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. Multidimensional parameters were analysed by Student's t-test. Logistic regression analysis and back-propagation neural network (BPNN) were used to identify the predictive capability for cognitive impairment. RESULTS: This study included 104 cognitive impairment patients (61.5% female; mean (SD) age, 68.3 (9.4) years), and 94 cognitively healthy age-matched and sex-matched subjects (56.4% female; mean (SD) age, 65.9 (7.6) years). The variation of most parameters including decreased pupil constriction amplitude (CA), relative CA, average constriction velocity, venous diameter, venous blood flow and increased centred retinal reflectance in 548 nm (RC548) in cognitive impairment was consistent with previous studies while the reduced flow acceleration index and oxygen metabolism were reported for the first time. Compared with the logistic regression model, BPNN had better predictive performance (accuracy: 0.91 vs 0.69; sensitivity: 93.3% vs 61.70%; specificity: 90.0% vs 68.66%). CONCLUSIONS: This study demonstrates retinal spectral signature alteration, neurodegeneration and angiopathy occur concurrently in cognitive impairment. The combination of multimodal ophthalmic imaging and BPNN can be a useful tool for predicting cognitive impairment with high performance for community screening.

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. Two logistic regression models were developed to predict NPDR using retinal and biochemical metrics, respectively. The predictive model 1 using retinal metrics incorporated male gender, insulin treatment condition, diastolic duration, resistance index, and oxygen extraction fraction presented a similar predictive power with model 2 using biochemical metrics incorporated diabetic duration, diastolic blood pressure, and glycated hemoglobin A1c (area under curve: 0.73 vs. 0.70; sensitivity: 76% vs. 68%; specificity: 64% vs. 62%). These results suggest that retinal oxygen metabolic and hemodynamic biomarkers may replace biochemical parameters to predict the occurrence of NPDR .

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 (Ovis aries) 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). Through high-throughput genome resequencing, we obtained a total of 24,130,801 effective single-nucleotide polymorphisms (SNPs). By conducting a selective-sweep analysis, we discovered that the most significantly selective region was located on chromosome 7. Within this region, we identified several genes, including VRTN, SYNDIG1L, LTBP2, and ABCD4, known to regulate the spinal development and morphology. Further, a genome-wide association study (GWAS) performed on sheep with increased and normal vertebral numbers confirmed that ABCD4 is a candidate gene for determining the number of vertebrae in sheep. Additionally, the most significant SNP on chromosome 7 was identified as a candidate QTL. Moreover, we detected two missense mutations in the ABCD4 gene; one of these mutations (Chr7: 89393414, C > T) at position 22 leads to the conversion of arginine (Arg) to glutamine (Gln), which is expected to negatively affect the protein's function. Notably, a transcriptome expression profile in mouse embryonic development revealed that ABCD4 is highly expressed during the critical period of vertebral formation (4.5-7.5 days). Our study highlights ABCD4 as a potential major gene influencing the number of vertebrae in Ujimqin sheep, with promising prospects for future genome-assisted breeding improvements in sheep.

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. In this study, we introduce UGAN_Resnet_CBAM (URNet; UGAN is a generative adversarial network that uses Unet for feature extraction), a two-stage end-to-end deep learning technique for the automatic detection of diabetic retinopathy. The characteristics of DDR fundus data set were used to design an adaptive image preprocessing module in the first stage. Gradient-weighted Class Activation Mapping (Grad-CAM) and t-distribution and stochastic neighbor embedding (t-SNE) were used as the evaluation indices to analyze the preprocessing results. In the second stage, we enhanced the performance of the Resnet50 network by integrating the convolutional block attention module (CBAM). The outcomes demonstrate that our proposed solution outperformed other current structures, achieving 94.5% and 94.4% precisions, and 96.2% and 91.9% recall for NRDR and RWDR, respectively.

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. In this paper, we proposed an iterative method that enables B-scan-based OCT image reconstruction, which has three major advantages: (1) Large-scale parallelism of the OCT dataset is achieved by using GPU acceleration. (2) A novel image-level cross-domain regularizer was developed, such that the image processing could be performed simultaneously during the image reconstruction; an enhanced image could be directly generated from the OCT interferogram. (3) The scalability of the proposed method was demonstrated for 3D OCT image reconstruction. Compared with the state-of-the-art (SOTA) iterative approaches, the proposed method achieves higher image quality with reduced computational time by orders of magnitude. To further show the image enhancement ability, a comparison was conducted between the proposed method and the conventional workflow, in which an IDFT reconstructed OCT image is later processed by a total variation-regularized denoising algorithm. The proposed method can achieve a better performance evaluated by metrics such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), while the speed is improved by more than 30 times. Real-time image reconstruction at more than 20 B-scans per second was realized with a frame size of 4096 (axial) × 1000 (lateral), which showcases the great potential of the proposed method in real-world applications.

