Single pixel imaging frameworks facilitate the acquisition of high-dimensional optical data in biological applications with photon starved conditions. However, they are still limited to slow acquisition times and low pixel resolution. Herein, we propose a convolutional neural network for fluorescence lifetime imaging with compressed sensing at high compression (NetFLICS-CR), which enables in vivo applications at enhanced resolution, acquisition and processing speeds, without the need for experimental training datasets. NetFLICS-CR produces intensity and lifetime reconstructions at 128 × 128 pixel resolution over 16 spectral channels while using only up to 1% of the required measurements, therefore reducing acquisition times from ∼2.5 hours at 50% compression to ∼3 minutes at 99% compression. Its potential is demonstrated in silico, in vitro and for mice in vivo through the monitoring of receptor-ligand interactions in liver and bladder and further imaging of intracellular delivery of the clinical drug Trastuzumab to HER2-positive breast tumor xenografts. The data acquisition time and resolution improvement through NetFLICS-CR, facilitate the translation of single pixel macroscopic flurorescence lifetime imaging (SP-MFLI) for in vivo monitoring of lifetime properties and drug uptake.

中文翻译：

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基于高压缩深度学习的单像素高光谱宏观荧光寿命体内成像

单像素成像框架有助于在光子匮乏条件下的生物应用中获取高维光学数据。但是，它们仍然限于缓慢的采集时间和低像素分辨率。本文中，我们提出了一种卷积神经网络，用于在高压缩率下进行压缩传感的荧光寿命成像（NetFLICS-CR），它可以在体内以增强的分辨率，采集和处理速度进行应用，而无需实验训练数据集。NetFLICS-CR可在16个光谱通道上以128×128像素分辨率产生强度和寿命重建，同时仅使用所需测量的1％，因此将采集时间从50％压缩时的约2.5小时减少到99％压缩时的约3分钟。其潜力已通过计算机演示，通过监测肝和膀胱中受体-配体的相互作用以及进一步成像，将临床药物曲妥珠单抗向HER2阳性乳腺肿瘤异种移植物的细胞内递送进行体外和体内小鼠研究。通过NetFLICS-CR的数据采集时间和分辨率提高，促进了单像素宏观荧光寿命成像（SP-MFLI）的转换，可用于体内监测寿命特性和药物吸收。