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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.

Quantitative analysis of optical attenuation based on optical coherence tomography images will offer an effective method to enhance diagnostic capabilities. In this paper, the optical attenuation in demineralized caries specimens was calculated to distinguish between normal teeth and carious teeth and further to differentiate the severity of caries, and thus come to the half‐automated diagnosis of dental caries. Results show that the attenuation coefficient in carious regions is approximately 4.97mm−1±0.206, while that of normal teeth is about 3.69mm−1±0.231. Attenuation coefficient of carious regions is 35% higher than that of normal teeth. Moreover, five classes of caries were qualified and classified based on the optical attenuation coefficient. Compared with the healthy teeth, there is a noticeable disparity in the attenuation coefficients of carious teeth, both on the surface and at the dentinoenamel junction. This study provides a method for accurate caries diagnosis, particularly in detection of early lesions and subtle structural changes. [ABSTRACT FROM AUTHOR]

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]

Objective. The high-precision segmentation of retinal vessels in fundus images is important for the early diagnosis of ophthalmic diseases. However, the extraction for microvessels is challenging due to their characteristics of low contrast and high structural complexity. Although some works have been developed to improve the segmentation ability in thin vessels, they have only been successful in recognizing small vessels with relatively high contrast. Approach. Therefore, we develop a deep learning (DL) framework with a multi-stage and dual-channel network model (MSDC_NET) to further improve the thin-vessel segmentation with low contrast. Specifically, an adaptive image enhancement strategy combining multiple preprocessing and the DL method is firstly proposed to elevate the contrast of thin vessels; then, a two-channel model with multi-scale perception is developed to implement whole- and thin-vessel segmentation; and finally, a series of post-processing operations are designed to extract more small vessels in the predicted maps from thin-vessel channels. Main results. Experiments on DRIVE, STARE and CHASE_DB1 demonstrate the superiorities of the proposed MSDC_NET in extracting more thin vessels in fundus images, and quantitative evaluations on several parameters based on the advanced ground truth further verify the advantages of our proposed DL model. Compared with the previous multi-branch method, the specificity and F1 score are improved by about 2.18%, 0.68%, 1.73% and 2.91%, 0.24%, 8.38% on the three datasets, respectively. Significance. This work may provide richer information to ophthalmologists for the diagnosis and treatment of vascular-related ophthalmic diseases. [ABSTRACT FROM AUTHOR]

Purpose: The optic disc and the macular are two major anatomical structures in the human eye. Optic discs are associated with the optic nerve. Macular mainly involves degeneration and impaired function of the macular region. Reliable optic disc and macular segmentation are necessary for the automated screening of retinal diseases. Methods: A swept‐source OCTA system was designed to capture OCTA images of human eyes. To address these segmentation tasks, first, we constructed a new Optic Disc and Macula in fundus Image with optical coherence tomography angiography (OCTA) dataset (ODMI). Second, we proposed a Coarse and Fine Attention‐Based Network (CFANet). Results: The five metrics of our methods on ODMI are 98.91%, 98.47%, 89.77%, 98.49%, and 89.77%, respectively. Conclusions: Experimental results show that our CFANet has achieved good performance on segmentation for the optic disc and macula in OCTA. [ABSTRACT FROM AUTHOR]

Purpose: Diabetic retinopathy (DR) is one of the most common diseases caused by diabetes and can lead to vision loss or even blindness. The wide‐field optical coherence tomography (OCT) angiography is non‐invasive imaging technology and convenient to diagnose DR. Methods: A newly constructed Retinal OCT‐Angiography Diabetic retinopathy (ROAD) dataset is utilized for segmentation and grading tasks. It contains 1200 normal images, 1440 DR images, and 1440 ground truths for DR image segmentation. To handle the problem of grading DR, we propose a novel and effective framework, named projective map attention‐based convolutional neural network (PACNet). Results: The experimental results demonstrate the effectiveness of our PACNet. The accuracy of the proposed framework for grading DR is 87.5% on the ROAD dataset. Conclusions: The information on ROAD can be viewed at URL https://mip2019.github.io/ROAD. The ROAD dataset will be helpful for the development of the early detection of DR field and future research. Translational Relevance: The novel framework for grading DR is a valuable research and clinical diagnosis method. [ABSTRACT FROM AUTHOR]

