Investigating ocular biomarkers and differential diagnosis of Alzheimer's disease and vascular cognitive impairment based on multimodal imaging.

Significance: The eye can be used as a potential monitoring window for screening, diagnosis, and monitoring of neurological diseases. Alzheimer's disease (AD) and vascular cognitive impairment (VCI) are common causes of cognitive impairment and may share many similarities in ocular signs. Multimodal ophthalmic imaging is a technology to quantify pupillary light reaction, retinal reflectance spectrum, and hemodynamics.

Physics-guided deep learning-based real-time image reconstruction of Fourier-domain optical coherence tomography.

In this paper, we introduce a physics-guided deep learning approach for high-quality, real-time Fourier-domain optical coherence tomography (FD-OCT) image reconstruction. Unlike traditional supervised deep learning methods, the proposed method employs unsupervised learning. It leverages the underlying OCT imaging physics to guide the neural networks, which could thus generate high-quality images and provide a physically sound solution to the original problem.

A multimodal imaging approach to investigate retinal oxygen and vascular dynamics, and neural dysfunction in bietti crystalline dystrophy.

Background: This study aimed to explore retinal changes in Bietti crystalline dystrophy (BCD) patients, including retinal metabolism, blood flow, vascular remodeling, and pupillary light reflex (PLR) abnormalities.

Methods: This cross-sectional study included 120 eyes from BCD patients and 120 eyes from healthy controls, utilizing a multimodal imaging system (MEFIAS 3200, SYSEYE, Chongqing, China) to evaluate retinal oxygenation, blood flow, vascular structure, and PLR. Measurements included oxygen saturation, blood flow velocity, vessel diameters, and pulsatility metrics.

Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening.

Background/aims: To investigate the comprehensive prediction ability for cognitive impairment in a general elder population using the combination of the multimodal ophthalmic imaging and artificial neural networks.

Methods: Patients with cognitive impairment and cognitively healthy individuals were recruited. All subjects underwent medical history, blood pressure measurement, the Montreal Cognitive Assessment, medical optometry, intraocular pressure and custom-built multimodal ophthalmic imaging, which integrated pupillary light reaction, multispectral imaging, laser speckle contrast imaging and retinal oximetry.

The retinal oxygen metabolism and hemodynamics as a substitute for biochemical tests to predict nonproliferative diabetic retinopathy.

Predicting the occurrence of nonproliferative diabetic retinopathy (NPDR) using biochemical parameters is invasive, which limits large-scale clinical application. Noninvasive retinal oxygen metabolism and hemodynamics of 215 eyes from 73 age-matched healthy subjects, 90 diabetic patients without DR, 40 NPDR, and 12 DR with postpanretinal photocoagulation were measured with a custom-built multimodal retinal imaging device. Diabetic patients underwent biochemical examinations.

Whole-Genome Resequencing of Ujimqin Sheep Identifies Genes Associated with Vertebral Number.

The number of vertebrae is a crucial economic trait that can significantly impact the carcass length and meat production in animals. However, our understanding of the quantitative trait loci (QTLs) and candidate genes associated with the vertebral number in sheep () remains limited. To identify these candidate genes and QTLs, we collected 73 Ujimqin sheep with increased numbers of vertebrae (T13L7, T14L6, and T14L7) and 23 sheep with normal numbers of vertebrae (T13L6).

URNet: System for recommending referrals for community screening of diabetic retinopathy based on deep learning.

Diabetic retinopathy (DR) will cause blindness if the detection and treatment are not carried out in the early stages. To create an effective treatment strategy, the severity of the disease must first be divided into referral-warranted diabetic retinopathy (RWDR) and non-referral diabetic retinopathy (NRDR). However, there are usually no sufficient fundus examinations due to lack of professional service in the communities, particularly in the developing countries.

GPU-accelerated iterative method for FD-OCT image reconstruction with an image-level cross-domain regularizer.

