Multiplexing and Sensing with Fluorescence Lifetime Imaging Microscopy Empowered by Phasor U-Net

Fluorescence lifetime imaging microscopy (FLIM) has been widely used as an essential multiplexing and sensing tool in frontier fields such as materials science and life sciences. However, the accuracy of lifetime estimation is compromised by limited time-correlated photon counts, and data processing is time-demanding due to the large data volume. Here, we introduce Phasor U-Net, a deep learning method designed for rapid and accurate FLIM imaging. Phasor U-Net incorporates two lightweight U-Net subnetworks to perform denoising and deconvolution to reduce the noise and calibrate the data caused by the instrumental response function, thus facilitating the downstream phasor analysis. Phasor U-Net is solely trained on computer-generated datasets, circumventing the necessity for large experimental datasets. The method reduced the modified Kullback-Leibler divergence on the phasor plots by 1.5-8-fold compared with the direct phasor method and reduced the mean absolute error of the lifetime images by 1.18-4.41-fold. We then show that this method can be used for multiplexed imaging on the small intestine samples of mice labeled by two fluorescence dyes with almost identical emission spectra. We further demonstrate that the size of quantum dots can be better estimated with measured lifetime information. This general method will open a new paradigm for more fundamental research with FLIM.