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Accuracy of two deep learning-based reconstruction methods compared with an adaptive statistical iterative reconstruction method for solid and ground-glass nodule volumetry on low-dose and ultra-low-dose chest computed tomography: A phantom study.
Kim, Cherry; Kwack, Thomas; Kim, Wooil; Cha, Jaehyung; Yang, Zepa; Yong, Hwan Seok.
Affiliation
  • Kim C; Department of Radiology, Ansan Hospital, Korea University College of Medicine, Ansan-si, Gyeonggi, South Korea.
  • Kwack T; Department of Radiology, Ansan Hospital, Korea University College of Medicine, Ansan-si, Gyeonggi, South Korea.
  • Kim W; Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA, United States of America.
  • Cha J; Medical Science Research Center, Ansan Hospital, Korea University College of Medicine, Ansan-si, Gyeonggi, South Korea.
  • Yang Z; Biomedical Research Center, Guro Hospital, Korea University College of Medicine, Seoul, South Korea.
  • Yong HS; Department of Radiology, Guro Hospital, Korea University College of Medicine, Seoul, South Korea.
PLoS One ; 17(6): e0270122, 2022.
Article in En | MEDLINE | ID: mdl-35737734
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning / Lung Neoplasms Type of study: Diagnostic_studies / Screening_studies Limits: Humans Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2022 Type: Article Affiliation country: South Korea
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning / Lung Neoplasms Type of study: Diagnostic_studies / Screening_studies Limits: Humans Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2022 Type: Article Affiliation country: South Korea