A Learning Approach to Optical Tomography

TL;DR

This paper introduces a neural network-based method for 3D optical tomography that reconstructs the refractive index distribution of objects, demonstrated on cellular samples, offering a new approach to imaging in biomedical optics.

Contribution

It presents a novel neural network training approach for 3D optical tomography that directly optimizes voxel refractive indices from measured scattered light.

Findings

Successfully reconstructed 3D refractive index of cells

Demonstrated effectiveness of neural network in optical imaging

Provides a new framework for optical tomography

Abstract

We describe a method for imaging 3D objects in a tomographic configuration implemented by training an artificial neural network to reproduce the complex amplitude of the experimentally measured scattered light. The network is designed such that the voxel values of the refractive index of the 3D object are the variables that are adapted during the training process. We demonstrate the method experimentally by forming images of the 3D refractive index distribution of cells.