物理模型模拟器训练的神经网络用于多次散射样品的计算三维相位成像
Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples
摘要：
恢复复杂的、多次散射的生物样本的3D相位特征传统上牺牲了计算效率和处理时间以获得物理模型精度和重建质量。这种权衡阻碍了对生物研究最感兴趣的活的、动态的生物样品的快速分析。在这里，我们通过将环形强度衍射断层扫描（aIDT）与近似引导的深度学习框架相结合来克服这一瓶颈。使用一种全新的基于物理模型模拟器的学习策略，完全在自然图像数据集上训练，我们证明了我们的网络可以鲁棒地重建任意大小和结构的复杂3D生物样本。这种方法强调了可以利用大规模多次散射模型来代替获取实验数据集，以实现高度概括的深度学习模型。我们设计了一个新的基于模型的数据归一化预处理过程，以提高样本对比度，并实现统一的预测质量，而不管散射强度。为了实现高效的训练和预测，我们实现了一个轻量级的2D网络结构，该结构利用多通道输入对轴向信息进行编码。我们证明了这个框架的实验测量上皮颊细胞和秀丽隐杆线虫蠕虫的能力。我们强调了这种方法的鲁棒性，通过评估动态样本的活蠕虫视频，我们强调我们的方法的普遍性，通过恢复藻类样品评估与不同的实验设置。为了评估预测质量，我们开发了一种新的定量评价指标，并表明我们的预测与我们的实验测量和多重散射物理是一致的。
英文摘要：
Recovering 3D phase features of complex, multiple-scattering biological samples traditionally sacrifices computational efficiency and processing time for physical model accuracy and reconstruction quality. This trade-off hinders the rapid analysis of living, dynamic biological samples that are often of greatest interest to biological research. Here, we overcome this bottleneck by combining annular intensity diffraction tomography (aIDT) with an approximant-guided deep learning framework. Using a novel physics model simulator-based learning strategy trained entirely on natural image datasets, we show our network can robustly reconstruct complex 3D biological samples of arbitrary size and structure. This approach highlights that large-scale multiple-scattering models can be leveraged in place of acquiring experimental datasets for achieving highly generalizable deep learning models. We devise a new model-based data normalization pre-processing procedure for homogenizing the sample contrast and achieving uniform prediction quality regardless of scattering strength. To achieve highly efficient training and prediction, we implement a lightweight 2D network structure that utilizes a multi-channel input for encoding the axial information. We demonstrate this framework's capabilities on experimental measurements of epithelial buccal cells and Caenorhabditis elegans worms. We highlight the robustness of this approach by evaluating dynamic samples on a living worm video, and we emphasize our approach's generalizability by recovering algae samples evaluated with different experimental setups. To assess the prediction quality, we develop a novel quantitative evaluation metric and show that our predictions are consistent with our experimental measurements and multiple-scattering physics.
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