Morphometric, hemodynamic, and biomechanical factors influencing blood flow and oxygen concentration in the human lamina cribrosa

Purpose: We developed a combined biomechanical and hemodynamic model of the human eye to estimate blood flow and oxygen concentration within the lamina cribrosa (LC) and rank the factors that influence LC oxygen concentration. Methods: We generated 5000 finite-element eye models with detailed microcapillary networks of the LC and computed the oxygen concentration of the lamina retinal ganglion cell axons. For each model, we varied the intraocular pressure (IOP) from 10 mm Hg to 55 mm Hg in 5-mm Hg increments, the cerebrospinal fluid pressure (13 ± 2 mm Hg), cup depth (0.2 ± 0.1 mm), scleral stiffness (±20% of the mean values), LC stiffness (0.41 ± 0.2 MPa), LC radius (1.2 ± 0.12 mm), average LC pore size (5400 ± 2400 µm2), the microcapillary arrangement (radial, isotropic, or circumferential), and perfusion pressure (50 ± 9 mm Hg). Blood flow was assumed to originate from the LC periphery and drain via the central retinal vein. Finally, we performed linear regressions to rank the influence of each factor on the LC tissue oxygen concentration. Results: LC radius and perfusion pressure were the most important factors in influencing the oxygen concentration of the LC. IOP was another important parameter, and eyes with higher IOP had higher compressive strain and slightly lower oxygen concentration. In general, superior–inferior regions of the LC had significantly lower oxygen concentration than the nasal–temporal regions, resulting in an hourglass pattern of oxygen deficiency. Conclusions: To the best of our knowledge, this study is the first to implement a comprehensive hemodynamical model of the eye that accounts for the biomechanical forces and morphological parameters of the LC. The results provide further insight into the possible relationship of biomechanical and vascular pathways leading to ischemia-induced optic neuropathy

OCT-GAN: single step shadow and noise removal from optical coherence tomography images of the human optic nerve head

Speckle noise and retinal shadows within OCT B-scans occlude important edges, fine textures and deep tissues, preventing accurate and robust diagnosis by algorithms and clinicians. We developed a single process that successfully removed both noise and retinal shadows from unseen single-frame B-scans within 10.4ms. Mean average gradient magnitude (AGM) for the proposed algorithm was 57.2% higher than current state-of-the-art, while mean peak signal to noise ratio (PSNR), contrast to noise ratio (CNR), and structural similarity index metric (SSIM) increased by 11.1%, 154% and 187% respectively compared to single-frame B-scans. Mean intralayer contrast (ILC) improvement for the retinal nerve fiber layer (RNFL), photoreceptor layer (PR) and retinal pigment epithelium (RPE) layers decreased from 0.362 ± 0.133 to 0.142 ± 0.102, 0.449 ± 0.116 to 0.0904 ± 0.0769, 0.381 ± 0.100 to 0.0590 ± 0.0451 respectively. The proposed algorithm reduces the necessity for long image acquisition times, minimizes expensive hardware requirements and reduces motion artifacts in OCT images

AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing

$\mathbf{Purpose}$: To use artificial intelligence (AI) to: (1) exploit biomechanical knowledge of the optic nerve head (ONH) from a relatively large population; (2) assess ONH robustness from a single optical coherence tomography (OCT) scan of the ONH; (3) identify what critical three-dimensional (3D) structural features make a given ONH robust. $\mathbf{Design}$: Retrospective cross-sectional study. $\mathbf{Methods}$: 316 subjects had their ONHs imaged with OCT before and after acute intraocular pressure (IOP) elevation through ophthalmo-dynamometry. IOP-induced lamina-cribrosa deformations were then mapped in 3D and used to classify ONHs. Those with LC deformations superior to 4% were considered fragile, while those with deformations inferior to 4% robust. Learning from these data, we compared three AI algorithms to predict ONH robustness strictly from a baseline (undeformed) OCT volume: (1) a random forest classifier; (2) an autoencoder; and (3) a dynamic graph CNN (DGCNN). The latter algorithm also allowed us to identify what critical 3D structural features make a given ONH robust. $\mathbf{Results}$: All 3 methods were able to predict ONH robustness from 3D structural information alone and without the need to perform biomechanical testing. The DGCNN (area under the receiver operating curve [AUC]: 0.76 $\pm$ 0.08) outperformed the autoencoder (AUC: 0.70 $\pm$ 0.07) and the random forest classifier (AUC: 0.69 $\pm$ 0.05). Interestingly, to assess ONH robustness, the DGCNN mainly used information from the scleral canal and the LC insertion sites. $\mathbf{Conclusions}$: We propose an AI-driven approach that can assess the robustness of a given ONH solely from a single OCT scan of the ONH, and without the need to perform biomechanical testing. Longitudinal studies should establish whether ONH robustness could help us identify fast visual field loss progressors

Photoacoustic imaging of lamina cribrosa microcapillaries in porcine eyes

Due to the embedded nature of the lamina cribrosa (LC) microcapillary network, conventional imaging techniques have failed to obtain the high-resolution images needed to assess the perfusion state of the LC. In this study, both optical resolution (OR) and acoustic resolution (AR) photoacoustic microscopy (PAM) techniques were used to obtain static and dynamic information about LC perfusion in ex vivo porcine eyes. The OR-PAM system could resolve a perfused LC microcapillary network with a lateral resolution of 4.2 μm and also provided good depth information (33 μm axial resolution) to visualize through-thickness vascular variations. The AR-PAM system was capable of detecting time-dependent perfusion variations. This study represents the first step towards using an emerging imaging modality (PAM) to study the LC’s perfusion, which could be a basis for further investigation of the hemodynamic aspects of glaucomatous optic neuropathy.MOE (Min. of Education, S’pore)Accepted versio

Microcapillary imaging of lamina cribrosa in porcine eyes using photoacoustic microscopy

In order to understand the pathophysiology of glaucoma, Lamina cribrosa (LC) perfusion needs to be the subject of thorough investigation. It is currently difficult to obtain high resolution images of the embedded microcapillary network of the LC using conventional imaging techniques. In this study, an optical resolution photoacoustic microscopy (OR-PAM) system was used for imaging lamina cribrosa of an ex vivo porcine eye. Extrinsic contrast agent was used to perfuse the eye via its ciliary arteries. The OR-PAM system have a lateral resolution of 4 μm and an axial resolution of 30 μm. The high resolution system could able resolve a perfused LC microcapillary network to show vascular structure within the LC thickness. OR-PAM could be a promising imaging modality to study the LC perfusion and hence could be used to elucidate the hemodynamic aspect of glaucoma.MOE (Min. of Education, S’pore)Published versio