Self-supervised multimodal structured illumination microscopic reconstruction method and system
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Solution Overview
Problem
Structured illumination microscopy (SIM) technologies face challenges in producing high-quality super-resolution images due to low signal-to-noise ratios (SNR) in raw images, leading to reconstruction artifacts and difficulty in distinguishing real sample information from noise, especially when imaging live cells.
Innovation Solution
A self-supervised multimodal structured illumination microscopic reconstruction method using pixel realigning and denoising neural networks, which generates a training set from a single capture of live cell images without requiring high SNR images, enabling effective denoising and super-resolution reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard SIM reconstruction algorithms are used on low SNR raw images, then super-resolution images can be obtained, but reconstruction artifacts are significantly amplified
Solution Approach 1:
The patent applies preliminary denoising actions before the standard SIM reconstruction process. By preprocessing the raw low-SNR images to remove noise and artifacts, the reconstruction algorithms can then operate on cleaner data, significantly reducing artifact amplification while maintaining super-resolution capabilities.
Solution Approach 2:
The patent introduces an intermediate processing step between image acquisition and final reconstruction. This intermediary denoising stage acts as a mediator that separates the harmful noise from the useful signal, allowing the reconstruction algorithms to work with purified input data and produce higher quality super-resolution images.
2Reliability
If multiple samplings are performed to improve image quality, then more training data can be obtained, but imaging time and phototoxicity increase
Solution Approach 1:
The patent employs self-supervised learning where the system uses its own acquired low-SNR images to train the denoising model without requiring additional high-SNR reference images or multiple samplings. The model learns to denoise by comparing reconstructed images against the original low-SNR inputs, enabling reliable training data generation from single acquisitions.
Solution Approach 2:
The patent creates synthetic training pairs by processing the same acquired images through different reconstruction pipelines. Instead of requiring multiple physical samplings, the system generates multiple training examples by copying and differently processing the original image data, maintaining reliability while avoiding additional imaging time and phototoxicity.
3Object-affected harmful factors
If low-intensity excitation is used for live cell imaging, then phototoxicity is reduced, but fluorescence signal strength decreases
Solution Approach 1:
The patent converts the harmful effect of low signal strength into a benefit by training the denoising model specifically on low-SNR images. The model learns to recognize and preserve weak fluorescence signals from live cells while removing noise, effectively turning the limitation of low-intensity excitation into an opportunity to develop specialized denoising capabilities that protect live cells from phototoxicity.
Solution Approach 2:
The patent changes the operational parameters of the imaging system by operating at low excitation intensities and then applying computational parameter enhancement through denoising. Instead of increasing physical illumination intensity, the system uses algorithmic parameter optimization to restore and enhance the weak fluorescence signals, maintaining low phototoxicity while achieving sufficient signal strength.
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
The method improves the quality of SIM images by reliably reconstructing denoised super-resolution images, expanding the technology's applicability and reducing experimental costs and damage to live cells.
Implementation Method 1
exciting a biological sample with structured illumination to obtain J raw fluorescence image sequences (Y) generated by the exciting of the biological sample
Data Source
AI summary
A self-supervised multimodal structured illumination microscopic reconstruction method comprises: exciting a biological sample with structured illumination to obtain J raw fluorescence image sequences, wherein J is an integer greater than or equal to 1, each raw fluorescence image sequence comprises S fluorescence images, S being an integer greater than or equal to 2; generating a training set for each raw fluorescence image sequence among the J raw fluorescence image sequences; training a denoising neural network on the basis of the training set; and performing super-resolution reconstruction on the S fluorescence images in each of the J raw fluorescence image sequences using a standard structured illumination super-resolution reconstruction algorithm to form a super-resolution image, which is input into the denoising neural network to obtain a final super-resolution reconstructed image.