.png){kind=icon}
.png){kind=icon}
Return
Pixel super-resolved fluorescence lifetime imaging using deep neural networks
.png){kind=icon}
OA
AI
DOI：10.1186/s43074-026-00277-9.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Fluorescence lifetime imaging microscopy (FLIM) is a powerful quantitative technique that provides metabolic and molecular contrast, offering strong translational potential for label-free, real-time diagnostics. However, its clinical adoption remains limited by long pixel dwell times and low signal-to-noise ratio (SNR), which impose a more severe resolution-speed trade-off than conventional optical imaging approaches. Here, we introduce FLIMPSR_k, a deep learning-based multi-channel pixel super-resolution (PSR) framework that reconstructs high-resolution FLIM images from data acquired with up to a fivefold increased pixel size. The model is trained using the conditional generative adversarial network (cGAN) framework, which, compared to diffusion model-based alternatives, delivers a more robust PSR reconstruction with substantially shorter inference times, a crucial advantage for practical deployment. FLIMPSR_k not only enables faster image acquisition but can also alleviate SNR limitations in autofluorescence-based FLIM. Blind testing on held-out patient-derived tumor tissue samples demonstrates that FLIMPSR_k reliably achieves a super-resolution factor of k = 5, resulting in a 25-fold increase in the space-bandwidth product of the output images and revealing fine architectural features lost in lower-resolution inputs, with statistically significant improvements across various image quality metrics. By increasing FLIM’s effective spatial resolution, FLIMPSR_k advances lifetime imaging toward faster, higher-resolution, and hardware-flexible implementations compatible with low-numerical-aperture and miniaturized platforms, better positioning FLIM for translational applications.
Keywords:
Fluorescence lifetime imaging microscopy (FLIM)
Pixel super-resolution
Deep learning
Computational microscopy
Conditional generative adversarial networks (cGAN)
Journal
IF:
19.1
Papers:
273
Citations:
2.3K