Diagnostic value of a second-generation super-resolution deep learning-based reconstruction combined with a metal artifact reduction algorithm for pelvic CT.
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| Title: | Diagnostic value of a second-generation super-resolution deep learning-based reconstruction combined with a metal artifact reduction algorithm for pelvic CT. |
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| Authors: | Takaishi T; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan., Yasaka K; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan. koyasaka@gmail.com., Miyamoto K; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan., Gotoda K; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan., Sato C; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan., Abe O; Department of Radiology, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan. |
| Source: | Skeletal radiology [Skeletal Radiol] 2026 Apr; Vol. 55 (4), pp. 757-765. Date of Electronic Publication: 2025 Nov 15. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Springer Verlag Country of Publication: Germany NLM ID: 7701953 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-2161 (Electronic) Linking ISSN: 03642348 NLM ISO Abbreviation: Skeletal Radiol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
| ISSN: | 1432-2161 |
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| DOI: | 10.1007/s00256-025-05080-4 |
