Origin and compensation of imaging artefacts in localization-based super-resolution microscopy

Area of Science:

• Microscopy and Imaging Science
• Biophysics
• Image Analysis

Background:

• Localization-based microscopy generates high-resolution images crucial for biological research.
• Image interpretation is hindered by artifacts originating from the optical system, sample, or computational algorithms.
• Quantifying and mitigating these artifacts is essential for accurate data analysis.

Purpose of the Study:

• To address the challenges in interpreting high-resolution images from localization-based microscopy.
• To categorize imaging artifacts based on their origin (optical, sample, algorithmic).
• To evaluate methods for artifact elimination and reduction.

Main Methods:

• Categorization of imaging artifacts by their source.
• Application of precise calibration procedures for artifact reduction.
• Utilizing pattern-specific metrics and fluorescence correlation spectroscopy for image qualification.

Main Results:

• Artifacts introduced by optical systems, samples, and algorithms were identified and categorized.
• Certain artifacts can be eliminated through calibration, while others are only reducible.
• Both theoretical and experimental image analyses were performed using defined metrics.

Conclusions:

• Understanding artifact origins is key to improving image quality in localization-based microscopy.
• Calibration and advanced spectroscopic metrics are vital tools for artifact management.
• Accurate image qualification is necessary for reliable interpretation of biological structures at high resolution.