Super-resolution Microscopy via Iterative Deconvolution
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Solution Overview
Problem
Existing fluorescence super-resolution methods have low temporal resolution, requiring numerous consecutive image collections to achieve high-quality super-resolution, which hinders their application in live cell imaging.
Innovation Solution
A super-resolution microscopic imaging method that collects a fluorescent signal sequence, determines the number of deconvolution iterations, performs pre-deconvolution and multiple reconstructions, and iteratively enhances the image to achieve high throughput and resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional fluorescence super-resolution methods are used, then high-quality super-resolution effect is achieved, but temporal resolution deteriorates (requiring 500-1000 consecutive image collections)
Solution Approach 1:
The patent performs preliminary deconvolution processing on individual frames before final reconstruction, pre-enhancing the signal quality to reduce the number of frames needed for achieving super-resolution. This preliminary action on each frame allows the method to achieve high spatial resolution with fewer temporal samples.
Solution Approach 2:
The patent replaces the conventional mechanical approach of collecting numerous sequential frames with a computational approach using deconvolution algorithms. Instead of relying on temporal averaging of many frames, the method uses mathematical processing to extract super-resolution information from fewer frames, substituting computational processing for temporal accumulation.
2Manufacturing precision
If multiple consecutive images are collected to achieve super-resolution, then manufacturing precision is improved, but productivity deteriorates due to the large number of image collections required
Solution Approach 1:
The patent substitutes computational deconvolution processing for the mechanical process of collecting numerous sequential images. By applying iterative deconvolution algorithms to individual or fewer frames, the method achieves super-resolution quality without the time-consuming process of accumulating 500-1000 frames, thereby improving imaging throughput.
Solution Approach 2:
The method performs preliminary deconvolution enhancement on individual frames before final reconstruction, pre-processing the data to extract maximum resolution information from each frame. This allows the system to achieve high-quality super-resolution with fewer total frames collected, improving productivity.
3Measurement precision
If conventional super-resolution methods are used, then spatial resolution is improved, but the method complexity increases due to requiring numerous image collections and processing steps
Solution Approach 1:
The patent performs preliminary deconvolution on individual frames before final reconstruction, simplifying the overall processing pipeline. By pre-enhancing each frame independently, the method reduces the complexity of processing large numbers of sequential frames while maintaining or improving spatial resolution.
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 achieves a two-fold improvement in three-dimensional spatial resolution and a 50 to 100 times improvement in temporal resolution, enabling high-quality super-resolution imaging with fewer frames while maintaining flexibility across various imaging modalities.
Implementation Method 1
estimating a background of the fluorescent signal using a wavelet transformation and removing background noise
Implementation Method 2
collecting a fluorescent signal sequence of a set of samples to be observed; uses the physical model of the random fluctuation of intensity of fluorescent molecules
Data Source
AI summary
Disclosed are a super-resolution microscopic imaging method and apparatus, a computer device, and a storage medium. The method includes the following steps: collecting a fluorescent signal sequence of a set of samples to be observed; determining the number of iterations of deconvolution; performing an iteration of pre-deconvolution on each frame of an initial image in the fluorescent signal sequence before outputting when the iteration reaches half of the number of iterations; performing two reconstructions on the output image; and performing a second iteration of deconvolution on the image after the two reconstructions before outputting when the iteration reaches the number of iterations or half of the number of iterations. The present disclosure, with advantages of non-parameterization, high throughput, and high resolution, can overcome the shortcomings and deficiencies of the prior art, and can be widely coupled to various imaging modalities, such as acoustic microscopy, that is, photoacoustic and ultrasound microscopic imaging technology.