Light: Science & Applications (Dec 2023)

Quantitatively mapping local quality of super-resolution microscopy by rolling Fourier ring correlation

  • Weisong Zhao,
  • Xiaoshuai Huang,
  • Jianyu Yang,
  • Liying Qu,
  • Guohua Qiu,
  • Yue Zhao,
  • Xinwei Wang,
  • Deer Su,
  • Xumin Ding,
  • Heng Mao,
  • Yaming Jiu,
  • Ying Hu,
  • Jiubin Tan,
  • Shiqun Zhao,
  • Leiting Pan,
  • Liangyi Chen,
  • Haoyu Li

DOI
https://doi.org/10.1038/s41377-023-01321-0
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 19

Abstract

Read online

Abstract In fluorescence microscopy, computational algorithms have been developed to suppress noise, enhance contrast, and even enable super-resolution (SR). However, the local quality of the images may vary on multiple scales, and these differences can lead to misconceptions. Current mapping methods fail to finely estimate the local quality, challenging to associate the SR scale content. Here, we develop a rolling Fourier ring correlation (rFRC) method to evaluate the reconstruction uncertainties down to SR scale. To visually pinpoint regions with low reliability, a filtered rFRC is combined with a modified resolution-scaled error map (RSM), offering a comprehensive and concise map for further examination. We demonstrate their performances on various SR imaging modalities, and the resulting quantitative maps enable better SR images integrated from different reconstructions. Overall, we expect that our framework can become a routinely used tool for biologists in assessing their image datasets in general and inspire further advances in the rapidly developing field of computational imaging.