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.
A fundamental challenge in fluorescence microscopy is the inherent photon shot noise caused by the inevitable stochasticity of photon detection. Noise increases measurement uncertainty, degrades image quality, and limits imaging resolution, speed, and sensitivity. To achieve high-sensitivity imaging beyond the shot-noise limit, we provide DeepCAD-RT, a versatile self-supervised method for effective noise suppression of fluorescence time-lapse imaging. We made comprehensive optimizations to reduce its data dependency, processing time, and memory consumption, finally allowing real-time processing on a two-photon microscope. High imaging signal-to-noise ratio (SNR) can be acquired with 10-fold fewer fluorescence photons. Meanwhile, the self-supervised superiority makes it a practical tool in fluorescence microscopy where ground-truth images for training are hard to obtain. We demonstrated the utility of DeepCAD-RT in extensive experiments, including in vivo calcium imaging of various model organisms (mouse, zebrafish larva, fruit fly), 3D migration of neutrophils after acute brain injury, and 3D dynamics of cortical ATP (adenosine 5'-triphosphate) release. DeepCAD-RT will facilitate the morphological and functional interrogation of biological dynamics with minimal photon budget.
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