Opt Lett. 2022 Mar 15;47(6):1533-1536. doi: 10.1364/OL.450935.
We report on the potential to perform image reconstruction in 3D k-space reflectance fluorescence tomography (FT) using deep learning (DL). Herein, we adopt a modified AUTOMAP architecture and develop a training methodology that leverages an open-source Monte-Carlo-based simulator to generate a large dataset. Using an enhanced EMNIST (EEMNIST) dataset as an embedded contrast function allows us to train the network efficiently. The optical strategy utilizes k-space illumination in a reflectance configuration to probe tissue in the mesoscopic regime with high sensitivity and resolution. The proposed DL model training and validation is performed with both in silico data and a phantom experiment. Overall, our results indicate that the approach can correctly reconstruct both single and multiple fluorescent embedding(s) in a 3D volume. Furthermore, the presented technique is shown to outperform the traditional approaches [least-squares (LSQ) and total-variation minimization (TVAL)], especially at higher depths. We, therefore, expect the proposed computational technique to have future implications in preclinical studies.
我们报告了在使用深度学习（DL）的 3D k 空间反射荧光层析成像（FT）中进行图像重建的潜力。在这里，我们采用了改进的 AUTOMAP 架构，并开发了一种利用开源基于蒙特卡罗的模拟器生成大数据集的训练方法。使用增强的 EMNIST（EEMNIST）数据集作为嵌入式对比函数，使我们能够有效地训练网络。光学策略利用反射配置中的 k 空间照明，以高灵敏度和分辨率探测介观组织中的荧光团。所提出的 DL 模型的训练和验证是在仿真数据和体模实验上进行的。总的来说，我们的结果表明，该方法可以正确地重建 3D 体积中的单个和多个荧光嵌入物。此外，所提出的技术被证明优于传统方法（最小二乘（LSQ）和全变差最小化（TVAL）），尤其是在更深的位置。因此，我们期望所提出的计算技术在临床前研究中具有未来的意义。
