  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 UV 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 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 microseconds
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.
