Motion artefact reduction in coronary CT angiography images with a deep learning method

BACKGROUND: The aim of this study was to investigate the ability of a pixel-to-pixel generative adversarial network (GAN) to remove motion artefacts in coronary CT angiography (CCTA) images. METHODS: Ninety-seven patients who underwent single-cardiac-cycle multiphase CCTA were retrospectively includ...

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Detalles Bibliográficos
Autores principales: Ren, Pengling, He, Yi, Zhu, Yi, Zhang, Tingting, Cao, Jiaxin, Wang, Zhenchang, Yang, Zhenghan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9615181/
https://www.ncbi.nlm.nih.gov/pubmed/36307787
http://dx.doi.org/10.1186/s12880-022-00914-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9615181/
https://www.ncbi.nlm.nih.gov/pubmed/36307787
http://dx.doi.org/10.1186/s12880-022-00914-2

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