ArticleBiomedical optics express2013
Super-resolution scanning laser microscopy through virtually structured detection.
Article in Biomedical optics express, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 56 citations in OpenAlex.
- Article
- Resolution-enhanced OCT and expanded framework of information capacity and resolution in coherent imaging.Scientific reports · 2021Article
- Review of bio-optical imaging systems with a high space-bandwidth product.Advanced photonics · 2021Article
- Virtually structured detection enables super-resolution ophthalmoscopy of rod and cone photoreceptors in human retina.Quantitative imaging in medicine and surgery · 2021Article
- Rapid Image Reconstruction of Structured Illumination Microscopy Directly in the Spatial Domain.IEEE photonics journal · 2021Article
- Super-resolution ophthalmoscopy: Virtually structured detection for resolution improvement in retinal imaging.Experimental biology and medicine (Maywood, N.J.) · 2021Review
- Virtual single-pixel imaging-based deconvolution method for spatial resolution improvement in wide-field fluorescence microscopy.Biomedical optics express · 2020Article
- Structured illumination imaging without grating rotation based on mirror operation on 1D Fourier spectrum.Optics express · 2019Article
- Connectomics of synaptic microcircuits: lessons from the outer retina.The Journal of physiology · 2017Review
- In vivo super-resolution retinal imaging through virtually structured detection.Journal of biomedical optics · 2016Article
- Image recombination transform algorithm for superresolution structured illumination microscopy.Journal of biomedical optics · 2016Article
- Virtual k-Space Modulation Optical Microscopy.Physical review letters · 2016Article
- Intrinsic optical signal imaging of retinal physiology: a review.Journal of biomedical optics · 2015Review
- Rapid super-resolution line-scanning microscopy through virtually structured detection.Optics letters · 2015Article
- Super-Resolution Scanning Laser Microscopy Based on Virtually Structured Detection.Critical reviews in biomedical engineering · 2015Review
- Breaking diffraction limit of lateral resolution in optical coherence tomography.Quantitative imaging in medicine and surgery · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
High resolution microscopy is essential for advanced study of biological structures and accurate diagnosis of medical diseases. The spatial resolution of conventional microscopes is light diffraction limited. Structured illumination has been extensively explored to break the diffraction limit in wide field light microscopy. However, deployable application of the structured illumination in scanning laser microscopy is challenging due to the complexity of the illumination system and possible phase errors in sequential illumination patterns required for super-resolution reconstruction. We report here a super-resolution scanning laser imaging system which employs virtually structured detection (VSD) to break the diffraction limit. Without the complexity of structured illumination, VSD provides an easy, low-cost and phase-artifact free strategy to achieve super-resolution in scanning laser microscopy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.