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通过融合深度学习与超弱光显微镜实现近零光子生物成像。

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

作者信息

Sheneman Lucas, Balogun Sulaimon, Johnson Jill L, Harrison Maria J, Vasdekis Andreas E

机构信息

Institute for Interdisciplinary Data Sciences, University of Idaho, Moscow, ID 83844-3051.

Department of Physics, University of Idaho, Moscow, ID 83844-0903.

出版信息

Proc Natl Acad Sci U S A. 2025 May 27;122(21):e2412261122. doi: 10.1073/pnas.2412261122. Epub 2025 May 19.


DOI:10.1073/pnas.2412261122
PMID:40388622
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12130841/
Abstract

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, including features represented by nearly zero photons. Beyond optical microscopy, the near-zero photon bioimaging paradigm can be applied in remote sensing, covert applications, and biomedical imaging that utilize damaging or quantum light.

摘要

通过降低样本辐照度来提高光学显微镜的可靠性和可重复性仍然是一个重要的生物技术目标。然而,随着辐照度的降低,光的粒子特性增强,会产生泊松噪声或光子稀疏性,这使得只有少数(0.5%)图像像素包含光子。光子稀疏性可以通过每个像素收集大约200个光子来解决;然而,这需要长时间采集,因此成像速率不理想。在这里,我们引入了近零光子生物成像技术,这是一种以千赫兹速率运行且辐照度比标准显微镜低10000倍的方法。为了达到这种性能水平,我们独特地将精心设计的能够实现超低背景水平的落射荧光显微镜与人工智能相结合,该人工智能能够从低至每像素0.01个光子的情况下学习重建生物图像。我们证明,近零光子生物成像能够以高保真度捕捉多细胞和亚细胞特征的结构,包括几乎由零光子表示的特征。除了光学显微镜,近零光子生物成像范式还可以应用于遥感、隐蔽应用以及利用有害或量子光的生物医学成像。

相似文献

[1]
Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy.

Proc Natl Acad Sci U S A. 2025-5-27

[2]
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本文引用的文献

[1]
Noise-robust latent vector reconstruction in ptychography using deep generative models.

Opt Express. 2024-1-1

[2]
CASPI: collaborative photon processing for active single-photon imaging.

Nat Commun. 2023-5-31

[3]
Video-rate Raman-based metabolic imaging by Airy light-sheet illumination and photon-sparse detection.

Proc Natl Acad Sci U S A. 2023-2-28

[4]
Real-time denoising enables high-sensitivity fluorescence time-lapse imaging beyond the shot-noise limit.

Nat Biotechnol. 2023-2

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Density fluctuations, homeostasis, and reproduction effects in bacteria.

Commun Biol. 2022-4-28

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J Healthc Eng. 2022

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Nat Methods. 2021-10

[8]
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J Big Data. 2021

[9]
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Nature. 2021-6

[10]
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Nat Commun. 2020-12-7

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