Effect of Changing Heart Rate on the Ocular Pulse and Dynamic Biomechanical Behavior of the Optic Nerve Head

Time-Resolved Dynamic Optical Coherence Tomography for Retinal Blood Flow Analysis

Purpose

Optical coherence tomography (OCT) representations in clinical practice are static and do not allow for a dynamic visualization and quantification of blood flow. This study aims to present a method to analyze retinal blood flow dynamics using time-resolved structural OCT.

Methods

We developed novel imaging protocols to acquire video-rate time-resolved OCT B-scans (1024 × 496 pixels, 10 degrees field of view) at four different sensor integration times (integration time of 44.8 µs at a nominal A-scan rate of 20 kHz, 22.4 µs at 40 kHz, 11.2 µs at 85 kHz, and 7.24 µs at 125 kHz). The vessel centers were manually annotated for each B-scan and surrounding subvolumes were extracted. We used a velocity model based on signal-to-noise ratio (SNR) drops due to fringe washout to calculate blood flow velocity profiles in vessels within five optic disc diameters of the optic disc rim.

Results

Time-resolved dynamic structural OCT revealed pulsatile SNR changes in the analyzed vessels and allowed the calculation of potential blood flow velocities at all integration times. Fringe washout was stronger in acquisitions with longer integration times; however, the ratio of the average SNR to the peak SNR inside the vessel was similar across all integration times.

Conclusions

We demonstrated the feasibility of estimating blood flow profiles based on fringe washout analysis, showing pulsatile dynamics in vessels close to the optic nerve head using structural OCT. Time-resolved dynamic OCT has the potential to uncover valuable blood flow information in clinical settings.

Denoising OCT videos based on temporal redundancy

The identification of eye diseases and their progression often relies on a clear visualization of the anatomy and on different metrics extracted from Optical Coherence Tomography (OCT) B-scans. However, speckle noise hinders the quality of rapid OCT imaging, hampering the extraction and reliability of biomarkers that require time series. By synchronizing the acquisition of OCT images with the timing of the cardiac pulse, we transform a low-quality OCT video into a clear version by phase-wrapping each frame to the heart pulsation and averaging frames that correspond to the same instant in the cardiac cycle. Here, we compare the performance of our one-cycle denoising strategy with a deep-learning architecture, Noise2Noise, as well as classical denoising methods such as BM3D and Non-Local Means (NLM). We systematically analyze different image quality descriptors as well as region-specific metrics to assess the denoising performance based on the anatomy of the eye. The one-cycle method achieves the highest denoising performance, increases image quality and preserves the high-resolution structures within the eye tissues. The proposed workflow can be readily implemented in a clinical setting.

Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma

Purpose

(1) To assess the performance of geometric deep learning in diagnosing glaucoma from a single optical coherence tomography (OCT) scan of the optic nerve head and (2) to compare its performance to that obtained with a three-dimensional (3D) convolutional neural network (CNN), and with a gold-standard parameter, namely, the retinal nerve fiber layer (RNFL) thickness.

Methods

Scans of the optic nerve head were acquired with OCT for 477 glaucoma and 2296 nonglaucoma subjects. All volumes were automatically segmented using deep learning to identify seven major neural and connective tissues. Each optic nerve head was then represented as a 3D point cloud with approximately 1000 points. Geometric deep learning (PointNet) was then used to provide a glaucoma diagnosis from a single 3D point cloud. The performance of our approach (reported using the area under the curve [AUC]) was compared with that obtained with a 3D CNN, and with the RNFL thickness.

Results

PointNet was able to provide a robust glaucoma diagnosis solely from a 3D point cloud (AUC = 0.95 ± 0.01).The performance of PointNet was superior to that obtained with a 3D CNN (AUC = 0.87 ± 0.02 [raw OCT images] and 0.91 ± 0.02 [segmented OCT images]) and with that obtained from RNFL thickness alone (AUC = 0.80 ± 0.03).

Conclusions

We provide a proof of principle for the application of geometric deep learning in glaucoma. Our technique requires significantly less information as input to perform better than a 3D CNN, and with an AUC superior to that obtained from RNFL thickness.

Translational Relevance

Geometric deep learning may help us to improve and simplify diagnosis and prognosis applications in glaucoma.

Mapping Pulsatile Optic Nerve Head Deformation Using OCT

OBJECTIVE

To develop a noninvasive technique to quantitatively assess the pulsatile deformation due to cardiac contractions of the optic nerve head (ONH).

DESIGN

Evaluation of a diagnostic test or technology.

PARTICIPANTS

Healthy subjects with no history of refractive surgery, divided into 2 cohorts on the basis of their axial length (AL).

METHODS

We present a noninvasive technique to quantitatively assess the pulsatile deformation of the ONH tissue by combining high-frequency OCT imaging and widely available image processing algorithms. We performed a thorough validation of the approach, numerically and experimentally, evaluating the sensitivity of the method to artificially induced deformation and its robustness to different noise levels. We performed deformation measurements in cohorts of healthy (n = 9) and myopic (n = 5) subjects in different physiological strain conditions by calculating the amplitude of tissue displacement in both the primary position and abduction. The head rotation was measured using a goniometer. During imaging in abduction, the head was rotated 40° ± 3°, and subjects were instructed to direct their gaze toward the OCT visual target.

