ArticleBiomedical optics express2018
Inverse matrix based phase estimation algorithm for structured illumination microscopy.
Article in Biomedical optics express, 2018. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 42 citations in OpenAlex.
- Visualizing intraorganellar ultrastructures, dynamics, and interactions with open-access background-free Lock-in-SIM.Nature communications · 2025Article
- Fast, multicolour optical sectioning over extended fields of view with patterned illumination and machine learning.Biomedical optics express · 2024Article
- Superresolution structured illumination microscopy reconstruction algorithms: a review.Light, science & applications · 2023Review
- Structured Illumination Microscopy of Mitochondrial in Mouse Hepatocytes with an Improved Image Reconstruction Algorithm.Micromachines · 2023Article
- Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging.Nature communications · 2022Article
- An LED-Based structured illumination microscope using a digital micromirror device and GPU accelerated image reconstruction.PloS one · 2022Article
- ML-SIM: universal reconstruction of structured illumination microscopy images using transfer learning.Biomedical optics express · 2021Article
- High-fidelity structured illumination microscopy by point-spread-function engineering.Light, science & applications · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The fast imaging speed and low-intensity requirement of structured illumination microscopy (SIM) have made it one of the most widely used imaging tools in live cell imaging. In order to obtain a high fidelity reconstructed image, a precise estimation of the phase of the illumination pattern is required, especially in those structured illumination based techniques that rely on high-order harmonics to improve the resolution. This can be achieved in one of two fundamental ways. The first is to build a high-end control system capable of shifting a sinusoidal pattern with high precision, while the second is to apply estimation algorithms to determine how patterns shift during post-processing. The latter method is preferred in low-cost super-resolution imaging systems; however, existing algorithms are either time-consuming or fail due to noise and a low modulation depth. In this paper, we introduce additional matrixes into the phase estimation algorithm and propose an inverse matrix based phase estimation method with which analytical solutions of the phases can be determined without iteration. The proposed algorithm was validated via simulation and experiments using a home-made total internal reflection fluorescent SIM system (TIRF-SIM). When tested, the method obtained the true phase even when the modulation depth was low. The source code is now available for download by researchers and others.
Identifiers
What OpenQuestion holds
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.