Learning Tomography Assessed Using Mie Theory

In optical diffraction tomography, the multiply scattered field is a nonlinear function of the refractive index (RI) of the object. The Rytov method relies on a single-scattering propagation model and is commonly used to reconstruct images. Recently, a reconstruction model was introduced based on the beam propagation method that takes multiple scattering into account. We refer to this method as learning tomography (LT). We carry out simulations and experiments in order to assess the performance of LT over the iterative single-scattering propagation method. Each algorithm is rigorously assessed for spherical and cylinderical objects, with synthetic data generated using 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 single-scattering propagation model. In addition, we show that LT is able to correct distortions that are evident in the Rytov-approximation-based reconstructions due to limitations in phase unwrapping. More importantly, the ability of LT to handle multiple scattering is demonstrated by simulations of multiple cylinders using Mie theory and is confirmed by experiment