ArticleNature communications2022
Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 26 citations in OpenAlex.
- Bio-friendly and high-precision super-resolution imaging through self-supervised reconstruction structured illumination microscopy.Nature methods · 2026Article
- High-speed blind structured illumination microscopy via unsupervised algorithm unrolling.Nature communications · 2026Article
- Visualizing intraorganellar ultrastructures, dynamics, and interactions with open-access background-free Lock-in-SIM.Nature communications · 2025Article
- Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas.Nature communications · 2025Article
- Synthesis of Arbitrary Interference Patterns Using a Single Galvanometric Mirror and Its Application to Structured Illumination Microscopy.ACS photonics · 2025Article
- Bayesian deep-learning structured illumination microscopy enables reliable super-resolution imaging with uncertainty quantification.Nature communications · 2025Article
- Fully automated multicolour structured illumination module for super-resolution microscopy with two excitation colours.Communications engineering · 2025Article
- Fast, multicolour optical sectioning over extended fields of view with patterned illumination and machine learning.Biomedical optics express · 2024Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
Structured Illumination Microscopy, SIM, is one of the most powerful optical imaging methods available to visualize biological environments at subcellular resolution. Its limitations stem from a difficulty of imaging in multiple color channels at once, which reduces imaging speed. Furthermore, there is substantial experimental complexity in setting up SIM systems, preventing a widespread adoption. Here, we present Machine-learning Assisted, Interferometric Structured Illumination Microscopy, MAI-SIM, as an easy-to-implement method for live cell super-resolution imaging at high speed and in multiple colors. The instrument is based on an interferometer design in which illumination patterns are generated, rotated, and stepped in phase through movement of a single galvanometric mirror element. The design is robust, flexible, and works for all wavelengths. We complement the unique properties of the microscope with an open source machine-learning toolbox that permits real-time reconstructions to be performed, providing instant visualization of super-resolved images from live biological samples.
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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.