Microscopy Decay Reconstruction Using Spread-Function Model Fitting
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Solution Overview
Problem
Existing microscopy methods struggle to accurately reconstruct location-related decay behavior of sample distributions due to the smearing effect of the optical spread function, leading to reduced signal-to-noise ratio and loss of spatial resolution, especially in data with low signal-to-noise ratio and coarse spatial sampling.
Innovation Solution
A computer-implemented method that fits a model function to sample distribution data, incorporating both location-dependent and time-dependent terms, to reconstruct the location-related decay behavior by convolving with a spatial spread function, reducing noise and improving reconstruction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-by-pixel curve-fitting is used to determine model parameters, then spatial sampling is maintained, but signal-to-noise ratio decreases leading to poor reconstruction quality
Solution Approach 1:
The patent combines multiple pixels into superpixels to aggregate signal strength while preserving spatial information. This merging approach increases the signal-to-noise ratio by pooling photons across adjacent pixels, enabling reliable parameter estimation in low-light conditions without completely sacrificing spatial resolution.
Solution Approach 2:
The patent applies different processing strategies to different spatial regions by adapting the superpixel size and merging level based on local signal conditions. Regions with sufficient signal maintain finer spatial sampling, while low-signal regions undergo greater merging to achieve acceptable noise levels, optimizing the balance between resolution and reliability locally.
2Reliability
If spatial binning is applied to improve signal-to-noise ratio, then noise is reduced, but spatial resolution is lost
Solution Approach 1:
The patent implements dynamic spatial binning where the binning level is not fixed but adapts based on local signal conditions and reconstruction needs. The superpixel formation and merging process is optimized iteratively to achieve the minimum necessary binning for acceptable noise levels, preserving spatial resolution wherever possible.
Solution Approach 2:
The patent transitions from traditional 2D spatial binning to a hierarchical approach that operates across multiple spatial scales. By organizing pixels into superpixels and then into larger regions, the method effectively adds a hierarchical dimension to spatial processing, allowing signal aggregation without complete loss of fine spatial details.
3Ease of manufacture
If model parameters are estimated independently at each pixel, then computational simplicity is maintained, but reconstruction accuracy deteriorates due to noise influence
Solution Approach 1:
The patent combines parameter estimation across multiple pixels by fitting the model to aggregated superpixel data rather than independent pixel data. This approach maintains computational efficiency compared to full joint optimization while significantly improving parameter estimation accuracy by reducing noise through signal aggregation.
Solution Approach 2:
The patent applies a middle-ground approach between complete independence and full joint optimization. By performing parameter estimation on superpixels (partial merging) rather than individual pixels, the method achieves sufficient accuracy improvement without the computational burden of complete joint optimization across all pixels.
Data Source
AI summary
The invention relates to a method, an imaging system and a computer program for improved reconstruction of location-related decay behavior of microscopic sample distributions. For this purpose, a model function is proposed that models an image formation and is suitable for reconstructing a location-related decay behavior by fitting the model function to the captured data.