Localization Microscopy Pixel Calibration for Noise Correction
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Solution Overview
Problem
Localization microscopy methods face challenges in accurately localizing signal sources due to inhomogeneities in signal behavior of pixels, leading to incorrect signal localization, especially in detectors like sCMOS sensors, where pixel-dependent noise is not adequately modeled by existing parameterized models.
Innovation Solution
A method that calibrates each pixel by ascertaining pixel-specific error parameters, storing them in a calibration data record, and fitting a point spread function (PSF) to image data, where pixels with error parameters exceeding a threshold are either ignored or interpolated, allowing for improved noise modeling and localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If parameterized models are used to model pixel noise in localization microscopy, then the complexity of noise modeling is reduced, but the measurement precision of signal source localization deteriorates due to inadequate modeling of pixel-dependent noise
Solution Approach 1:
The patent applies local quality by transitioning from uniform parameterized noise modeling to pixel-specific noise characterization. Each pixel is individually calibrated to determine its unique noise parameters (offset, gain, readout noise), creating a spatially varying noise model that adapts to local pixel characteristics. This resolves the contradiction by increasing modeling detail at the pixel level while maintaining overall system manageability through automated calibration procedures.
Solution Approach 2:
The patent implements preliminary action by performing pixel calibration before actual localization measurements. The calibration process pre-determines noise parameters for each pixel, which are then stored and applied during subsequent imaging and localization steps. This preliminary characterization eliminates the need for complex real-time noise modeling during localization, thereby improving precision without excessive computational complexity.
2Productivity
If all pixels are used for signal source localization, then the productivity of localization is maximized, but the measurement precision deteriorates due to inclusion of pixels with high error parameters
Solution Approach 1:
The patent applies the taking out principle by identifying and excluding pixels with error parameters exceeding predetermined thresholds from the localization process. The calibration data record stores error parameters for each pixel, and pixels with unacceptably high noise characteristics are extracted and removed from subsequent analysis. This ensures that only high-quality pixels contribute to localization, maintaining precision while preserving productivity by utilizing all acceptable pixels.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting the effective pixel set based on calibrated error parameters. Instead of using a fixed pixel array, the system modifies which pixels are active in localization by applying threshold criteria to noise parameters. This selective parameter adjustment optimizes the balance between utilizing sufficient pixels for productivity and excluding noisy pixels for precision.
3Measurement precision
If pixel-specific calibration is performed for each pixel, then the measurement precision of localization is improved, but the loss of time for calibration and data processing increases
Solution Approach 1:
The patent applies preliminary action by performing pixel calibration once in advance before actual localization experiments. The calibration data record is created and stored, containing pre-computed error parameters for each pixel. During subsequent localization measurements, this pre-calibrated data is directly applied without requiring repeated calibration procedures, thereby minimizing time loss while maintaining high localization precision.
Solution Approach 2:
The patent implements copying by creating a digital calibration data record that replicates pixel characteristics and noise parameters. This calibration copy is stored and reused across multiple imaging sessions and localization experiments, eliminating the need to repeat time-consuming calibration measurements. The copied calibration data enables rapid processing while preserving the precision benefits of pixel-specific characterization.
Data Source
AI summary
The invention relates to a localization microscopy method for localizing signal sources. Here, at least once for each pixel of a detector, values of an error parameter are ascertained and stored in a calibration data record in a manner assigned to the relevant pixel. Captured image data are used to identify regions of origin of signal sources and fit a point spread function to the pixel values of the respective regions of origin. The respective signal source is localized on the basis of the point spread function. The pixel-specific error parameter of each pixel can be compared to a threshold. If the threshold is exceeded, these pixels are either ignored or replaced by means of interpolation when fitting the point spread function. In addition or as an alternative thereto, the real noise performance of the pixels is ascertained and corrected on the basis of derived pixel-specific error parameters.