Development of a denoising convolutional neural network-based algorithm for metal artifact reduction in digital tomosynthesis for arthroplasty: A phantom study

The present study aimed to develop a denoising convolutional neural network metal artifact reduction hybrid reconstruction (DnCNN-MARHR) algorithm for decreasing metal objects in digital tomosynthesis (DT) for arthroplasty by using projection data. For metal artifact reduction (MAR), we implemented...

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Detalles Bibliográficos
Autores principales: Gomi, Tsutomu, Sakai, Rina, Hara, Hidetake, Watanabe, Yusuke, Mizukami, Shinya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6743787/
https://www.ncbi.nlm.nih.gov/pubmed/31518374
http://dx.doi.org/10.1371/journal.pone.0222406

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6743787/
https://www.ncbi.nlm.nih.gov/pubmed/31518374
http://dx.doi.org/10.1371/journal.pone.0222406

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