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

Myopia is a significant cause of visual impairment which may lead to many complications. However, the understanding of the mechanisms of myopia is still limited. In this paper, in order to investigate the development and the treatment of myopia, we analyzed the biological structure parameters of mice eyes, obtained from optical coherence tomography (OCT), and the optical performance of mice eyes calculated using ZEMAX software (ZEMAX Development Corporation, Kirkland, WA, USA) in which the optical model was built on the segment-by-segment optically corrected OCT 3D-images. Time-serial evaluation of three groups of mice eyes (form-deprivation myopia mice eyes, normal mice eyes, and atropine-treated myopia mice eyes) was performed. In addition to the biological structure parameters, imaging performance with the development of root-mean-square wavefront aberration at six filed angles was compared and analyzed. Results show that the biological structure parameters of the eye are closely related to the development of myopia. The peripheral defocus of the retina has a significant impact on inducing myopia, which verifies the new theory of myopia development. The delaying effect of atropine solution on myopia development is shown to verify the therapeutic effect of the medicine. This study provides technical support for the investigation of the myopia mechanism. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) is an imaging modality that acquires high‐resolution cross‐sectional images of living tissues and it has become the standard in ophthalmological diagnoses. However, most quantitative morphological measurements are based on the raw OCT images which are distorted by several mechanisms such as the refraction of probe light in the sample and the scan geometries and thus the analysis of the raw OCT images inevitably induced calculation errors. In this paper, based on Fermat's principle and the concept of inverse light tracing, image distortions due to refraction occurred at tissue boundaries in the whole‐eye OCT imaging of mouse by telecentric scanning were corrected. Specially, the mathematical correction models were deducted for each interface, and the high‐precision whole‐eye image was recovered segment by segment. We conducted phantom and in vivo experiments on mouse and human eyes to verify the distortion correction algorithm, and several parameters of the radius of curvature, thickness of tissues and error, were calculated to quantitatively evaluate the images. Experimental results demonstrated that the method can provide accurate and reliable measurements of whole‐eye parameters and thus be a valuable tool for the research and clinical diagnosis. [ABSTRACT FROM AUTHOR]

The shape and structure of retinal layers are basic characteristics for the diagnosis of many ophthalmological diseases. Based on B-Scans of optical coherence tomography, most of retinal layer segmentation methods are composed of two-steps: classifying pixels and extracting retinal layers, in which the optimization of two independent steps decreases the accuracy. Although the methods based on deep learning are highly accurate, they require a large amount of labeled data. This paper proposes a single-step method based on transformer for retinal layer segmentation, which is trained by axial data (A-Scans), to obtain the boundary of each layer. The proposed method was evaluated on two public data sets. The first one contains eight retinal layer boundaries for diabetic macular edema, and the second one contains nine retinal layer boundaries for healthy controls and subjects with multiple sclerosis. Its absolute average distance errors are 0.99 pixels and 3.67 pixels, respectively, for the two sets, and its root mean square error is 1.29 pixels for the latter set. In addition, its accuracy is acceptable even if the training data is reduced to 0.3. The proposed method achieves state-of-the-art performance while maintaining the correct topology and requires less labeled data. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) angiography has drawn much attention in the medical imaging field. Binarization plays an important role in quantitative analysis of eye with optical coherence tomography. To address the problem of few training samples and contrast‐limited scene, we proposed a new binarization framework with specific‐patch SVM (SPSVM) for low‐intensity OCT image, which is open and classification‐based framework. This new framework contains two phases: training model and binarization threshold. In the training phase, firstly, the patches of target and background from few training samples are extracted as the ROI and the background, respectively. Then, PCA is conducted on all patches to reduce the dimension and learn the eigenvector subspace. Finally, the classification model is trained from the features of patches to get the target value of different patches. In the testing phase, the learned eigenvector subspace is conducted on the pixels of each patch. The binarization threshold of patch is obtained with the learned SVM model. We acquire a new OCT mice eye (OCT‐ME) database, which is publicly available at https://mip2019.github.io/spsvm. Extensive experiments were performed to demonstrate the effectiveness of the proposed SPSVM framework. [ABSTRACT FROM AUTHOR]

