Deep learning reconstruction with single-energy metal artifact reduction in pelvic computed tomography for patients with metal hip prostheses

PURPOSE: The aim of this study was to assess the impact of the deep learning reconstruction (DLR) with single-energy metal artifact reduction (SEMAR) (DLR-S) technique in pelvic helical computed tomography (CT) images for patients with metal hip prostheses and compare it with DLR and hybrid iterativ...

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Detalles Bibliográficos
Autores principales: Hosoi, Reina, Yasaka, Koichiro, Mizuki, Masumi, Yamaguchi, Haruomi, Miyo, Rintaro, Hamada, Akiyoshi, Abe, Osamu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10366278/
https://www.ncbi.nlm.nih.gov/pubmed/36862290
http://dx.doi.org/10.1007/s11604-023-01402-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10366278/
https://www.ncbi.nlm.nih.gov/pubmed/36862290
http://dx.doi.org/10.1007/s11604-023-01402-5

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