Revisiting 35 and 94 GHZ Millimeter Wave Exposure to the Non-Human Primate Eye.

A previous study reported thermal effects resulting from millimeter wave exposures at 35 and 94 GHz on non-human primates, specifically rhesus monkeys' (Macaca mulatta) corneas, but the data exhibited large variations in the observed temperatures and uncertainties in the millimeter wave dosimetry. By incorporating improvements in models and dosimetry, a non-human primate experiment was conducted involving corneal exposures that agreed well with a three-layer, one-dimensional, thermodynamic model to predict the expected surface temperature rise. The new data indicated that the originally reported safety margins for eye exposures were underestimated by 41 ± 20% over the power densities explored. As a result, the expected minimal visible lesion thresholds should be raised to 10.6 ± 1.5 and 7.1 ± 1.0 J cm at 35 and 94 GHz, respectively, provided that the power density is less than 6 W cm for subjects that are unable to blink. If the blink reflex was active, a power density threshold of 20 W cm could be used to protect the eye, although the eyelid could be burned if the exposure was long enough.

A Deep-Learning Approach for Automated OCT En-Face Retinal Vessel Segmentation in Cases of Optic Disc Swelling Using Multiple En-Face Images as Input.

Purpose: In cases of optic disc swelling, segmentation of projected retinal blood vessels from optical coherence tomography (OCT) volumes is challenging due to swelling-based shadowing artifacts. Based on our hypothesis that simultaneously considering vessel information from multiple projected retinal layers can substantially increase vessel visibility, in this work, we propose a deep-learning-based approach to segment vessels involving the simultaneous use of three OCT en-face images as input. Methods: A human expert vessel tracing combining information from OCT en-face images of the retinal pigment epithelium (RPE), inner retina, and total retina as well as a registered fundus image served as the reference standard. The deep neural network was trained from the imaging data from 18 patients with optic disc swelling to output a vessel probability map from three OCT en-face input images. The vessels from the OCT en-face images were also manually traced in three separate stages to compare with the performance of the proposed approach. Results: On an independent volume-matched test set of 18 patients, the proposed deep-learning-based approach outperformed the three OCT-based manual tracing stages. The manual tracing based on three OCT en-face images also outperformed the manual tracing using only the traditional RPE en-face image. Conclusions: In cases of optic disc swelling, use of multiple en-face images enables better vessel segmentation when compared with the traditional use of a single en-face image. Translational Relevance: Improved vessel segmentation approaches in cases of optic disc swelling can be used as features for an improved assessment of the severity and cause of the swelling.