Usefulness of a Metal Artifact Reduction Algorithm in Digital Tomosynthesis Using a Combination of Hybrid Generative Adversarial Networks

Usefulness of a Metal Artifact Reduction Algorithm in Digital Tomosynthesis Using a Combination of Hybrid Generative Adversarial Networks

In this study, a novel combination of hybrid generative adversarial networks (GANs) comprising cycle-consistent GAN, pix2pix, and (mask pyramid network) MPN (CGpM-metal artifact reduction [MAR]), was developed using projection data to reduce metal artifacts and the radiation dose during digital tomo...

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Bibliographic Details
Main Authors: Tsutomu Gomi, Rina Sakai, Hidetake Hara, Yusuke Watanabe, Shinya Mizukami
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Diagnostics
Subjects:
tomosynthesis
metal artifact reduction
generative adversarial network
arthroplasty
radiation-dose reduction
Online Access:https://www.mdpi.com/2075-4418/11/9/1629

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