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

被引：18

作者：

Chung, Hyungjin ^{[1
]}

Huh, Jaeyoung ^{[1
]}

Kim, Geon ^{[2
]}

Park, Yong Keun ^{[2
]}

Ye, Jong Chul ^{[1
]}

机构：

[1] Korea Adv Inst Sci & Technol KAIST, Dept Bio & Brain Engn, Daejeon 34141, South Korea

[2] Korea Adv Inst Sci & Technol KAIST, Dept Phys, Daejeon 34141, South Korea

来源：

IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING | 2021年 / 7卷

基金：

新加坡国家研究基金会;

关键词：

Optical diffraction tomography; deep learning; unsupervised learning; optimal transport; CycleGAN; OPTICAL DIFFRACTION TOMOGRAPHY; CONVOLUTIONAL NEURAL-NETWORK; PHASE MICROSCOPY; RECONSTRUCTION; SUPERRESOLUTION; CYCLEGAN;

D O I：

10.1109/TCI.2021.3098937

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：747 / 758

页数：12

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