  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.
