Pixel Super-Resolved Fluorescence Lifetime Imaging Using Deep Learning

TL;DR

This paper introduces FLIM_PSR_k, a deep learning framework that enhances the spatial resolution of fluorescence lifetime imaging microscopy (FLIM), enabling faster, higher-resolution, and more practical imaging for clinical and translational applications.

Contribution

The paper presents a novel cGAN-based super-resolution method for FLIM that improves resolution and speed, outperforming diffusion models in inference time and robustness.

Findings

Achieved a super-resolution factor of 5 in FLIM images.

Increased the space-bandwidth product by 25 times.

Demonstrated reliable super-resolution on tumor tissue samples.

Abstract

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 stricter resolution-speed trade-off than conventional optical imaging approaches. Here, we introduce FLIM_PSR_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 5-fold 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. FLIM_PSR_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 FLIM_PSR_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, FLIM_PSR_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.