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Updated: Mar 14, 2026
Super-resolution Imaging of the Cytokinetic Z Ring in Live Bacteria Using Fast 3D-Structured Illumination Microscopy f3D-SIM
Published on: September 29, 2014
Motion artefact detection in structured illumination microscopy for live cell imaging
Abstract:
The reconstruction process of structured illumination microscopy (SIM) creates substantial artefacts if the specimen has moved during the acquisition. This reduces the applicability of SIM for live cell imaging, because these artefacts cannot always be recognized as such in the final image. A movement is not necessarily visible in the raw data, due to the varying excitation patterns and the photon noise. We present a method to detect motion by extracting and comparing two independent 3D wide-field images out of the standard SIM raw data without needing additional images. Their difference reveals moving objects overlaid with noise, which are distinguished by a probability theory-based analysis. Our algorithm tags motion-artefacts in the final high-resolution image for the first time, preventing the end-user from misinterpreting the data. We show and explain different types of artefacts and demonstrate our algorithm on a living cell.
Insights
Structured illumination microscopy (SIM) can create motion artifacts in live cell imaging. This study introduces a novel method to detect these artifacts directly from standard SIM data, improving image reliability.
Area of Science:
- Microscopy
- Biophysics
- Image Analysis
Background:
- Structured illumination microscopy (SIM) is powerful for high-resolution imaging.
- Specimen movement during SIM acquisition generates artifacts, limiting live cell applications.
- These artifacts are often indistinguishable in the final high-resolution image.
Purpose of the Study:
- To develop a method for detecting motion artifacts in SIM data.
- To improve the reliability of SIM for live cell imaging.
- To prevent misinterpretation of microscopy data due to motion artifacts.
Main Methods:
- Extracting and comparing two independent 3D wide-field images from standard SIM raw data.
- Utilizing probability theory-based analysis to distinguish motion-induced noise.
- Developing an algorithm to tag motion artifacts in the final reconstructed image.
Main Results:
- The method successfully detects motion artifacts without requiring additional imaging.
- Moving objects and noise are identified through image differencing and probabilistic analysis.
- The algorithm effectively tags artifacts in the high-resolution SIM images.
Conclusions:
- The presented method reliably detects motion artifacts in SIM data.
- This technique enhances the applicability of SIM for live cell imaging by ensuring data integrity.
- The algorithm provides a crucial tool for researchers to avoid misinterpreting microscopy results.
