Missing Cone Artifacts Removal in ODT using Unsupervised Deep Learning in Projection Domain
This addresses resolution issues in ODT imaging for biomedical applications, but appears incremental as it adapts existing deep learning techniques to a specific domain problem.
The paper tackled the missing cone problem in optical diffraction tomography (ODT), which causes poor axial resolution, by proposing an unsupervised deep learning framework using optimal transport-driven cycleGAN to learn missing projection views, resulting in significant artifact removal.
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.