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.
@article{arxiv.1502.01914,
title = {A Learning Approach to Optical Tomography},
author = {Morteza H. Shoreh and Ulugbek S. Kamilov and Ioannis N. Papadopoulos and Alexandre Goy and Cedric Vonesch and Michael Unser and Demetri Psaltis},
journal= {arXiv preprint arXiv:1502.01914},
year = {2015}
}