MAIN OUTCOME MEASURES

Pulsatile tissue displacement maps.

RESULTS

The robustness of the method was assessed using artificial deformations and increasing noise levels. The results show acceptable absolute errors before the noise simulations grossly exaggerate image degradation. For the group of subjects with AL of < 25 mm (n = 9), the median pulsatile displacement of the ONH was 7.8 ± 1.3 μm in the primary position and 8.9 ± 1.2 μm in abduction. The Wilcoxon test showed a significant difference (P ≤ 0.005) between the 2 paired measures. Reproducibility was tested in 2 different sessions in 5 different subjects with the same intraocular pressure, and an intraclass correlation coefficient of 0.99 was obtained (P < 0.005).

CONCLUSIONS

The computational pipeline demonstrated good reproducibility and had the capacity to accurately map the pulsatile deformation of the optic nerve. In a clinical setting, we detected physiological changes in normal subjects supporting its translation potential as a novel biomarker for the diagnosis and progression of optic nerve diseases.

Biomechanical analysis of ocular diseases and its in vitro study methods

Ocular diseases are closely related to the physiological changes in the eye sphere and its contents. Using biomechanical methods to explore the relationship between the structure and function of ocular tissue is beneficial to reveal the pathological processes. Studying the pathogenesis of various ocular diseases will be helpful for the diagnosis and treatment of ocular diseases. We provide a critical review of recent biomechanical analysis of ocular diseases including glaucoma, high myopia, and diabetes. And try to summarize the research about the biomechanical changes in ocular tissues (e.g., optic nerve head, sclera, cornea, etc.) associated with those diseases. The methods of ocular biomechanics research in vitro in recent years are also reviewed, including the measurement of biomechanics by ophthalmic equipment, finite element modeling, and biomechanical analysis methods. And the preparation and application of microfluidic eye chips that emerged in recent years were summarized. It provides new inspiration and opportunity for the pathogenesis of eye diseases and personalized and precise treatment.

Computational Modeling of Ophthalmic Procedures: Computational Modeling of Ophthalmic Procedures

PURPOSE

To explore how finite-element calculations can continue to contribute to diverse problems in ophthalmology and vision science, we describe our recent work on modeling the force on the peripheral retina in intravitreal injections and how that force increases with shorter, smaller gauge needles. We also present a calculation that determines the location and stress on a retinal pigment epithelial detachment during an intravitreal injection, the possibility that stress induced by the injection can lead to a tear of the retinal pigment epithelium.

BACKGROUND

Advanced computational models can provide a critical insight into the underlying physics in many surgical procedures, which may not be intuitive.

METHODS

The simulations were implemented using COMSOL Multiphysics. We compared the monkey retinal adhesive force of 18 Pa with the results of this study to quantify the maximum retinal stress that occurs during intravitreal injections.

CONCLUSIONS

Currently used 30-gauge needles produce stress on the retina during intravitreal injections that is only slightly below the limit that can create retinal tears. As retina specialists attempt to use smaller needles, the risk of complications may increase. In addition, we find that during an intravitreal injection, the stress on the retina in a pigment epithelial detachment occurs at the edge of the detachment (found clinically), and the stress is sufficient to tear the retina. These findings may guide physicians in future clinical research. NOTE: Publication of this article is sponsored by the American Ophthalmological Society.

Changes in physiological network connectivity of body system in narcolepsy during REM sleep

BACKGROUND

Narcolepsy is marked by pathologic symptoms including excessive daytime drowsiness and lethargy, even with sufficient nocturnal sleep. There are two types of narcolepsy: type 1 (with cataplexy) and type 2 (without cataplexy). Unlike type 1, for which hypocretin is a biomarker, type 2 narcolepsy has no adequate biomarker to identify the causality of narcoleptic phenomenon. Therefore, we aimed to establish new biomarkers for narcolepsy using the body's systemic networks.

METHOD

Thirty participants (15 with type 2 narcolepsy, 15 healthy controls) were included. We used the time delay stability (TDS) method to examine temporal information and determine relationships among multiple signals. We quantified and analyzed the network connectivity of nine biosignals (brainwaves, cardiac and respiratory information, muscle and eye movements) during nocturnal sleep. In particular, we focused on the differences in network connectivity between groups according to sleep stages and investigated whether the differences could be potential biomarkers to classify both groups by using a support vector machine.

RESULT

In rapid eye movement sleep, the narcolepsy group displayed more connections than the control group (narcolepsy connections: 24.47 ± 2.87, control connections: 21.34 ± 3.49; p = 0.022). The differences were observed in movement and cardiac activity. The performance of the classifier based on connectivity differences was a 0.93 for sensitivity, specificity and accuracy, respectively.

CONCLUSION

Network connectivity with the TDS method may be used as a biomarker to identify differences in the systemic networks of patients with narcolepsy type 2 and healthy controls.