Leveraging deep learning-based kernel conversion for more precise airway quantification on CT.
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| Title: | Leveraging deep learning-based kernel conversion for more precise airway quantification on CT. |
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| Authors: | Choe J; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea., Yun J; Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea., Kim MJ; Department of Radiology, AMIST, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea., Oh YJ; Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Korea., Bae S; Coreline Soft, Co., Ltd., Seoul, Korea., Yu D; Coreline Soft, Co., Ltd., Seoul, Korea., Seo JB; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea., Lee SM; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea. asellion@hanmail.net., Lee HY; Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Korea. hoyunlee96@gmail.com.; Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea. hoyunlee96@gmail.com. |
| Source: | European radiology [Eur Radiol] 2025 Nov; Vol. 35 (11), pp. 7185-7198. Date of Electronic Publication: 2025 May 22. |
| Publication Type: | Journal Article; Multicenter Study |
| Journal Info: | Publisher: Springer International Country of Publication: Germany NLM ID: 9114774 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-1084 (Electronic) Linking ISSN: 09387994 NLM ISO Abbreviation: Eur Radiol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1432-1084 |
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| DOI: | 10.1007/s00330-025-11696-w |
