Feasibility of Dedicated Breast Positron Emission Tomography Image Denoising Using a Residual Neural Network

OBJECTIVE(S): This study aimed to create a deep learning (DL)-based denoising model using a residual neural network (Res-Net) trained to reduce noise in ring-type dedicated breast positron emission tomography (dbPET) images acquired in about half the emission time, and to evaluate the feasibility an...

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Detalles Bibliográficos
Autores principales: Itagaki, Koji, Miyake, Kanae K., Tanoue, Minori, Oishi, Tae, Kataoka, Masako, Kawashima, Masahiro, Toi, Masakazu, Nakamoto, Yuji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Mashhad University of Medical Sciences 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10261694/
https://www.ncbi.nlm.nih.gov/pubmed/37324225
http://dx.doi.org/10.22038/AOJNMB.2023.71598.1501

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10261694/
https://www.ncbi.nlm.nih.gov/pubmed/37324225
http://dx.doi.org/10.22038/AOJNMB.2023.71598.1501

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