A high-accuracy and high-efficiency digital volume correlation method to characterize in-vivo optic nerve head biomechanics from optical coherence tomography

The Robust Lamina Cribrosa Vasculature: Perfusion and Oxygenation Under Elevated Intraocular Pressure

Purpose

Elevated intraocular pressure (IOP) is thought to cause lamina cribrosa (LC) blood vessel distortions and potentially collapse, adversely affecting LC hemodynamics, reducing oxygenation, and triggering, or contributing to, glaucomatous neuropathy. We assessed the robustness of LC perfusion and oxygenation to vessel collapses.

Methods

From histology, we reconstructed three-dimensional eye-specific LC vessel networks of two healthy monkey eyes. We used numerical simulations to estimate LC perfusion and from this the oxygenation. We then evaluated the effects of collapsing a fraction of LC vessels (0%-36%). The collapsed vessels were selected through three scenarios: stochastic (collapse randomly), systematic (collapse strictly by the magnitude of local experimentally determined IOP-induced compression), and mixed (a combination of stochastic and systematic).

Results

LC blood flow decreased linearly as vessels collapsed-faster for stochastic and mixed scenarios and slower for the systematic one. LC regions suffering severe hypoxia (oxygen <8 mm Hg) increased proportionally to the collapsed vessels in the systematic scenario. For the stochastic and mixed scenarios, severe hypoxia did not occur until 15% of vessels collapsed. Some LC regions had higher perfusion and oxygenation as vessels collapsed elsewhere. Some severely hypoxic regions maintained normal blood flow. Results were equivalent for both networks and patterns of experimental IOP-induced compression.

Conclusions

LC blood flow was sensitive to distributed vessel collapses (stochastic and mixed) and moderately vulnerable to clustered collapses (systematic). Conversely, LC oxygenation was robust to distributed vessel collapses and sensitive to clustered collapses. Locally normal flow does not imply adequate oxygenation. The actual nature of IOP-induced vessel collapse remains unknown.

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.

High-accuracy optical coherence elastography digital volume correlation methods to measure depth regions with low correlation

The decreasing correlation of optical coherence tomography (OCT) images with depth is an unavoidable problem for the depth measurement of the digital volume correlation (DVC) based optical coherence elastography (OCE) method. We propose an OCE-DVC method to characterize biological tissue deformation in deeper regions. The method proposes a strategy based on reliability layer guided displacement tracking to achieve the OCE-DVC method for the deformation measurement in deep regions of OCT images. Parallel computing solves the computational burden associated with the OCE-DVC method. The layer-by-layer adaptive data reading methods are used to guarantee the parallel computing of high-resolution OCT images. The proposed method shown in this study nearly doubles the depth of quantitative characterization of displacement and strain. At this depth, the standard deviation of displacement and strain measurements is reduced by nearly 78%. Under nonuniform deformation field, OCE-DVC method tracked the displacement with large strain gradient in depth region.

Optical Coherence Tomography Angiography Demonstrates Strain and Volume Effects on Optic Disk and Peripapillary Vasculature Caused by Horizontal Duction

PURPOSE

The optic nerve mechanically loads the eye during ocular rotation, thus altering the configuration of the disk and peripapillary tissues. We used optical coherence tomography (OCT) angiography (OCTA) to investigate mechanical strains and volume changes in disk and peripapillary blood vessels during horizontal duction.

METHODS

Structural OCT and OCTA were performed centered on optic disks; imaging was repeated in central gaze, and in 30° ab- and adduction. By an algorithm employing point-set registration of 3 D features, we developed a novel approach for measuring disk strains, and strains and volumes of the blood vessels associated with horizontal duction. Repeatability was demonstrated in each gaze position.

RESULTS

19 eyes of 10 healthy adults of average age 37 ± 15 (standard deviation, SD) years were imaged. The method was validated by demonstrating numerically consistent vascular volumes and strains for repeated imaging under identical conditions. Compared with central gaze, vascular volume increased by 5.2 ± 4.1% in adduction. Adduction and abduction caused strains of 3.0 ± 1.6% and 2.6 ± 1.8% in the optic disk, whereas blood vessels showed greater strains of 8.1 ± 1.3% and 8.2 ± 1.7%. Decomposition of strain components depending on directionality and regions demonstrated that adduction induces significant net tensile strains, suggesting traction exerted by the optic nerve. The decomposition also showed that nasotemporal compressive strains are larger in temporal hemidisks than nasal hemidisks. The Bruch's membrane opening was significantly compressed horizontally in adduction by 1.1% (p = .009).

CONCLUSION

This novel analysis combining structural OCT and OCTA demonstrates that optic disk compression during adduction is associated with disk and vascular strains much larger than reported for intraocular pressure elevation and pulsatile perfusion, as well as compressing the disk and increasing peripapillary vascular volume. These changes may be relevant to the pathogenesis of optic nerve and retinal vascular disorders.

Reverse translation of artificial intelligence in glaucoma: Connecting basic science with clinical applications

Artificial intelligence (AI) has been approved for biomedical research in diverse areas from bedside clinical studies to benchtop basic scientific research. For ophthalmic research, in particular glaucoma, AI applications are rapidly growing for potential clinical translation given the vast data available and the introduction of federated learning. Conversely, AI for basic science remains limited despite its useful power in providing mechanistic insight. In this perspective, we discuss recent progress, opportunities, and challenges in the application of AI in glaucoma for scientific discoveries. Specifically, we focus on the research paradigm of reverse translation, in which clinical data are first used for patient-centered hypothesis generation followed by transitioning into basic science studies for hypothesis validation. We elaborate on several distinctive areas of research opportunities for reverse translation of AI in glaucoma including disease risk and progression prediction, pathology characterization, and sub-phenotype identification. We conclude with current challenges and future opportunities for AI research in basic science for glaucoma such as inter-species diversity, AI model generalizability and explainability, as well as AI applications using advanced ocular imaging and genomic data.