Pancreaticobiliary carcinomas is a highly malignant gastrointestinal tumor. Most pancreaticobiliary cancers arise from epithelial proliferation within the pancreaticobiliary ducts, referred to as pancreatic intraepithelial neoplasias (PanINs). Some PanINs are benign metaplasia, while others progress to invasive duct adenocarcinoma (IDAC). However, there is no standard program to diagnose the progression from PanINs to IDAC. In this study, we present a tri-modality imaging system, which integrates ultrasound (US), optical coherence tomography (OCT), and near-infrared fluorescence (NIRF) for pancreaticobiliary duct imaging. This system can obtain OCT, US, and NIRF images in real-time with a frame rate of 30 frames per second. For the endoscopy probe with an outer diameter of 0.9 mm, the US transducer and fiber ball lens were placed back to back. In vivo experiments were performed on the rectums of Sprague–Dawley rats to demonstrate the imaging performance of US, OCT, and fluorescence angiography. An ex vivo experiment on a human pancreatic duct was performed for a more accurate assessment of the pancreaticobiliary duct. The tomography images of rat rectums and human pancreatic ducts were correlated with hematoxylin and eosin (H&E) histology to check the measurement accuracy. The integrated tri-modality system has great clinical potential in mechanism studies, early diagnosis, and prognosis evaluation of malignant pancreaticobiliary carcinomas. [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.

Age-related Macular Degeneration (AMD) and Diabetic Macular Edema (DME) are two common retinal diseases for elder people that may ultimately cause irreversible blindness. Timely and accurate diagnosis is essential for the treatment of these diseases. In recent years, computer-aided diagnosis (CAD) has been deeply investigated and effectively used for rapid and early diagnosis. In this paper, we proposed a method of CAD using vision transformer to analyze optical coherence tomography (OCT) images and to automatically discriminate AMD, DME, and normal eyes. A classification accuracy of 99.69% was achieved. After the model pruning, the recognition time reached 0.010 s and the classification accuracy did not drop. Compared with the Convolutional Neural Network (CNN) image classification models (VGG16, Resnet50, Densenet121, and EfficientNet), vision transformer after pruning exhibited better recognition ability. Results show that vision transformer is an improved alternative to diagnose retinal diseases more accurately. [ABSTRACT FROM AUTHOR]

Tumor angiogenesis is a complex process that is unamenable to intravital whole‐process monitoring, especially on microscopic assessment of tumor microvessel and quantifying microvascular hemodynamics before and after the nanotherapeutics, which hinder the understanding of nanotheranostics outcomes in tumor treatment. Herein, a new photoacoustic (PA) imaging‐optical coherence tomography angiography (OCTA)‐laser speckle (LS) multimodal imaging strategy is first proposed, which is not only able to precisely macro guide the thermo‐chemotherapy of tumor by monitoring blood oxygen saturation (SaO2) and hemoglobin content (HbT), but also capable of long‐term microscopic investigating the microvessel morphology (microvascular density) and hemodynamics changes (relative blood flow) before and after the nanotherapeutics in vivo. Moreover, to realize the tumor thermo‐chemotherapy treatment based on this novel multimodal imaging strategy, a 2D 5‐fluorouracil silicon nanosheets (5‐Fu‐Si NSs) therapeutic agent is designed. Furthermore, 2D high‐resolution tumor microvascular images in different stage display that tendency of the thermo‐chemotherapy effect is closely associated with tumor angiogenesis. Taken together, the investigations establish the fundamental base in theory and technology for further tailoring the novel specific diagnosis and treatment strategy in tumor. More importantly, this technique will be beneficial to evaluate the tumor microvascular response to nanotherapeutics at microscale. [ABSTRACT FROM AUTHOR]

