Reducing image artifacts in sparse projection CT using conditional generative adversarial networks

Abstract Reducing the amount of projection data in computed tomography (CT), specifically sparse-view CT, can reduce exposure dose; however, image artifacts can occur. We quantitatively evaluated the effects of conditional generative adversarial networks (CGAN) on image quality restoration for spars...

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Bibliografische gegevens

Hoofdauteurs:	Keisuke Usui, Sae Kamiyama, Akihiro Arita, Koichi Ogawa, Hajime Sakamoto, Yasuaki Sakano, Shinsuke Kyogoku, Hiroyuki Daida
Formaat:	Artigo
Taal:	Inglês
Gepubliceerd in:	Nature Portfolio 2024-02-01
Reeks:	Scientific Reports
Onderwerpen:	Sparse projection CGAN Deep learning Artifact reduction CT
Online toegang:	https://doi.org/10.1038/s41598-024-54649-x
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