On the Simulation of Ultra-Sparse-View and Ultra-Low-Dose Computed Tomography with Maximum a Posteriori Reconstruction Using a Progressive Flow-Based Deep Generative Model

Ultra-sparse-view computed tomography (CT) algorithms can reduce radiation exposure for patients, but these algorithms lack an explicit cycle consistency loss minimization and an explicit log-likelihood maximization in testing. Here, we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruc...

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Detalles Bibliográficos
Autores principales: Shibata, Hisaichi, Hanaoka, Shouhei, Nomura, Yukihiro, Nakao, Takahiro, Takenaga, Tomomi, Hayashi, Naoto, Abe, Osamu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498355/
https://www.ncbi.nlm.nih.gov/pubmed/36136875
http://dx.doi.org/10.3390/tomography8050179

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9498355/
https://www.ncbi.nlm.nih.gov/pubmed/36136875
http://dx.doi.org/10.3390/tomography8050179

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