Determination of the precise location and the degree of the Choroidal neovascularization (CNV) lesion is essential for diagnosation Neovascular age-related macular degeneration (AMD) and evaluation the efficacy of treatment. Noninvasive imaging techniques with specific contrast for CNV evaluation are demanded. In this paper, two noninvasive imaging techniques, namely Optical coherence tomography (OCT) and Photoacoustic microscopy (PAM), are combined to provide specific detection of CNV for their complimentary contrast mechanisms. In vivo time-serial evaluation of Laser-induced CNV in rats is present at days 1, 3, 5, 7, 14, 21 after laser photocoagulation is applied to the rat fundus. Both OCT and PAM show that the CNV increases to its maximum at day 7 and decreases at day 14. Quantification of CNV area and CNV thickness is given. The dual-modal information of CNV is consistent with the histologic evaluation by hematoxylin and eosin (H&E) staining. [ABSTRACT FROM AUTHOR]

Purpose: To demonstrate the value of the laser-scanning optical-resolution (LSOR)-photoacoustic (PA) microscopy (PAM) system and the conventional multimodal imaging techniques in the evaluation of laser-induced retinal injury and choroidal neovascularization (CNV) in rats. Methods: Different degrees of retinal injury were induced using laser photocoagulation. We compared the LSOR-PAM system with conventional imaging techniques in evaluating retinal injury with or without CNV. Six additional rats, treated with an anti-VEGF antibody or immunoglobulin G immediately after photocoagulation, were imaged 7 and 14 days after injection, and CNV lesion areas were compared. Results: In the retinal injury model, fundus autofluorescence showed well-defined hyperreflection, while the lesion displayed abundant PA signals demonstrating nonuniform melanin distribution in retinal pigment epithelium (RPE). RPE was detected with higher contrast in the PAM B-scan image than optical coherence tomography (OCT). Additionally, the CNV lesion was present with multiple PA signal intensities which distinctly characterized the location and area of CNV as found in fundus fluorescein angiography. Furthermore, the decreased PA signals extending from the CNV lesion were similar to those of the vascular bud in ex vivo imaging, which was invisible in other in vivo images. When treated with anti-VEGF agents, statistically significant differences can be demonstrated by PAM similar to other modalities. Conclusions: LSOR-PAM can detect the melanin distribution of RPE in laser-induced retinal injury and CNV in rats. PAM imaging provides a potential new tool to evaluate the vitality and functionality of RPE in vivo as well as to monitor the development and treatment of CNV. [ABSTRACT FROM AUTHOR]

The human eyes provide a natural window for noninvasive measurement of the pulse wave velocity (PWV) of small arteries. By measuring the retinal PWV, the stiffness of small arteries can be assessed, which may better detect early vascular diseases. Therefore, retinal PWV measurement has attracted increasing attention. In this study, a jump‐scanning method was proposed for noninvasive measurement of retinal PWV using spectral‐domain optical coherence tomography (SD‐OCT). The jump‐scanning method uses the phase‐resolved Doppler OCT to obtain the pulse shapes. To realize PWV measurement, the jump‐scanning method extracts the transit time of the pulse wave from an original OCT scanning site to another through a transient jump. The measured retinal arterial PWV of a young human subject with normal blood pressure was in the order of 20 to 30 mm/s, which was consistent with previous studies. As a comparison, PWV of 50 mm/s was measured for a young human subject with prehypertension, which was in accordance with the finding of strong association between retinal PWV and blood pressure. In summary, it is believed the proposed jump‐scanning method could benefit the research and diagnosis of vascular diseases through the window of human eyes. [ABSTRACT FROM AUTHOR]

