Key Laboratory of Ultra-fast Photoelectric Diagnostics Technology, Xi'an Institute of Optics and Precision Mechanics (XIOPM), Chinese Academy of Sciences (CAS), Xi'an, Shaanxi, China.
University of Chinese Academy of Sciences, Beijing, China.
PLoS One. 2022 Aug 12;17(8):e0271441. doi: 10.1371/journal.pone.0271441. eCollection 2022.
Compressed fluorescence lifetime imaging (Compressed-FLIM) is a novel Snapshot compressive imaging (SCI) method for single-shot widefield FLIM. This approach has the advantages of high temporal resolution and deep frame sequences, allowing for the analysis of FLIM signals that follow complex decay models. However, the precision of Compressed-FLIM is limited by reconstruction algorithms. To improve the reconstruction accuracy of Compressed-FLIM in dealing with large-scale FLIM problem, we developed a more effective combined prior model 3DTGp V_net, based on the Plug and Play (PnP) framework. Extensive numerical simulations indicate the proposed method eliminates reconstruction artifacts caused by the Deep denoiser networks. Moreover, it improves the reconstructed accuracy by around 4dB (peak signal-to-noise ratio; PSNR) over the state-of-the-art TV+FFDNet in test data sets. We conducted the single-shot FLIM experiment with different Rhodamine reagents and the results show that in practice, the proposed algorithm has promising reconstruction performance and more negligible lifetime bias.
压缩荧光寿命成像（Compressed-FLIM）是一种新颖的单镜头宽场 FLIM 快照压缩成像（SCI）方法。该方法具有高时间分辨率和深帧序列的优点，可分析遵循复杂衰减模型的 FLIM 信号。然而，Compressed-FLIM 的精度受重建算法的限制。为了提高 Compressed-FLIM 在处理大规模 FLIM 问题时的重建精度，我们基于 Plug and Play（PnP）框架，开发了一种更有效的联合先验模型 3DTGp V_net。大量数值模拟表明，该方法消除了 Deep denoiser 网络引起的重建伪影。此外，与最先进的 TV+FFDNet 相比，在测试数据集上，重建精度提高了约 4dB（峰值信噪比；PSNR）。我们进行了不同 Rhodamine 试剂的单镜头 FLIM 实验，结果表明，在实际应用中，该算法具有有前途的重建性能和更小的寿命偏差。
