  Optical diffraction tomography (ODT) produces three dimensional distribution
of refractive index (RI) by measuring scattering fields at various angles.
Although the distribution of RI index is highly informative, due to the missing
cone problem stemming from the limited-angle acquisition of holograms,
reconstructions have very poor resolution along axial direction compared to the
horizontal imaging plane. To solve this issue, here we present a novel
unsupervised deep learning framework, which learns the probability distribution
of missing projection views through optimal transport driven cycleGAN.
Experimental results show that missing cone artifact in ODT can be
significantly resolved by the proposed method.