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. Compared with classic LSCI algorithms, SGLSA presents a much clearer perfusion image and higher signal-to-noise ratio pulsatility. The SGLSA algorithm also shows better performance on patients than traditional LSCI methods. The high spatiotemporal resolution provided by the SGLSA algorithm greatly enhances the ability of retinal microcirculation analysis, which makes up for the deficiency of the LSCI technology, and attaches great significance to retinal hemodynamic imaging, biomarker research, and clinical diagnosis.

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. But it's currently not clinically available, particularly via a single imaging device. Therefore, this work aims to develop a retinal oxygen kinetics imaging and analysis (ROKIA) technology by integrating dual-wavelength imaging with laser speckle contrast imaging modalities, which achieves structural and functional analysis with high spatial resolution and dynamic measurement, taking both external and lumen vessel diameters into account. The ROKIA systematically evaluated eight vascular metrics, four blood flow metrics, and fifteen oxygenation metrics. The single device scheme overcomes the incompatibility of optical design, harmonizes the field of view and resolution of different modalities, and reduces the difficulty of registration and image processing algorithms. More importantly, many of the metrics (such as oxygen delivery, oxygen metabolism, vessel wall thickness, etc.) derived from the fusion of structural and functional information, are unique to ROKIA. The oxygen kinetic analysis technology proposed in this paper, to our knowledge, is the first demonstration of the vascular metrics, blood flow metrics, and oxygenation metrics via a single system, which will potentially become a powerful tool for disease diagnosis and clinical research.

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. Hence, it is significant to devise robust training strategies to mitigate label noise in the medical image classification tasks. METHODS: In this work, we propose a novel Bayesian statistics-guided label refurbishment mechanism (BLRM) for DNNs to prevent overfitting noisy images. BLRM utilizes maximum a posteriori probability in the Bayesian statistics and the exponentially time-weighted technique to selectively correct the labels of noisy images. The training images are purified gradually with the training epochs when BLRM is activated, further improving classification performance. RESULTS: Comprehensive experiments on both synthetic noisy images (public OCT & Messidor datasets) and real-world noisy images (ANIMAL-10N) demonstrate that BLRM refurbishes the noisy labels selectively, curbing the adverse effects of noisy data. Also, the anti-noise BLRMs integrated with DNNs are effective at different noise ratio and are independent of backbone DNN architectures. In addition, BLRM is superior to state-of-the-art comparative methods of anti-noise. CONCLUSIONS: These investigations indicate that the proposed BLRM is well capable of mitigating label noise in medical image classification tasks.

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. As a result, the valuable neighborhood information and inherent temporal characteristics of OCTA are not fully utilized. In this paper, we designed a neighborhood information-fused Pseudo-3D U-Net (NI-P3D-U) for OCTA reconstruction. METHODS: The proposed NI-P3D-U was investigated on an in vivo animal dataset by a cross-validation strategy under both fully supervised learning and weakly supervised learning pipelines. To demonstrate the OCTA reconstruction capability of the proposed NI-P3D-U, we compared it with several state-of-the-art methods. RESULTS: The results showed that the proposed network outperformed the state-of-the-art deep learning-based OCTA algorithms in terms of visual quality and quantitative metrics, and demonstrated an effective generalization for different training strategies (fully supervised and weakly supervised) and imaging protocols. Meanwhile, the idea of neighborhood information fusion was also expanded to other network architectures, resulting in significant improvements. CONCLUSIONS: These investigations indicate that the proposed network, which combines the neighborhood information strategy with temporal modeling architecture, is well capable of performing OCTA reconstruction, and has a certain potential for clinical applications.

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. Retinal oximetry was performed using 550 nm and 600 nm LED illumination. LSCI of blood flow was performed using 850 nm laser diode illumination at 82 frames per second. LSCI can visualize retinal and choroidal vasculature without requiring exogenous contrast agents and can provide time-resolved information on blood flow, generating a cardiac pulse waveform from retinal vasculature. The technology can rapidly acquire structural MSI images, retinal oximetry and LSCI blood flow information in a simplified clinical workflow without requiring patients to move between instruments. Results from multiple modalities can be combined and registered to provide structural as well as functional information on the retina. These advances can reduce barriers for clinical adoption, accelerating research using MSI, retinal oximetry and LSCI of blood flow for diagnosis, monitoring and elucidating disease pathogenesis.

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. In this article, we conducted a comparative study to investigate the denoising performance of OCT images over different deep neural networks through an unsupervised Noise2Noise (N2N) strategy, which only trained with noisy OCT samples. Four representative network architectures including U-shaped model, multi-information stream model, straight-information stream model and GAN-based model were investigated on an OCT image dataset acquired from healthy human eyes. The results demonstrated all four unsupervised N2N models offered denoised OCT images with a performance comparable with that of supervised learning models, illustrating the effectiveness of unsupervised N2N models in denoising OCT images. Furthermore, U-shaped models and GAN-based models using UNet network as generator are two preferred and suitable architectures for reducing speckle noise of OCT images and preserving fine structure information of retinal layers under unsupervised N2N circumstances.

