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Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation
Weisong Zhao1,2, Xiaoshuai Huang3, Jianyu Yang4
1Innovation Photonics and Imaging Center, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, China.
Light, Science & Applications
|December 14, 2023
Summary
This study introduces a new rolling Fourier ring correlation (rFRC) method to assess image quality in super-resolution microscopy. The method helps biologists evaluate reconstruction uncertainties and improve image analysis.
Area of Science:
- Microscopy
- Computational Imaging
- Image Analysis
Background:
- Computational algorithms enhance fluorescence microscopy images, but local quality variations can mislead interpretation.
- Existing methods struggle to accurately map local image quality at super-resolution scales.
Purpose of the Study:
- To develop a novel method for evaluating reconstruction uncertainties in super-resolution (SR) microscopy down to the SR scale.
- To create a comprehensive map for identifying regions with low image reliability.
Main Methods:
- Development of a rolling Fourier ring correlation (rFRC) method.
- Combination of filtered rFRC with a modified resolution-scaled error map (RSM) for visual assessment.
- Demonstration on various SR imaging modalities.
Main Results:
- The rFRC method accurately evaluates reconstruction uncertainties at SR scales.
- The combined rFRC and RSM provide a concise map of image reliability.
- Quantitative maps facilitate better integration of SR images from different reconstructions.
Conclusions:
- The developed framework offers a reliable tool for biologists to assess super-resolution image datasets.
- This approach is expected to advance the field of computational imaging by improving image quality assessment.