The image reconstruction for Fourier-domain optical coherence tomography (FD-OCT) could be achieved by iterative methods, which offer a more accurate estimation than the traditional inverse discrete Fourier transform (IDFT) reconstruction. However, the existing iterative methods are mostly A-line-based and are developed on CPU, which causes slow reconstruction. Besides, A-line-based reconstruction makes the iterative methods incompatible with most existing image-level image processing techniques.

SGLSA: Sphygmus gated laser speckle angiography for microcirculation hemodynamics imaging.

Hemodynamics imaging of the retinal microcirculation has been demonstrated to be potential access to evaluating ophthalmic diseases, cardio-cerebrovascular diseases, and metabolic diseases. However, existing structural and functional imaging techniques are insufficient in spatial or temporal resolution. The sphygmus gated laser speckle angiography (SGLSA) is proposed for structural and functional imaging with high spatiotemporal resolution.

Retinal oxygen kinetics imaging and analysis (ROKIA) based on the integration and fusion of structural-functional imaging.

The retina is one of the most metabolically active tissues in the body. The dysfunction of oxygen kinetics in the retina is closely related to the disease and has important clinical value. Dynamic imaging and comprehensive analyses of oxygen kinetics in the retina depend on the fusion of structural and functional imaging and high spatiotemporal resolution.

Bayesian statistics-guided label refurbishment mechanism: Mitigating label noise in medical image classification.

Purpose: Deep neural networks (DNNs) have been widely applied in medical image classification, benefiting from its powerful mapping capability among medical images. However, these existing deep learning-based methods depend on an enormous amount of carefully labeled images. Meanwhile, noise is inevitably introduced in the labeling process, degrading the performance of models.

Rethinking the neighborhood information for deep learning-based optical coherence tomography angiography.

Purpose: Optical coherence tomography angiography (OCTA) is a premium imaging modality for noninvasive microvasculature studies. Deep learning networks have achieved promising results in the OCTA reconstruction task, benefiting from their powerful modeling capability. However, two limitations exist in the current deep learning-based OCTA reconstruction methods: (a) the angiogram information extraction is only limited to the locally consecutive B-scans; and (b) all reconstruction models are confined to the 2D convolutional network architectures, lacking effective temporal modeling.

Functional imaging of human retina using integrated multispectral and laser speckle contrast imaging.

A novel integration of retinal multispectral imaging (MSI), retinal oximetry and laser speckle contrast imaging (LSCI) is presented for functional imaging of retinal blood vessels that could potentially allow early detection or monitoring of functional changes. We designed and built a cost-effective, scalable, retinal imaging instrument that integrates structural and functional retinal imaging techniques, including MSI, retinal oximetry and LSCI. Color fundus imaging was performed with 470 nm, 550 nm and 600 nm wavelength light emitting diode (LED) illumination.

Comparative study of deep neural networks with unsupervised Noise2Noise strategy for noise reduction of optical coherence tomography images.

As a powerful diagnostic tool, optical coherence tomography (OCT) has been widely used in various clinical setting. However, OCT images are susceptible to inherent speckle noise that may contaminate subtle structure information, due to low-coherence interferometric imaging procedure. Many supervised learning-based models have achieved impressive performance in reducing speckle noise of OCT images trained with a large number of noisy-clean paired OCT images, which are not commonly feasible in clinical practice.

Effect of spectral leakage on the image formation of Fourier-domain optical coherence tomography.

We report on the investigation of spectral leakage's impact on the reconstruction of Fourier-domain optical coherence tomography (FD-OCT). We discuss the shift-variant nature introduced by the spectral leakage and develop a novel spatial-domain FD-OCT image formation model. A proof-of-concept phantom experiment is conducted to validate our model.

Weakly Supervised Deep Learning-Based Optical Coherence Tomography Angiography.

Optical coherence tomography angiography (OCTA) is a promising imaging modality for microvasculature studies. Deep learning networks have been widely applied in the field of OCTA reconstruction, benefiting from its powerful mapping capability among images. However, these existing deep learning-based methods depend on high-quality labels, which are hard to acquire considering imaging hardware limitations and practical data acquisition conditions.

