Ultra-High-Resolution CT of the Head and Neck with Deep Learning Reconstruction—Assessment of Image Quality and Radiation Exposure and Intraindividual Comparison with Normal-Resolution CT

Objectives: To assess the benefits of ultra-high-resolution CT (UHR-CT) with deep learning–based image reconstruction engine (AiCE) regarding image quality and radiation dose and intraindividually compare it to normal-resolution CT (NR-CT). Methods: Forty consecutive patients with head and neck UHR-...

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Detalles Bibliográficos
Autores principales: Altmann, Sebastian, Abello Mercado, Mario A., Ucar, Felix A., Kronfeld, Andrea, Al-Nawas, Bilal, Mukhopadhyay, Anirban, Booz, Christian, Brockmann, Marc A., Othman, Ahmed E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177822/
https://www.ncbi.nlm.nih.gov/pubmed/37174926
http://dx.doi.org/10.3390/diagnostics13091534

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10177822/
https://www.ncbi.nlm.nih.gov/pubmed/37174926
http://dx.doi.org/10.3390/diagnostics13091534

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