ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2021
GPU-accelerated real-time reconstruction in Python of three-dimensional datasets from structured illumination microscopy with hexagonal patterns.
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 8 papers.
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Who cites it
8 citing papers in PubMed.
- Amplitude- and Phase-Programmable Dual-Color Photonic Chip for High-Contrast Structured Illumination Microscopy.ACS photonics · 2026Article
- Fully automated multicolour structured illumination module for super-resolution microscopy with two excitation colours.Communications engineering · 2025Article
- Facile Conversion and Optimization of Structured Illumination Image Reconstruction Code into the GPU Environment.International journal of biomedical imaging · 2024Article
- Superresolution structured illumination microscopy reconstruction algorithms: a review.Light, science & applications · 2023Review
- An open-source microscopy framework for simultaneous control of image acquisition, reconstruction, and analysis.HardwareX · 2023Article
- 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
- Improving axial resolution in Structured Illumination Microscopy using deep learning.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
We present a structured illumination microscopy system that projects a hexagonal pattern by the interference among three coherent beams, suitable for implementation in a light-sheet geometry. Seven images acquired as the illumination pattern is shifted laterally can be processed to produce a super-resolved image that surpasses the diffraction-limited resolution by a factor of over 2 in an exemplar light-sheet arrangement. Three methods of processing data are discussed depending on whether the raw images are available in groups of seven, individually in a stream or as a larger batch representing a three-dimensional stack. We show that imaging axially moving samples can introduce artefacts, visible as fine structures in the processed images. However, these artefacts are easily removed by a filtering operation carried out as part of the batch processing algorithm for three-dimensional stacks. The reconstruction algorithms implemented in Python include specific optimizations for calculation on a graphics processing unit and we demonstrate its operation on experimental data of static objects and on simulated data of moving objects. We show that the software can process over 239 input raw frames per second at 512 × 512 pixels, generating over 34 super-resolved frames per second at 1024 × 1024 pixels. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.
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