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.
Abstract (translated)
荧光寿命成像显微镜（FLIM）是一种强大的定量技术，可提供代谢和分子对比度，并且具有重要的转化潜力，可用于无标签实时诊断。然而，由于像素停留时间长以及信噪比低，其临床应用受到了限制，这些因素对分辨率与速度的权衡要求比传统光学成像方法更为严格。在这里，我们引入了FLIM_PSR_k，这是一种基于深度学习的多通道像素超分辨率（PSR）框架，可以从使用高达5倍像素尺寸采集的数据中重建高分辨率的FLIM图像。该模型采用条件生成对抗网络（cGAN）架构进行训练，相比于基于扩散模型的方法，在提供更稳健的PSR重建的同时，还具有显著缩短推理时间的优势，这对于实际部署至关重要。 FLIM_PSR_k不仅加快了成像速度，还可以缓解自体荧光基FLIM中的信噪比限制。在未公开的患者来源肿瘤组织样本上的盲测结果表明，FLIM_PSR_k可靠地实现了k=5倍超分辨率，从而将输出图像的空间带宽产品提高了25倍，并揭示了低分辨率输入中丢失的精细结构特征，在各种图像质量指标上均有统计学意义上的显著改善。通过提高FLIM的有效空间分辨率，FLIM_PSR_k推进了寿命成像技术朝向更快、更高分辨率以及硬件灵活兼容的方向发展，尤其适用于低数值孔径和微型化平台，并且更好地将FLIM定位为转化应用的理想选择。
URL
https://arxiv.org/abs/2512.16266
