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A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

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Untrained neural network enabling fast and universal structured-illumination microscopy.

Zitong Ye, Xiaoyan Li, Yile Sun

|May 1, 2024

Summary

This study introduces an untrained neural network for structured-illumination microscopy (SIM) that significantly reduces data requirements for super-resolution imaging. The method reconstructs high-fidelity images using fewer frames, enhancing temporal resolution in microscopy.

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Area of Science:

Microscopy
Optical Imaging
Computational Imaging

Background:

Structured-illumination microscopy (SIM) enhances resolution beyond the optical diffraction limit.
Current SIM reconstruction, especially for speckle SIM, requires numerous raw frames, limiting temporal resolution.
Reconstruction of speckle SIM is particularly time-consuming due to the need for hundreds of frames.

Purpose of the Study:

To develop an untrained neural network for SIM reconstruction to reduce raw data requirements.
To improve the temporal resolution of speckle SIM by minimizing the number of acquired frames.
To achieve high-fidelity super-resolution (SR) images with significantly less input data.

Main Methods:

Proposed an untrained structured-illumination reconstruction neural network (USRNN) utilizing known illumination patterns.
Employed an unsupervised optimizing strategy combined with Convolutional Neural Networks (CNNs) structure priors.
Reduced the required raw data for speckle SIM reconstruction by 20 times.

Main Results:

Reconstructed high-fidelity SR images with approximately twofold resolution enhancement using five frames or less.
Demonstrated high-frequency information acquisition without the need for training datasets.
Achieved high-speed reconstruction and high universality across non-biological and biological samples.

Conclusions:

The USRNN method significantly accelerates SIM reconstruction, particularly for speckle SIM.
The approach enables high-resolution imaging with substantially reduced data acquisition, improving temporal resolution.
The method shows broad applicability and high performance for various sample types.