Structured Light Microscopy Reconstruction for Low-SNR Live Cell Imaging
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Solution Overview
Problem
Structured illumination microscopy (SIM) technologies face challenges in denoising low signal-to-noise ratio (SNR) images of live cells due to low fluorescence labeling efficiency, low excitation illumination intensity, and high cell movement, leading to severe reconstruction artifacts and impaired image quality.
Innovation Solution
A self-supervised multimodal structured illumination microscopic reconstruction method using pixel realignment and a denoising neural network trained without high SNR images, generating a training set from a single capture of live cell images to enhance image denoising and super-resolution reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised training with high SNR-low SNR image pairs is used to train denoising neural networks, then denoising performance is improved, but phototoxicity to live cells increases and experimental cost increases
Solution Approach 1:
The patent implements self-supervised learning where the neural network trains itself using only low SNR images from a single capture. The network learns to distinguish noise from signal by processing the same image multiple times with different processing paths, eliminating the need for high SNR reference images that would require additional high-intensity illumination and multiple samplings of live cells.
Solution Approach 2:
The patent segments the low SNR image into multiple sub-images through pixel realignment and super-resolution reconstruction processes. By dividing the image processing into multiple stages (pixel realignment, super-resolution reconstruction, denoising), the system can train the neural network effectively without requiring external high SNR data, thus avoiding additional phototoxicity.
2Measurement precision
If multiple samplings of the same live sample are performed to collect training data, then training set quality is improved, but temporal resolution decreases
Solution Approach 1:
The system performs self-supervised training using only a single capture of live cell images. By processing the same low SNR image through multiple computational paths (pixel realignment, super-resolution, denoising variations), the network generates its own training data internally, eliminating the need for multiple physical samplings of live cells that would reduce temporal resolution.
Solution Approach 2:
The patent creates multiple virtual copies of the same low SNR image through computational processing (pixel realignment, super-resolution reconstruction). These computational copies serve as training data substitutes for physical multiple samplings, allowing the network to learn from diverse processed versions of the same original image without requiring additional live cell captures.
3Object-affected harmful factors
If low excitation illumination intensity is used to avoid damaging live cells, then phototoxicity is reduced, but fluorescence signal intensity decreases
Solution Approach 1:
The patent replaces the physical/optical approach of increasing illumination intensity to improve signal with a computational approach. The neural network, trained through self-supervised learning, computationally enhances the low SNR signal obtained from low-intensity illumination, effectively substituting optical amplification with intelligent signal processing that preserves live cell viability.
Solution Approach 2:
The system changes the parameter of illumination intensity to remain low (reducing phototoxicity) while compensating for the resulting low signal intensity through computational parameters. The neural network's denoising capabilities and super-resolution reconstruction algorithms enhance the quality of images acquired under low-intensity illumination, allowing the system to maintain low phototoxicity while achieving high-quality images.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves image quality and reduces experimental costs by constructing a training set from raw data, minimizing phototoxicity and enabling high-speed dynamic imaging of live cells.
Implementation Method 1
exciting a biological sample with structured illumination to obtain J raw fluorescence image sequences (Y)
Data Source
Figure 1~2B
Figure 3
Figure 4A~4B
AI summary
Disclosed in the present application are a self-supervised multi-modal structured light microscopy reconstruction method and system. The method comprises: using structured light to excite a biological sample, so as to acquire J original fluorescence image sequences (Y) which are generated from the excitation of the biological sample, wherein J is an integer greater than or equal to 1, each original fluorescence image sequence (Y) comprises S fluorescence images, S being an integer greater than or equal to 2, and each fluorescence image has a pixel size of M*N, with M and N being even numbers; generating a training set for each original fluorescence image sequence (Y) among the J original fluorescence image sequences (Y); training a denoising neural network on the basis of the training set; and by using a standard structured light super-resolution reconstruction algorithm, performing super-resolution reconstruction on the S fluorescence images in each original fluorescence image sequence (Y) among the J original fluorescence image sequences (Y), so as to form a super-resolution image, and using the super-resolution image as an input for the denoising neural network, so as to obtain a final super-resolution reconstructed image.