Abstract
Conventional second near-infrared (NIR-II; 1,000 to 1,700 nm) fluorescence imaging cannot simultaneously achieve a high signal-to-noise ratio and motion-artifact-free capture of rapid physiological dynamics. Here, we introduce NIR-II compressive fluorescence imaging (COFI), a high-speed, pixel-super-resolved compressive imaging technique that encodes dynamics into single frames using a high-speed spatial light modulator and a low-frame-rate NIR-II camera. A hybrid reconstruction algorithm integrating a denoising convolutional neural network with an enhanced super-resolution generative adversarial network subsequently restores high-fidelity videos. The system achieves 3.3 kiloframes per second with a space–bandwidth–time product of 4.22 × 108 pixels/s without compromising intrinsic sensitivity. Compared to conventional short-exposure imaging with the same duration of 500 μs, NIR-II COFI achieves a 36% improvement in signal-to-noise ratio. Furthermore, using bright 1,525-nm nanoparticle probes, we demonstrate multicomponent phosphorescence lifetime imaging, high-speed motion tracking, and real-time visualization of murine intestinal peristalsis in both awake and anesthetized states. This work facilitates deep-tissue, high-speed in vivo imaging of fast biological processes.
| Original language | English |
|---|---|
| Article number | 1146 |
| Journal | Research |
| Volume | 9 |
| DOIs | |
| State | Published - Jan 2026 |
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