ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2021
Improving axial resolution in Structured Illumination Microscopy using deep learning.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Superresolution structured illumination microscopy reconstruction algorithms: a review.Light, science & applications · 2023Review
- Imaging nanoscale axial dynamics at the basal plasma membrane.The international journal of biochemistry & cell biology · 2023Review
- Super-resolution microscopy: a brief history and new avenues.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- An LED-Based structured illumination microscope using a digital micromirror device and GPU accelerated image reconstruction.PloS one · 2022Article
- Biophysics is reshaping our perception of the epigenome: from DNA-level to high-throughput studies.Biophysical reports · 2021Review
- Super-resolution structured illumination microscopy: past, present and future.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning models, we demonstrate a method to reconstruct 3D SIM image stacks with twice the axial resolution attainable through conventional SIM reconstructions. We further demonstrate our method is robust to noise and evaluate it against two-point cases and axial gratings. Finally, we discuss potential adaptions of the method to further improve resolution. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.
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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.