@article{wang_sadeghi_velayati_paul_hetzler_danilov_ligler_wei_2023, title={Low-rate smartphone videoscopy for microsecond luminescence lifetime imaging with machine learning}, volume={2}, ISSN={["2752-6542"]}, url={https://doi.org/10.1093/pnasnexus/pgad313}, DOI={10.1093/pnasnexus/pgad313}, abstractNote={Abstract
               Time-resolved techniques have been widely used in time-gated and luminescence lifetime imaging. However, traditional time-resolved systems require expensive lab equipment such as high-speed excitation sources and detectors or complicated mechanical choppers to achieve high repetition rates. Here, we present a cost-effective and miniaturized smartphone lifetime imaging system integrated with a pulsed ultraviolet (UV) light-emitting diode (LED) for 2D luminescence lifetime imaging using a videoscopy-based virtual chopper (V-chopper) mechanism combined with machine learning. The V-chopper method generates a series of time-delayed images between excitation pulses and smartphone gating so that the luminescence lifetime can be measured at each pixel using a relatively low acquisition frame rate (e.g. 30 frames per second [fps]) without the need for excitation synchronization. Europium (Eu) complex dyes with different luminescent lifetimes ranging from microseconds to seconds were used to demonstrate and evaluate the principle of V-chopper on a 3D-printed smartphone microscopy platform. A convolutional neural network (CNN) model was developed to automatically distinguish the gated images in different decay cycles with an accuracy of >99.5%. The current smartphone V-chopper system can detect lifetime down to ∼75 µs utilizing the default phase shift between the smartphone video rate and excitation pulses and in principle can detect much shorter lifetimes by accurately programming the time delay. This V-chopper methodology has eliminated the need for the expensive and complicated instruments used in traditional time-resolved detection and can greatly expand the applications of time-resolved lifetime technologies.}, number={10}, journal={PNAS NEXUS}, author={Wang, Yan and Sadeghi, Sina and Velayati, Alireza and Paul, Rajesh and Hetzler, Zach and Danilov, Evgeny and Ligler, Frances S. and Wei, Qingshan}, editor={Reis, RuiEditor}, year={2023}, month={Sep} }