We report the virtual instrumentation of both time-domain (TD) and spectral-domain (SD) optical coherence tomography (OCT) systems. With a virtual partial coherence source from either a simulated or measured spectrum, the OCT signals of both A-scan and B-scan were demonstrated. The spectrometric detector's pixel number, dynamic range, noise, as well as spectral resolution can be simulated in the virtual spectral domain (SD-OCT). The virtual-OCT system provides an environment for parameter evaluation and algorithm optimization for experimental OCT instrumentation, and promotes the understanding of OCT imaging and signal post-processing processes. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the feasibility and practicality of real-time noninvasive imaging and measurement of ocular accommodation (0-5D with one diopter step) using dual-channel, dual-focus spectral domain optical coherence tomography (SD-OCT). Materials and Methods: A custom-built, dual-channel, dual-focus SD-OCT was used to image the anterior segment, including the cornea, iris, and anterior and posterior lens surfaces. Three consecutive measurements were taken in each accommodative session for each subject. Changes in ocular dimensions during accommodation were calculated based on the acquired SD-OCT images. A Friedman test was used to test sensitivity of ocular dimension changes per diopter of accommodation. Results: With accommodation, anterior chamber depth (ACD), curvature radius of both anterior (RAL) and posterior (RPL) lens surfaces, and pupil diameter (PD) decreased significantly, whereas lens thickness (LT) increased significantly ( p < 0.05, Friedman test). Ocular dimensions tended to change according to the increasing of accommodative stimulus, as did a backward movement of the posterior lens surface. Conclusions: SD-OCT, via extended imaging depth through a dual-channel, dual-focus approach, is a feasible and practical modality for noninvasive imaging and measurement of ocular accommodation. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the effect of powerless contact lens on improving the quality of optical coherence tomography imaging of rodent retina. Methods: A spectral-domain optical coherence tomography (SD-OCT) system was built for in vivo imaging of rodent retina. The calibrated depth resolution of the system was 3 |im in tissue. A commercial powerless contact lens for rat eye was tested in the experiments. For each rat eye, the retina was imaged in vivo sequentially first without wearing contact lens and then with wearing contact lens. The lateral resolution and signal-to-noise ratio of the OCT images with and without contact lens were compared to evaluate the improvement of image quality. Results: The fundus images generated from the measured 3D OCT datasets with contact lens showed sharper retinal blood vessels than those without contact lens. The contrast of the retinal blood vessels was also significantly enhanced in the OCT fundus images with contact lens. As high as 10 dB improvements in SNR was observed for OCT images with contact lens compared to the images of the same retinal area without contact lens. Conclusions: We have demonstrated that the use of powerless contact lens on rat eye can significantly improve OCT image quality of rodent retina, which is a benefit in addition to preventing cataract formation. We believe the improvement in image quality is the result of partial compensation of the optical aberrations of the rodent eye by the contact lens. [ABSTRACT FROM AUTHOR]

Abstract: We propose a united theory that describes the two-center recording system by taking scattering noise into account. The temporal evolution of the signal-to-noise ratio in doubly doped photorefractive crystals is described based on jointly solving material equations and coupled-wave equations with the fourth-order Runge–Kutta method. Roles of microcosmic optical parameters of dopants on the signal-to-noise ratio are discussed in detail. The theoretical results can confirm and predict experimental results. [Copyright &y& Elsevier]

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.)

Background: Diabetic retinopathy (DR) is one of the most common eye diseases. Convolutional neural networks (CNNs) have proven to be a powerful tool for learning DR features; however, accurate DR grading remains challenging due to the small lesions in optical coherence tomography angiography (OCTA) images and the small number of samples., Methods: In this article, we developed a novel deep-learning framework to achieve the fine-grained classification of DR; that is, the lightweight channel and spatial attention network (CSANet). Our CSANet comprises two modules: the baseline model, and the hybrid attention module (HAM) based on spatial attention and channel attention. The spatial attention module is used to mine small lesions and obtain a set of spatial position weights to address the problem of small lesions being ignored during the convolution process. The channel attention module uses a set of channel weights to focus on useful features and suppress irrelevant features., Results: The extensive experimental results for the OCTA-DR and diabetic retinopathy analysis challenge (DRAC) 2022 data sets showed that the CSANet achieved state-of-the-art DR grading results, showing the effectiveness of the proposed model. The CSANet had an accuracy rate of 97.41% for the OCTA-DR data set and 85.71% for the DRAC 2022 data set., Conclusions: Extensive experiments using the OCTA-DR and DRAC 2022 data sets showed that the proposed model effectively mitigated the problems of mutual confusion between DRs of different severity and small lesions being neglected in the convolution process, and thus improved the accuracy of DR classification., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1270/coif). The authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Objectives: Optical coherence tomography (OCT) is a new imaging technology that uses an optical analog of ultrasound imaging for biological tissues. Image segmentation plays an important role in dealing with quantitative analysis of medical images., Methods: We have proposed a novel framework to deal with the low intensity problem, based on the labeled patches and Bayesian classification ( LPBC ) model. The proposed method includes training and testing phases. During the training phase, firstly, we manually select the sub-images of background and Region of Interest (ROI) from the training image, and then extract features by patches. Finally, we train the Bayesian model with the features. The segmentation threshold of each patch is computed by the learned Bayesian model., Results: In addition, we have collected a new dataset of mouse eyes in vivo with OCT, named MEVOCT , which can be found at URL https://17861318579.github.io/LPBC. MEVOCT consists of 20 high-resolution images. The resolution of every image is 2048 × 2048 pixels., Conclusions: The experimental results demonstrate the effectiveness of the LPBC method on the new MEVOCT dataset. The ROI segmentation is of great importance for the distortion correction., (© 2023 Walter de Gruyter GmbH, Berlin/Boston.)

