Towards smart optical focusing: Deep learning-empowered wavefront shaping in nonstationary scattering media

TL;DR

This paper introduces RFOTNet, a deep learning framework that enables rapid and efficient optical focusing in nonstationary scattering media, overcoming limitations of traditional wavefront shaping methods in dynamic tissues.

Contribution

The study presents a novel deep convolutional neural network approach for wavefront shaping in nonstationary media, reducing computational time and enabling real-time refocusing in dynamic tissues.

Findings

RFOTNet successfully refocuses light in nonstationary media.

Experimental validation confirms rapid and accurate focusing.

Framework shows potential for deep tissue imaging in biomedical optics.

Abstract

Optical focusing at depths in tissue is the Holy Grail of biomedical optics that may bring revolutionary advancement to the field. Wavefront shaping is a widely accepted approach to solve this problem, but most implementations thus far have only operated with stationary media which, however, are scarcely existent in practice. In this article, we propose to apply a deep convolutional neural network named as ReFocusing-Optical-Transformation-Net (RFOTNet), which is a Multi-input Single-output network, to tackle the grand challenge of light focusing in nonstationary scattering media. As known, deep convolutional neural networks are intrinsically powerful to solve inverse scattering problems without complicated computation. Considering the optical speckles of the medium before and after moderate perturbations are correlated, an optical focus can be rapidly recovered based on fine-tuning of pre-trained neural networks, significantly reducing the time and computational cost in refocusing. The feasibility is validated experimentally in this work. The proposed deep learning-empowered wavefront shaping framework has great potentials in facilitating optimal optical focusing and imaging in deep and dynamic tissue.