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

ABSTRACT: A fundamental challenge in fluorescence microscopy is the photon shot noise arising from the inevitable stochasticity of photon detection. Noise increases measurement uncertainty and limits imaging resolution, speed and sensitivity. To achieve high-sensitivity fluorescence imaging beyond the shot-noise limit, we present DeepCAD-RT, a self-supervised deep learning method for real-time noise suppression. Based on our previous framework DeepCAD, we reduced the number of network parameters by 94%, memory consumption by 27-fold and processing time by a factor of 20, allowing real-time processing on a two-photon microscope. A high imaging signal-to-noise ratio can be acquired with tenfold fewer photons than in standard imaging approaches. We demonstrate the utility of DeepCAD-RT in a series of photon-limited experiments, including in vivo calcium imaging of mice, zebrafish larva and fruit flies, recording of three-dimensional (3D) migration of neutrophils after acute brain injury and imaging of 3D dynamics of cortical ATP release. DeepCAD-RT will facilitate the morphological and functional interrogation of biological dynamics with a minimal photon budget.

SUBMITTER: Li X

PROVIDER: S-EPMC9931589 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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

Li Xinyang X Li Yixin Y Zhou Yiliang Y Wu Jiamin J Zhao Zhifeng Z Fan Jiaqi J Deng Fei F Wu Zhaofa Z Xiao Guihua G He Jing J Zhang Yuanlong Y Zhang Guoxun G Hu Xiaowan X Chen Xingye X Zhang Yi Y Qiao Hui H Xie Hao H Li Yulong Y Wang Haoqian H Fang Lu L Dai Qionghai Q

Nature biotechnology 20220926 2

A fundamental challenge in fluorescence microscopy is the photon shot noise arising from the inevitable stochasticity of photon detection. Noise increases measurement uncertainty and limits imaging resolution, speed and sensitivity. To achieve high-sensitivity fluorescence imaging beyond the shot-noise limit, we present DeepCAD-RT, a self-supervised deep learning method for real-time noise suppression. Based on our previous framework DeepCAD, we reduced the number of network parameters by 94%, m ...[more]

PMID: 36163547

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Project description:BackgroundLow-dose X-ray images have become increasingly popular in the last decades, due to the need to guarantee the lowest reasonable patient's exposure. Dose reduction causes a substantial increase of quantum noise, which needs to be suitably suppressed. In particular, real-time denoising is required to support common interventional fluoroscopy procedures. The knowledge of noise statistics provides precious information that helps to improve denoising performances, thus making noise estimation a crucial task for effective denoising strategies. Noise statistics depend on different factors, but are mainly influenced by the X-ray tube settings, which may vary even within the same procedure. This complicates real-time denoising, because noise estimation should be repeated after any changes in tube settings, which would be hardly feasible in practice. This work investigates the feasibility of an a priori characterization of noise for a single fluoroscopic device, which would obviate the need for inferring noise statics prior to each new images acquisition. The noise estimation algorithm used in this study was tested in silico to assess its accuracy and reliability. Then, real sequences were acquired by imaging two different X-ray phantoms via a commercial fluoroscopic device at various X-ray tube settings. Finally, noise estimation was performed to assess the matching of noise statistics inferred from two different sequences, acquired independently in the same operating conditions.ResultsThe noise estimation algorithm proved capable of retrieving noise statistics, regardless of the particular imaged scene, also achieving good results even by using only 10 frames (mean percentage error lower than 2%). The tests performed on the real fluoroscopic sequences confirmed that the estimated noise statistics are independent of the particular informational content of the scene from which they have been inferred, as they turned out to be consistent in sequences of the two different phantoms acquired independently with the same X-ray tube settings.ConclusionsThe encouraging results suggest that an a priori characterization of noise for a single fluoroscopic device is feasible and could improve the actual implementation of real-time denoising strategies that take advantage of noise statistics to improve the trade-off between noise reduction and details preservation.

Dataset Information

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

Publications

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

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