic recording are theoretically analyzed. The theoretical results are consistent with those of the experiments. OCIS codes: 210.2860, 090.2900, 090.7330, 160.2900, 050.7330.

Pathological myopia (PM) is a severe ocular disease leading to blindness. As a traditional noninvasive diagnostic method, fundus color photography (FCP) is widely used in detecting PM due to its high fidelity and precision. However, manual examination of fundus photographs for PM is time-consuming and prone to high error rates. Existing automated detection technologies have yet to study the detailed classification in diagnosing different stages of PM lesions. In this paper, we proposed an intelligent system which utilized Resnet101 technology to multi-categorically diagnose PM by classifying FCPs with different stages of lesions. The system subdivided different stages of PM into eight subcategories, aiming to enhance the precision and efficiency of the diagnostic process. It achieved an average accuracy rate of 98.86% in detection of PM, with an area under the curve (AUC) of 98.96%. For the eight subcategories of PM, the detection accuracy reached 99.63%, with an AUC of 99.98%. Compared with other widely used multi-class models such as VGG16, Vision Transformer (VIT), EfficientNet, this system demonstrates higher accuracy and AUC. This artificial intelligence system is designed to be easily integrated into existing clinical diagnostic tools, providing an efficient solution for large-scale PM screening. [ABSTRACT FROM AUTHOR]

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.

The structure of the retinal layers provides valuable diagnostic information for many ophthalmic diseases. Optical coherence tomography (OCT) obtains cross-sectional images of the retina, which reveals information about the retinal layers. The U-net based approaches are prominent in retinal layering methods, which are usually beneficial to local characteristics but not good at obtaining long-distance dependence for contextual information. Furthermore, the morphology of retinal layers with the disease is more complex, which brings more significant challenges to the task of retinal layer segmentation. We propose a U-shaped network combining an encoder-decoder architecture and self-attention mechanisms. In response to the characteristics of retinal OCT cross-sectional images, a self-attentive module in the vertical direction is added to the bottom of the U-shaped network, and an attention mechanism is also added in skip connection and up-sampling to enhance essential features. In this method, the transformer's self-attentive mechanism obtains the global field of perception, thus providing the missing context information for convolutions, and the convolutional neural network also efficiently extracts local features, compensating the local details the transformer ignores. The experiment results showed that our method is accurate and better than other methods for segmentation of the retinal layers, with the average Dice scores of 0.871 and 0.820, respectively, on two public retinal OCT image datasets. To perform the layer segmentation of retinal OCT image better, the proposed method incorporates the transformer's self-attention mechanism in a U-shaped network, which is helpful for ophthalmic disease diagnosis., Competing Interests: The authors declare that they have no conflict of interest., (© 2024 Optica Publishing Group.)