  Two features desired in a three-dimensional (3D) optical tomographic image
reconstruction algorithm are the ability to reduce imaging artifacts and to do
fast processing of large data volumes. Traditional iterative inversion
algorithms are impractical in this context due to their heavy computational and
memory requirements. We propose and experimentally validate a novel scalable
iterative mini-batch algorithm (SIMBA) for fast and high-quality optical
tomographic imaging. SIMBA enables high-quality imaging by combining two
complementary information sources: the physics of the imaging system
characterized by its forward model and the imaging prior characterized by a
denoising deep neural net. SIMBA easily scales to very large 3D tomographic
datasets by processing only a small subset of measurements at each iteration.
We establish the theoretical fixed-point convergence of SIMBA under
nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on
both simulated and experimentally collected intensity diffraction tomography
(IDT) datasets. Our results show that SIMBA can significantly reduce the
computational burden of 3D image formation without sacrificing the imaging
quality.