Optical Coherence Tomography (OCT) is widely used for endoscopic imaging in endoluminal organs because of its high imaging accuracy and resolution. However, OCT endoscopic imaging suffers from Non-Uniform Rotational Distortion (NURD), which can be caused by many factors, such as irregular motor rotation and changes in friction between the probe and the sheath. Correcting this distortion is essential to obtaining high-quality Optical Coherence Tomography Angiography (OCTA) images. There are two main approaches for correcting NURD: hardware-based methods and algorithm-based methods. Hardware-based methods can be costly, challenging to implement, and may not eliminate NURD. Algorithm-based methods, such as image registration, can be effective for correcting NURD but can also be prone to the problem of NURD propagation. To address this issue, we process frames by coarse and fine registration, respectively. The new reference frame is generated by filtering out the A-scan that may have the NURD problem by coarse registration. And the fine registration uses this frame to achieve the final NURD correction. In addition, we have improved the Features from Accelerated Segment Test (FAST) algorithm and put it into coarse and fine registration process. Four evaluation functions were used for the experimental results, including signal-to-noise ratio (SNR), peak signal-to-noise ratio (PSNR), mean squared error (MSE), and structural similarity index measure (SSIM). By comparing with Scale-invariant feature transform (SIFT), Speeded up robust features (SURF), Oriented FAST and Rotated BRIEF (ORB), intensity-based (Cross-correlation), and Optical Flow algorithms, our algorithm has a higher similarity between the corrected frames. Moreover, the noise in the OCTA data is better suppressed, and the vascular information is well preserved. Our image registration-based algorithm reduces the problem of NURD propagation between B-scan frames and improves the imaging quality of OCT endoscopic images.

Optical coherence tomography angiography (OCTA) images suffer from inevitable micromotion (breathing, heartbeat, and blinking) noise. These image artifacts can severely disturb the visibility of results and reduce accuracy of vessel morphological and functional metrics quantization. Herein, we propose a multiple wavelet-FFT algorithm (MW-FFTA) comprising multiple integrated processes combined with wavelet-FFT and minimum reconstruction that can be used to effectively attenuate motion artifacts and significantly improve the precision of quantitative information. We verified the fidelity of image information and reliability of MW-FFTA by the image quality evaluation. The efficiency and robustness of MW-FFTA was validated by the vessel parameters on multi-scene in vivo OCTA imaging. Compared with previous algorithms, our method provides better visual and quantitative results. Therefore, the MW-FFTA possesses the potential capacity to improve the diagnosis of clinical diseases with OCTA.

