3D k-space reflectance fluorescence tomography via deep learning.
3D k-space reflectance fluorescence tomography via deep learning.
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DOI：
10.1364/ol.450935
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发表时间：
2022-03-15
期刊：
影响因子：
3.6
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中科院分区：
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我们报告了使用深度学习（DL）在3D k空间反射荧光断层扫描（FT）中执行图像重建的潜力。在这里，我们采用了修改后的AUTOMAP架构，并开发了一种训练方法，利用开源的基于蒙特-卡罗的模拟器来生成一个大的数据集。使用增强的EMNIST（EEMNIST）数据集作为嵌入的对比度函数，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状态中的组织。提出的DL模型的训练和验证都在硅片数据和幻影实验。总的来说，我们的结果表明，该方法可以正确地重建单个和多个荧光嵌入（S）在3D体积。此外，所提出的技术优于传统的方法[最小二乘（LSQ）和总变差最小化（TVAL）]，特别是在较高的深度。因此，我们希望提出的计算技术在临床前研究中有未来的影响。
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.