Missing Cone Artifact Removal in ODT Using Unsupervised Deep Learning in the Projection Domain

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Optical diffraction tomography (ODT) produces a three-dimensional distribution of the refractive index (RI) by measuring scattering fields at various angles. Although the distribution of the RI is highly informative, due to the missing cone problem stemming from the limited-angle acquisition of holograms, reconstructions have very poor resolution along the axial direction compared to the horizontal imaging plane. To solve this issue, we present a novel unsupervised deep learning framework that learns the probability distribution of missing projection views through an optimal transport-driven CycleGAN. The experimental results show that missing cone artifacts in ODT data can be significantly resolved by the proposed method.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2021-07
Language
English
Article Type
Article
Citation

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING, v.7, pp.747 - 758

ISSN
2573-0436
DOI
10.1109/TCI.2021.3098937
URI
http://hdl.handle.net/10203/287199
Appears in Collection
PH-Journal Papers(저널논문)AI-Journal Papers(저널논문)
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