simultaneous-label-free-autofluorescence-multi-harmonic-microscopy

Simultaneous Label-Free Autofluorescence Multi-Harmonic Microscopy

Nonlinear optical microscopy images biological samples by detecting signals from the nonlinear interaction of ultrashort laser pulses with endogenous ...

University of Illinois Urbana-Champaign

The dynamic range and fluctuations of fluorescence intensities and lifetimes in biological samples are large, demanding fast, precise, and versatile techniques. Among the high-speed fluorescence lifetime imaging microscopy (FLIM) techniques, directly sampling the output of analog single-photon detectors at GHz rates combined with computational photon counting can handle a larger range of photon rates. Traditionally, the laser clock is not sampled explicitly in fast FLIM; rather the detection is synchronized to the laser clock so that the excitation pulse train can be inferred from the cumulative photon statistics of several pixels. This has two disadvantages for sparse or weakly fluorescent samples: inconsistencies in inferring the laser clock within a frame and inaccuracies in aligning the decay curves from different frames for averaging. The data throughput is also very inefficient in systems with repetition rates much larger than the fluorescence lifetime due to significant silent regions where no photons are expected. We present a method for registering the photon arrival times to the excitation using time-domain multiplexing for fast FLIM. The laser clock is multiplexed with photocurrents into the silent region. Our technique does not add to the existing data bottleneck, has the sub-nanosecond dead time of computational photon counting based fast FLIM, works with various detectors, lasers, and electronics, and eliminates the errors in lifetime estimation in photon-starved conditions. We demonstrate this concept on two multiphoton setups of different laser repetition rates for single and multichannel FLIM multiplexed into a single digitizer channel for real-time imaging of biological samples.

Kevin K. D. Tan

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