  Recovering 3D phase features of complex, multiple-scattering biological
samples traditionally sacrifices computational efficiency and processing time
for physical model accuracy and reconstruction quality. This trade-off hinders
the rapid analysis of living, dynamic biological samples that are often of
greatest interest to biological research. Here, we overcome this bottleneck by
combining annular intensity diffraction tomography (aIDT) with an
approximant-guided deep learning framework. Using a novel physics model
simulator-based learning strategy trained entirely on natural image datasets,
we show our network can robustly reconstruct complex 3D biological samples of
arbitrary size and structure. This approach highlights that large-scale
multiple-scattering models can be leveraged in place of acquiring experimental
datasets for achieving highly generalizable deep learning models. We devise a
new model-based data normalization pre-processing procedure for homogenizing
the sample contrast and achieving uniform prediction quality regardless of
scattering strength. To achieve highly efficient training and prediction, we
implement a lightweight 2D network structure that utilizes a multi-channel
input for encoding the axial information. We demonstrate this framework's
capabilities on experimental measurements of epithelial buccal cells and
Caenorhabditis elegans worms. We highlight the robustness of this approach by
evaluating dynamic samples on a living worm video, and we emphasize our
approach's generalizability by recovering algae samples evaluated with
different experimental setups. To assess the prediction quality, we develop a
novel quantitative evaluation metric and show that our predictions are
consistent with our experimental measurements and multiple-scattering physics.
