论文标题
深度$^2 $：深度学习的动力去冰片和激发图案
DEEP$^2$: Deep Learning Powered De-scattering with Excitation Patterning
论文作者
论文摘要
有限的吞吐量是使用非线性光学显微镜在体内深部组织成像中的关键挑战。当前金标准的点扫描多光子显微镜（尤其是与用于光学清除或薄样本的宽视野成像方式相比，尤其是较慢）。最近，我们引入了“以激发图案或深度的脱落”，作为对点扫描几何形状的广阔场所替代品。使用图案化的多光子激发，深层在散射之前对组织内部的空间信息进行编码。但是，要在典型的深处进行脱落，需要数百种这种图案激发。在这项工作中，我们介绍了一个基于深度学习的模型，它可以从数十张图案激发而不是数百个模型中删除片段图像。因此，我们几乎将深层的吞吐量提高了一个数量级。我们在多个数值和物理实验中演示了我们的方法，包括体内皮质脉管系统，在活着的小鼠中，最大四个散射长度成像。
Limited throughput is a key challenge in in-vivo deep-tissue imaging using nonlinear optical microscopy. Point scanning multiphoton microscopy, the current gold standard, is slow especially compared to the wide-field imaging modalities used for optically cleared or thin specimens. We recently introduced 'De-scattering with Excitation Patterning or DEEP', as a widefield alternative to point-scanning geometries. Using patterned multiphoton excitation, DEEP encodes spatial information inside tissue before scattering. However, to de-scatter at typical depths, hundreds of such patterned excitations are needed. In this work, we present DEEP$^2$, a deep learning based model, that can de-scatter images from just tens of patterned excitations instead of hundreds. Consequently, we improve DEEP's throughput by almost an order of magnitude. We demonstrate our method in multiple numerical and physical experiments including in-vivo cortical vasculature imaging up to four scattering lengths deep, in alive mice.
