DEEP$^2$: Deep Learning Powered De-scattering with Excitation Patterning

TL;DR

DEEP$^2$ leverages deep learning to significantly accelerate in-vivo deep-tissue imaging by reducing the number of patterned excitations needed for effective de-scattering, enabling faster imaging at greater depths.

Contribution

This work introduces DEEP$^2$, a novel deep learning approach that enhances the DEEP method, reducing excitation requirements and increasing imaging throughput in deep-tissue microscopy.

Findings

Achieves nearly tenfold increase in imaging throughput.

Successfully images cortical vasculature in vivo at four scattering lengths depth.

Reduces patterned excitations from hundreds to tens for effective de-scattering.

Abstract

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.