# Assessment of learning tomography using Mie theory

## TL;DR

This paper evaluates a nonlinear reconstruction method called Learning Tomography (LT) in optical diffraction tomography, demonstrating its improved accuracy and robustness over linear models like Rytov, especially in complex multiple scattering scenarios.

## Contribution

The paper introduces a simulation-based assessment of Learning Tomography using Mie theory, showing its advantages over traditional linear methods in handling multiple scattering and distortions.

## Key findings

- LT outperforms linear models in accuracy and robustness.
- LT corrects phase unwrapping distortions.
- LT effectively handles multiple scattering in experiments.

## Abstract

In Optical diffraction tomography, the multiply scattered field is a nonlinear function of the refractive index of the object. The Rytov method is a linear approximation of the forward model, and is commonly used to reconstruct images. Recently, we introduced a reconstruction method based on the Beam Propagation Method (BPM) that takes the nonlinearity into account. We refer to this method as Learning Tomography (LT). In this paper, we carry out simulations in order to assess the performance of LT over the linear iterative method. Each algorithm has been rigorously assessed for spherical objects, with synthetic data generated using the Mie theory. By varying the RI contrast and the size of the objects, we show that the LT reconstruction is more accurate and robust than the reconstruction based on the linear model. In addition, we show that LT is able to correct distortion that is evident in Rytov approximation due to limitations in phase unwrapping. More importantly, the capacity of LT in handling multiple scattering problem are demonstrated by simulations of multiple cylinders using the Mie theory and confirmed by experimental results of two spheres.