Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy

• 成果类型: Article
• 署名作者: Sheneman, Lucas; Balogun, Sulaimon; Johnson, Jill L.; Harrison, Maria J.; Vasdekis, Andreas E.
• 署名单位: University of Idaho; University of Idaho; University of Idaho; Cornell University; Boyce Thompson Institute for Plant Research; United States Department of Energy (DOE); Pacific Northwest National Laboratory
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-9883
• DOI: 10.1073/pnas.2412261122
• 发表日期: 2025-05-27
• 关键词: sheet fluorescence microscopy; single-molecule

摘要

Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise to Poisson noise, or photon sparsity that restricts only a few (0.5%) image pixels to comprise a photon. Photon sparsity can be addressed by collecting approximately 200 photons per pixel; this, however, requires long acquisitions and, as such, suboptimal imaging rates. Here, we introduce near- zero photon bioimaging, a method that operates at kHz rates and 10,000- fold lower irradiance than standard microscopy. To achieve this level of performance, we uniquely combined a judiciously designed epifluorescence microscope enabling ultralow background levels and AI that learns to reconstruct biological images from as low as 0.01 photons per pixel. We demonstrate that near- zero photon bioimaging captures the structure of multicellular and subcellular features with high fidelity, near- zero photon bioimaging paradigm can be applied in remote sensing, covert applications, and biomedical imaging that utilize damaging or quantum light.