N2NSR-OCT: Simultaneous denoising and super-resolution in optical coherence tomography images using semisupervised deep learning.

Optical coherence tomography (OCT) imaging shows a significant potential in clinical routines due to its noninvasive property. However, the quality of OCT images is generally limited by inherent speckle noise of OCT imaging and low sampling rate. To obtain high signal-to-noise ratio (SNR) and high-resolution (HR) OCT images within a short scanning time, we presented a learning-based method to recover high-quality OCT images from noisy and low-resolution OCT images.

Retinal neurovascular responses to transcorneal electrical stimulation measured with optical coherence tomography.

Unlabelled: Noninvasive transcorneal electrical stimulation (TES) has emerged as a potential strategy to facilitate visual restoration and promote retinal cell survival for certain retinal and optic nerve diseases owing to its neuroprotective effects. However, the neurovascular responses of retinal neurons evoked by TES have not been completely determined. To investigate this issue, we utilized a custom-designed spectral-domain optical coherence tomography (SD-OCT) to record the retinal neural and vascular responses under TES simultaneously.

Evaluation of Retinal Pigment Epithelium and Choroidal Neovascularization in Rats Using Laser-Scanning Optical-Resolution Photoacoustic Microscopy.

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.

Tumor Chemo-Radiotherapy with Rod-Shaped and Spherical Gold Nano Probes: Shape and Active Targeting Both Matter.

The morphologies of gold nanoparticles (NPs) affect their tumor accumulation through enhanced permeability and retention effect. However, detailed information and mechanisms of NPs' characteristics affecting tumor accumulation are limited. The aim of this study is to evaluate the effects of shape and active targeting ligands of theranostic NPs on tumor accumulation and therapeutic efficacy, and to elucidate the underlying mechanism.

Depth-Resolved Physiological Response of Retina to Transcorneal Electrical Stimulation Measured With Optical Coherence Tomography.

Transcorneal electrical stimulation (TES) has become an effective strategy to modulate retinal neural activities and partially restore visual function in ophthalmic diseases. However, the exact responses in different retinal layers still need to be clarified. This paper's goal was to evaluate the depth-resolved retinal physiological responses evoked by TES by using optical coherence tomography (OCT).

Comparison of cortical responses to the activation of retina by visual stimulation and transcorneal electrical stimulation.

Background: Electrical stimulation has been widely used in many ophthalmic diseases to modulate neuronal activities or restore partial visual function. Due to the different processing pathways and mechanisms, responses to visual and electrical stimulation in the primary visual cortex and higher visual areas might be different. This differences would shed some light on the properties of cortical responses evoked by electrical stimulation.

Response to Comment on "Retinal pulse wave velocity measurement using spectral-domain optical coherence tomography".

Spahr et al. recently commented on our latest paper "Retinal pulse wave velocity measurement using spectral-domain optical coherence tomography" with a conclusion that the measured retinal pulse wave velocity (rPWV) in our paper was contradictory to theoretical predictions and previously published results. However, the theoretical predictions by Spahr et al.

Retinal pulse wave velocity measurement using spectral-domain optical coherence tomography.

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.

A Multifunctional Platform for Tumor Angiogenesis-Targeted Chemo-Thermal Therapy Using Polydopamine-Coated Gold Nanorods.

Image-guided combined chemo-thermal therapy assists in optimizing treatment time, enhancing therapeutic efficiency, and circumventing side effects. In the present study, we developed a chemo-photothermal theranostic platform based on polydopamine (PDA)-coated gold nanorods (GNRs). The PDA coating was thin; however, it significantly suppressed the cytotoxicity of the cetyltrimethylammonium bromide template and allowed high cisplatin loading efficiency, arginine-glycine-aspartic acid (RGD) peptide (c(RGDyC)) conjugation, and chelator-free iodine-125 labeling (RGD-IPt-PDA@GNRs).