We developed a fast ultrahigh resolution optical coherence photoacoustic microscopy (FU-OCPAM) system by combining two complementary imaging modes of optical coherence microscopy (OCM) and photoacoustic microscopy (PAM) for cellular/subcellular imaging. The system used optical scanning to realize fast imaging speed and provided ultrahigh resolution of 1.24 and 0.59 μm for OCM and PAM, respectively. We imaged the retinal pigment epithelium (RPE) to demonstrate the subcellular imaging capability of the FU-OCPAM system. The OCM and PAM images clearly showed the RPE cell morphology and reflected the complementary optical properties of scattering and absorption. A quantitative analysis of the RPE cells was made based on photoacoustic (PA) signals. The cell area mainly ranged from 80 to 300  μm2, and had a linear relationship with the sum intensity of PA signals which mainly reflected the melanin content of the cells. The morphology and the PA signal could be used to identify qualitatively and quantitatively the aging and healthy states of the RPE cells. The results show the potential applications in studying the real-time cellular response to external stimulations and the progress of aging and diseases at the cellular level with FU-OCPAM.

We developed an improved dual band dual focus spectral domain optical coherence tomography (SD-OCT) for in vivo 2D/3D imaging of the whole eye segment, including the whole anterior segment and retina. The system featured two OCT channels with two different bands centered at 840 nm and 1050 nm, which were designed to image the retina and the anterior segments of the eye, respectively. By combing the two probe light beams for co-axial scanning and separating them for focusing at different segments of the eye with a combination of three dichroic mirrors, we not only minimized the loss of the backscattered light from the sample but also improved the imaging depth, scan range and resolution. The full resolved complex (FRC) method was applied to double the imaging depth for the whole anterior segment imaging, with which an imaging depth of 36.71 mm in air was achieved. We demonstrated that this system was capable of measuring the dynamic changes of ocular dimensions, including the asphericity of the cornea and lens, during accommodation.

Background and Objectives: To image and measure whole eye segments using dual-channel spectral-domain optical coherence tomography (SD-OCT) and to compare the results with those from the IOLMaster (Carl Zeiss Meditec, Dublin, CA)., Patients and Methods: Twenty eyes of 20 volunteers were recruited. Ocular dimensions, including cornea thickness, anterior chamber depth (ACD), lens thickness, anterior lens surface curvature, and axial length (AL), were calculated. The reproducibility of SD-OCT measurements, statistical significance of inter-instrument difference, correlation, and agreement were evaluated., Results: No significant differences were found between independent SD-OCT measurements (P > .05). The ACD and AL measured with IOLMaster were significantly shorter than those from SD-OCT (P < .001). There were high correlations and agreements in ACD (r = 0.994; 95% limits of agreement [LOA], 0.131-0.223 mm) and AL (r = 0.998; 95% LOA, 0.678-0.853 mm) between the two methods., Conclusion: Dual-channel SD-OCT was demonstrated to have good repeatability in imaging and measuring whole eye segments. The results had high correlations and agreements with those from the IOLMaster., (Copyright 2015, SLACK Incorporated.)

To research retinal stretching or distortion with accommodation, accommodation-induced changes in retinal thickness (RT) in the macular area were investigated in a population of young adults (n = 23) by using a dual-channel spectral domain optical coherence tomography (SD-OCT) system manufactured in-house for this study. This dual-channel SD-OCT is capable of imaging the cornea and retina simultaneously with an imaging speed of 24 kHz A-line scan rate, which can provide the anatomical dimensions of the eye, including the RT and axial length. Thus, the modification of the RT with accommodation can be calculated. A significant decrease in the RT (13.50 ± 1.25 μm) was observed during maximum accommodation. In the 4 mm × 4 mm macular area centered at the fovea, we did not find a significant quadrant-dependent difference in retinal volume change, which indicates that neither retinal stretching nor distortion was quadrant-dependent during accommodation. We speculate that the changes in RT with maximum accommodation resulted from accommodation-induced ciliary muscle contractions.

We have demonstrated a dual-channel multiplexing spectral-domain optical-coherence tomography (SD-OCT) system based on a 3×3 fiber coupler for extended imaging range of whole human eye depth, with a single light source and spectrometer. OCT images of anterior segments of a human eye were sequentially performed and constructed to demonstrate an extended depth range as large as 15 mm in air. A good quality OCT image of the whole anterior segment of an eye was present. Furthermore, whole eye segmental imaging was performed and ocular distances were calculated to show the validation of the system for whole eye morphological measurement.