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. In this article, we proposed an unprecedented weakly supervised deep learning-based pipeline for OCTA reconstruction task, in the absence of high-quality training labels. The proposed pipeline was investigated on an in vivo animal dataset and a human eye dataset by a cross-validation strategy. Compared with supervised learning approaches, the proposed approach demonstrated similar or even better performance in the OCTA reconstruction task. These investigations indicate that the proposed weakly supervised learning strategy is well capable of performing OCTA reconstruction, and has a certain potential towards clinical applications.

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. We proposed a semisupervised learning approach named N2NSR-OCT, to generate denoised and super-resolved OCT images simultaneously using up- and down-sampling networks (U-Net (Semi) and DBPN (Semi)). Additionally, two different super-resolution and denoising models with different upscale factors (2× and 4×) were trained to recover the high-quality OCT image of the corresponding down-sampling rates. The new semisupervised learning approach is able to achieve results comparable with those of supervised learning using up- and down-sampling networks, and can produce better performance than other related state-of-the-art methods in the aspects of maintaining subtle fine retinal structures.

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. Compared with previous models, the proposed framework could better describe the image formation process, especially when the fineness of the axial structure approaches the theoretical resolution limit.

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. 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.

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. Methods:αvß3 integrin-targeted, cisplatin-loaded and radioisotope iodine-125 labeled spherical and rod-shaped gold nano theranostic probes (RGD-125IPt-AuNPs and RGD-125IPt-AuNRs) with similar sizes were fabricated and characterized. The in vivo distribution and chemo-radio therapeutic efficacy against tumors of these newly developed probes were subsequently evaluated. Moreover, a physiologically based pharmacokinetic (PBPK) model was developed to characterize the in vivo kinetics of these probes at the sub-organ level, and to reveal the mechanism of NPs' shape and active targeting ligands effects on tumor accumulation. Result: Cisplatin and iodine-125 were loaded sequentially onto the NPs through a thin polydopamine coating layer on the NPs. Both RGD-125IPt-AuNPs and RGD-125IPt-AuNRs exhibited high specificity for αvß3 in vitro, with the rod-shaped probe being more efficient. The PBPK model revealed that rod-shaped gold NPs diffused more rapidly in tumor interstitial than the spherical ones. Tumor accumulations of non-targeted and rod-shaped RAD-125IPt-AuNRs was higher in short term (1 h post injection), but not pronounced and similar to that of non-targeted spherical RAD-125IPt-AuNPs in 24 h after intravenous injection, revealing that the NPs' shape did not have a significant impact on tumor accumulations through enhanced permeability and retention (EPR) effect in long-term. While for actively targeted NPs, in addition to a higher distribution coefficient, RGD-125IPt-AuNRs also had a much higher tumor maximum uptake rate constant than RGD-125IPt-AuNPs, indicating both the shape and active targeting ligands affected the tumor uptake of rod-shaped NPs. As a result, RGD-125IPt-AuNRs had a more effective inhibition of tumor growth than RGD-125IPt-AuNPs by chemo-radiationtherapy. Conclusion: Our study suggests that both the shape and active targeting ligands of gold NPs play important roles on tumor accumulation and chemo-radio therapeutic effect.

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). A custom-built spectral-domain OCT system was used to record the intrinsic optical signals (IOSs) in different retinal layers. TES and flickers were used to stimulate the retina electrically and visually. Tetrodotoxin was used to inhibit the retinal neural activity for confirming the origin of TES-induced IOSs. We found both positive and negative IOSs could be evoked by TES in three segmented retinal layers, especially in the inner retina and subretinal space. The TES-induced IOSs correlated with the TES intensity. After tetrodotoxin injection, the IOSs evoked by TES were significantly declined, peculiarly in the inner retina. The IOSs elicited by flickers kept increasing during the stimulation, while those evoked by TES kept at a stable level. In conclusion, TES could elicit IOSs that originated from retinal neural activity in all segmented layers. The TES-induced IOSs were highly synchronized to the electrical field in the retina.

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. OBJECTIVE: This study's goal was to directly compare the cortical responses evoked by visual and electrical stimulation and investigate the cortical processing of visual information and extrinsic electrical signal. METHODS: Optical imaging of intrinsic signals (OIS) was used to probe the cortical hemodynamic responses in 11 cats. Transcorneal electrical stimulation (TES) through an ERG-jet contact lens electrode was used to activate visual cortices. Full-field and peripheral drifting gratings were used as the visual stimuli. RESULTS: The response latency evoked by TES was shorter than that responding to visual stimulation (VS). Cortical responses evoked by VS were retinotopically organized, which was consistent with previous studies. On the other hand, the cortical region activated by TES was preferentially located in the secondary visual cortex (Area 18), while the primary visual cortex (Area 17) was activated by a higher current intensity. Compared with the full-field VS, the cortical response in Area 18 to TES with a current intensity above 1.2 mA was significantly stronger. CONCLUSION: According to our results, we provided some evidence that the cortical processing of TES was influenced by the distribution of the electrical field in the retina and the activating threshold of different retinal ganglion cells.

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. 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.