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.

翻译：使用非线性光学显微镜进行点扫描的多光子显微镜,目前的金本位标准,与光学清理或薄试样所使用的广域成像模型相比,尤其缓慢。我们最近采用了“以刺激模式或低薄标本进行切除”作为点扫描地理比例的宽野替代物。使用模式化多光子喷射,在散射前将组织内部的空间信息编码为DEEED。然而,在典型深度进行脱散时,需要数百种这种有型解析。在这项工作中,我们提供了一种基于深层学习的模型,即DEEP$2$,这个模型可以将仅仅几十种模式的图象除去,而不是几百种。因此,我们用几乎一个数量级的大小来改进DEEEP的吞吐量。我们在多个数字和物理实验中展示了我们的方法,包括活性血管血管血管血管成形成像,将4个散布到活小鼠的深度。