{"ID":2827430,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.16266","arxiv_id":"2512.16266","title":"Pixel Super-Resolved Fluorescence Lifetime Imaging Using Deep Learning","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.","short_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)...","url_abs":"https://arxiv.org/abs/2512.16266","url_pdf":"https://arxiv.org/pdf/2512.16266v1","authors":"[\"Paloma Casteleiro Costa\",\"Parnian Ghapandar Kashani\",\"Xuhui Liu\",\"Alexander Chen\",\"Ary Portes\",\"Julien Bec\",\"Laura Marcu\",\"Aydogan Ozcan\"]","published":"2025-12-18T07:28:10Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\",\"physics.med-ph\",\"physics.optics\"]","methods":"[\"Diffusion Model\",\"Generative Adversarial Network\"]","has_code":false}