Multiplexed structured illumination super-resolution imaging with time-domain upconversion nanoparticles
Baolei Liu, Jiayan Liao, Yiliao Song, Jie Lu, Jiajia Zhou, Fan Wang

TL;DR
This paper introduces a super-resolution imaging method using lifetime-engineered upconversion nanoparticles combined with deep learning, achieving high-resolution multiplexed imaging with over 93% decoding accuracy, surpassing diffraction limits.
Contribution
The study presents a novel time-domain super-resolution technique with engineered nanoparticles and deep learning, significantly enhancing multiplexing capacity and decoding accuracy in optical imaging.
Findings
Achieved 186 nm lateral resolution, less than 1/4th of the excitation wavelength.
Demonstrated over 93% decoding accuracy in three-channel multiplexing.
Enabled potential seven-channel multiplexing with over 60% accuracy.
Abstract
The emerging optical multiplexing within nanoscale shows super-capacity in encoding information by using the time-domain fingerprints from uniform nanoparticles. However, the optical diffraction limit compromises the decoding throughput and accuracy of the nanoparticles during wide-field imaging. This, in turn, challenges the quality of nanoparticles to afford the modulated excitation condition, and further to retain the multiplexed optical fingerprints for super-resolution multiplexing. Here we report a tailor-made time-domain super-resolution method with the lifetime-engineered upconversion nanoparticles for multiplexing. We demonstrate that the nanoparticles are bright, uniform, and stable under structured illumination, which supports a lateral resolution of 186 nm, less than 1/4th of the excitation wavelength. We further develop a deep learning algorithm to coordinate with…
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Taxonomy
TopicsAdvanced Fluorescence Microscopy Techniques · Photoacoustic and Ultrasonic Imaging · Random lasers and scattering media
