Open AccessSuper-resolution ophthalmoscopy: Virtually structured detection for resolution improvement in retinal imaging
Author(s) -
Xincheng Yao,
Rongwen Lu,
Benquan Wang,
Yiming Lu,
Tae-Hoon Kim
Publication year - 2020
Publication title -
experimental biology and medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.012
H-Index - 146
eISSN - 1535-3702
pISSN - 1535-3699
DOI - 10.1177/1535370220970533
Subject(s) - scanning laser ophthalmoscopy , ophthalmoscopy , artifact (error) , computer science , optics , microscopy , resolution (logic) , retinal , image resolution , computer vision , adaptive optics , retina , artificial intelligence , medicine , ophthalmology , physics
Quantitative retinal imaging is essential for advanced study and clinical management of eye diseases. However, spatial resolution of retinal imaging has been limited due to available numerical aperture and optical aberration of the ocular optics. Structured illumination microscopy has been established to break the diffraction-limit resolution in conventional light microscopy. However, practical implementation of structured illumination microscopy for in vivo ophthalmoscopy of the retina is challenging due to inevitable eye movements that can produce phase artifacts. Recently, we have demonstrated the feasibility of using virtually structured detection as one alternative to structured illumination microscopy for super-resolution imaging. By providing the flexibility of digital compensation of eye movements, the virtually structured detection provides a feasible, phase-artifact-free strategy to achieve super-resolution ophthalmoscopy. In this article, we summarize the technical rationale of virtually structured detection, and its implementations for super-resolution imaging of freshly isolated retinas, intact animals, and awake human subjects.