Artificial Intelligence–Driven Single-Shot PET Image Artifact Detection and Disentanglement: Toward Routine Clinical Image Quality Assurance

PURPOSE: Medical imaging artifacts compromise image quality and quantitative analysis and might confound interpretation and misguide clinical decision-making. The present work envisions and demonstrates a new paradigm PET image Quality Assurance NETwork (PET-QA-NET) in which various image artifacts...

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Detalles Bibliográficos
Autores principales: Shiri, Isaac, Salimi, Yazdan, Hervier, Elsa, Pezzoni, Agathe, Sanaat, Amirhossein, Mostafaei, Shayan, Rahmim, Arman, Mainta, Ismini, Zaidi, Habib
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10662584/
https://www.ncbi.nlm.nih.gov/pubmed/37883015
http://dx.doi.org/10.1097/RLU.0000000000004912

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10662584/
https://www.ncbi.nlm.nih.gov/pubmed/37883015
http://dx.doi.org/10.1097/RLU.0000000000004912

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